CN109193643A - A kind of method and system calculating distribution system network loss and reliability - Google Patents
A kind of method and system calculating distribution system network loss and reliability Download PDFInfo
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- CN109193643A CN109193643A CN201811196743.5A CN201811196743A CN109193643A CN 109193643 A CN109193643 A CN 109193643A CN 201811196743 A CN201811196743 A CN 201811196743A CN 109193643 A CN109193643 A CN 109193643A
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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
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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
- 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]
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Abstract
The present invention provides a kind of method and system for calculating distribution system network loss and reliability, this method is specifically included based on described with electricity consumption mathematical model, it determines described wait increase the prediction operation data for matching electrical equipment, wherein, it is described wait increase match electrical equipment prediction operation data at any time or load value variation, i.e. prediction operation data is the data obtained according to the service condition for increasing electrical equipment newly, meet the operating status of newly-increased electrical equipment, the data such as the network loss and reliability that are calculated in this way just meet the actual conditions of intelligent distribution system, and then the accuracy of the data such as the network loss being calculated and reliability is higher.
Description
Technical field
The present invention relates to adapted electrical domains, more specifically, being related to a kind of calculating distribution system network loss and reliability
Method and system.
Background technique
Intelligent distribution system and conventional electrical distribution net have biggish difference in terms of operation characteristic, generate the main original of difference
Because being to increase novel with electrical equipment in intelligent distribution system, distributed generation resource, energy storage device, electric car are such as increased
Charge and discharge power station etc..
Novel with after electrical equipment, the network loss and reliability of entire distribution system are increased in intelligent distribution system
Etc. data can change.In the prior art, be intended to increase it is novel with electrical equipment when, can calculate first and increase this and novel match
The data such as network loss and reliability after electrical equipment when calculating the data such as network loss and reliability, can set and be intended to increased novel match
The power output of electrical equipment or electricity consumption are fixed and invariable, but in fact, are intended to increased novel power output or use with electrical equipment
Electricity be it is continually changing with time or load value, as the power output of distributed photovoltaic power be as the time is continually changing,
So that in the prior art intelligent distribution system be intended to increase it is novel with electrical equipment when, be calculated to increase this new
Type matches the network loss and the data inaccuracy such as reliability after electrical equipment.
Summary of the invention
In view of this, the present invention provides a kind of method and system for calculating distribution system network loss and reliability, to solve
In the prior art intelligent distribution system be intended to increase it is novel with electrical equipment when, be calculated to increase this novel with electricity consumption
The problem of the data such as network loss and reliability after equipment inaccuracy.
In order to solve the above technical problems, present invention employs following technical solutions:
A method of calculating distribution system network loss and reliability, comprising:
Construct adapted electric model；It wherein, include having the circuit structure model with electrical equipment in the adapted electric model
With wait increase match electrical equipment circuit structure model；
Obtain have history data with electrical equipment and it is described wait increase match electrical equipment with electricity consumption mathematical modulo
Type；Wherein, the electricity consumption mathematical model of matching is described wait increase the power output mathematical model or electricity consumption mathematical model of matching electrical equipment；
Match electricity consumption mathematical model based on described, determines described wait increase the prediction operation data for matching electrical equipment；Wherein, described
Wait increase match electrical equipment prediction operation data at any time or load value variation；
It described sets according to the adapted electric model, the history data having with electrical equipment and wait increase with electricity consumption
Standby prediction operation data calculates the operating parameter of the adapted electric model；
Wherein, the operating parameter of the adapted electric model includes calculation of tidal current, network loss value and coefficient of reliability.
Preferably, according to the adapted electric model, the history data having with electrical equipment and described wait increase
Prediction operation data with electrical equipment calculates the operating parameter of the adapted electric model, comprising:
Discontinuity surface when determining multiple；
Discontinuity surface corresponding history run having in the history data with electrical equipment when determining each described
Data and the prediction wait increase in the prediction operation data with electrical equipment run subdata；
Based on the adapted electric model, it is each described when the corresponding history run subdata of discontinuity surface and prediction operation son
Data calculate the operating parameter of the adapted electric model.
Be preferably based on the adapted electric model, it is each described when the corresponding history run subdata of discontinuity surface and pre-
Operation subdata is surveyed, the operating parameter of the adapted electric model is calculated, comprising:
Based on the adapted electric model, it is each described when the corresponding history run subdata of discontinuity surface and prediction operation son
Data, discontinuity surface corresponding circuit structure diagram when constructing each described；
According to it is each described when the corresponding circuit structure diagram of discontinuity surface, carry out Load flow calculation, obtain calculation of tidal current；
According to calculation of tidal current, it is each described when the corresponding history run subdata of discontinuity surface and prediction operation subnumber
According to the corresponding subnet damage value of discontinuity surface and reliability subsystem number when being calculated each described；
Based on it is each described when the corresponding subnet damage value of discontinuity surface and reliability subsystem number, calculate the adapted electric model
Network loss value and coefficient of reliability.
The corresponding subnet damage value of discontinuity surface and reliability subsystem number, calculate the adapted when being preferably based on each described
The network loss value and coefficient of reliability of electric model, comprising:
The corresponding subnet damage value of discontinuity surface carries out integral operation when will be each described, obtains the network loss of the adapted electric model
Value；
When will be each described in the corresponding reliability subsystem number of discontinuity surface, the corresponding the smallest reliability subsystem number of numerical value be made
For the coefficient of reliability.
Preferably, it is described based on it is each described when the corresponding subnet damage value of discontinuity surface and reliability subsystem number, calculate institute
After the step of stating the network loss value and coefficient of reliability of adapted electric model, further includes:
The network loss value is compared with the history network loss value of the adapted electric model, obtains the first comparison result；
The coefficient of reliability is compared with the historical reliability coefficient of the adapted electric model, second is obtained and compares
As a result；
According to first comparison result and second comparison result, determines and match the excellent of electrical equipment wait increase described in increasing
Pessimum result.
A kind of system calculating distribution system network loss and reliability, comprising:
Model construction module, for constructing adapted electric model；It wherein, include having to set with electricity consumption in the adapted electric model
Standby circuit structure model and wait increase the circuit structure model for matching electrical equipment；
Data obtaining module has history data with electrical equipment and described matches electricity consumption wait increase for obtaining
Equipment matches electricity consumption mathematical model；Wherein, the electricity consumption mathematical model of matching is described wait increase the power output mathematical modulo for matching electrical equipment
Type or electricity consumption mathematical model；
Data determining module determines described wait increase the prediction for matching electrical equipment for matching electricity consumption mathematical model based on described
Operation data；Wherein, it is described wait increase match electrical equipment prediction operation data at any time or load value variation；
Parameter calculating module has the history data with electrical equipment for the adapted electric model, according to described
With described wait increase the prediction operation data for matching electrical equipment, the operating parameter of the adapted electric model is calculated；
Wherein, the operating parameter of the adapted electric model includes calculation of tidal current, network loss value and coefficient of reliability.
Preferably, the parameter calculating module includes:
Time determines submodule, discontinuity surface when for determining multiple；
Data determine submodule, and discontinuity surface is corresponding when for determining each described has the history run with electrical equipment
History run subdata and the prediction wait increase in the prediction operation data with electrical equipment in data run subdata；
Parameter computation module, discontinuity surface corresponding history run when being used for the adapted electric model, being based on each described
Subdata and prediction operation subdata, calculate the operating parameter of the adapted electric model.
Preferably, the parameter computation module includes:
Structure chart construction unit, for based on the adapted electric model, it is each described when the corresponding history run of discontinuity surface
Subdata and prediction operation subdata, discontinuity surface corresponding circuit structure diagram when constructing each described；
First computing unit, for according to it is each described when the corresponding circuit structure diagram of discontinuity surface, carry out Load flow calculation, obtain
To calculation of tidal current；
Second computing unit, for according to calculation of tidal current, it is each described when the corresponding history run subnumber of discontinuity surface
Accordingly and prediction runs subdata, the corresponding subnet damage value of discontinuity surface and reliability subsystem number when being calculated each described；
Third computing unit, the corresponding subnet damage value of discontinuity surface and reliability subsystem number when for being based on each described, meter
Calculate the network loss value and coefficient of reliability of the adapted electric model.
Preferably, the third computing unit includes:
Computation subunit, for will each described when the corresponding subnet damage value of discontinuity surface carry out integral operation, obtain described in
The network loss value of adapted electric model；
Coefficient determines subelement, for will be each described when the corresponding reliability subsystem number of discontinuity surface in, corresponding numerical value
The smallest reliability subsystem number is as the coefficient of reliability.
Preferably, further includes:
First comparing subunit, the corresponding subnet damage value of discontinuity surface and can when being based on each described for third computing unit
After temper coefficient, the network loss value and coefficient of reliability that calculate the adapted electric model, the network loss value is matched into electricity consumption with described
The history network loss value of model is compared, and obtains the first comparison result；
Second comparing subunit, for by the historical reliability coefficient of the coefficient of reliability and the adapted electric model into
Row compares, and obtains the second comparison result；
As a result subelement is determined, for determining and increasing institute according to first comparison result and second comparison result
It states wait increase the superiority-inferiority result for matching electrical equipment.
Compared to the prior art, the invention has the following advantages:
The present invention provides a kind of method and system for calculating distribution system network loss and reliability, institute is based in the present invention
It states with electricity consumption mathematical model, determines described wait increase the prediction operation data for matching electrical equipment, wherein described to match electrical equipment wait increase
Prediction operation data at any time or load value variation, i.e., prediction operation data be according to increase newly electrical equipment service condition obtain
The data arrived meet the operating status of newly-increased electrical equipment, and the data such as network loss and reliability for being calculated in this way just meet intelligence
The actual conditions of energy distribution system, and then the accuracy of the data such as the network loss being calculated and reliability is higher.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of invention for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is the method flow diagram provided in an embodiment of the present invention for calculating distribution system network loss and reliability；
Fig. 2 is a kind of schematic diagram of wind speed curve provided in an embodiment of the present invention；
Fig. 3 is a kind of characteristic schematic diagram of power of fan provided in an embodiment of the present invention；
Fig. 4 is a kind of electrical block diagram of non-linear universal battery model provided in an embodiment of the present invention；
Fig. 5 is the flow chart of step S4 detailed process in method provided in an embodiment of the present invention；
Fig. 6 is the flow chart of step S43 detailed process in method provided in an embodiment of the present invention；
Fig. 7 is distributed generation resource provided in an embodiment of the present invention and duration of load application variation tendency and up and down adjustment capacity side
The schematic diagram on boundary；
Fig. 8 is the structural schematic diagram of the system of calculating distribution system network loss and reliability provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
As shown in Figure 1, the embodiment of the invention provides a kind of method for calculating distribution system network loss and reliability, it can be with
Include:
S1, building adapted electric model；
It wherein, include having circuit structure model with electrical equipment and wait increase the electricity for matching electrical equipment in adapted electric model
Line structure model.
Specifically, having with electrical equipment may include following three classes:
(1) the distribution network structure model based on connection of network line；
(2) temporal model based on time series fractograph analysis, such as blower, photovoltaic；
Temporal model based on time series fractograph analysis refers to the active or reactive capability curve with electrical equipment
It changes over time.Such as with electrical equipment be distributed generation resource in photovoltaic, the active power output of photovoltaic is related with intensity of illumination, i.e., with
Time is related, photovoltaic it is idle related with output voltage, i.e., it is also related with the time.
(3) by event occur as unit of influence system running state event class model, such as electric car charge and discharge stake, storage
Energy device, friendly load etc..Wherein, friendly load includes interruptible load and schedulable load.
By event occur as unit of influence system running state event class model refer to the power curve with electrical equipment
It is related with part throttle characteristics.It is such as energy storage device (such as flywheel energy storage, super capacitor), the operation side of energy storage device with electrical equipment
Formula have charging, electric discharge and leave unused three kinds of states, energy storage device with whether have load etc. it is related.
Wait increase match electrical equipment include timing class model, such as photovoltaic, blower, be also possible to event class model, such as it is electronic
Automobile charge and discharge power station, energy storage device etc..
It can be one it should be noted that having with electrical equipment and wait increase the quantity with electrical equipment with no restrictions
Or it is multiple.
S2, acquisition have history data with electrical equipment and it is described wait increase match electrical equipment with electricity consumption number
Learn model；
Wherein, described to include power output mathematical model and electricity consumption mathematical modulo with electricity consumption mathematical model with electrical equipment wait increase
Type；It is specially distinguished and is used according to electrical equipment, when increasing with electrical equipment is the timing class models such as photovoltaic or blower, adapted
Electric mathematical model be power output mathematical model, when wait increase match electrical equipment be the events class such as energy storage device or electric car charge and discharge power station
It is electricity consumption mathematical model with electricity consumption mathematical model when model.
It should be noted that including: with electricity consumption mathematical model with electrical equipment wait increase described in building
When matching electrical equipment difference wait increase, building it is different with electricity consumption mathematical model, specifically how under:
One, distributed generation resource
1. photovoltaic
By establishing the temporal model of intensity of solar radiation, photovoltaic power output model is obtained using photoelectric conversion relationship；
(1) intensity of solar radiation model
For being in latitude on groundLongitude is the day of the sun at a time observed for the observer of λ
Vertex angle theta (complementary angle of solar elevation) and azimuth angle alpha can be calculated with following equations:
Wherein, δ is solar declination the angle of the line of centres and equatorial plane (day), is changed between ± 23 ° of 27'.ω
For hour angle, for observation point longitude circle be overlapped with the sun after (i.e. locality high noon) earth rotation angle, daily from 0 ° to 360 °, just
It is 0 ° that the period of the day from 11 a.m. to 1 p.m, which carves hour angle,.
In sunrise and sunset moment, θ is 90 °.From the equations above it can be seen that
Wherein, ω0And α0The respectively solar hour angle and azimuth at sunrise and sunset moment, with placeAnd season
(δ) and it is different.For example, the spring (autumn) for the Northern Hemisphere divides day, δ=0, ω0=± 90 °, α0=90 ° and 270 °, i.e., day and night etc.
Long, the sun rises from positive east, falls from positive west；For the summer solstice, δ=23.5 °,Place, ω0=±
180 °, illustrate that the whole day sun is not fallen in the Arctic Circle.
Corresponded to section [0,2 π] 365 days, taking D for the number of days in 1 year, (January 1, D are equal to 1；December 31, D=
365), then
Wherein, X is date regulation coefficient, for the influence of the revolution of the earth position of sun of some day in label 1 year.
Solar declination δ are as follows:
δ=0.006894-0.399512cos X+0.072075sin X-0.006799cos (2X)
+0.000896sin(2X)-0.002689cos(3X)+0.001516sin(3X) (6)
Solar distance dm are as follows:
dm=1.000109+0.033494cos X+0.001472sin X
+0.000768cos(2X)+0.000079sin(2X) (7)
Since the different moments sun is in different height, the solar irradiance of incident aeropause horizontal plane should
Are as follows:
Wherein, S0Irradiation level is integrated for the sun on aeropause and daylight vertical plane, d is solar distance, with the earth
Revolution variation, d0For mean Earth-Sun distance (1.496 × 108Km is the earth in 21-22 days March and the September 22-23 days day reached
Ground average distance, when perihelion: 1.47 × 108Km, when aphelion: 1.52 × 108Km),Referred to as solar constant, World Meteorological
TissueOptimum value be set to 1367 ± 7W/m2。
Atmospheric transparency P are as follows:
In formula: P2For annual atmospheric transparency.
Intensity of solar radiation Pn after atmosphere are as follows:
Pn=S0Pm (11)
Wherein, m is air quality number.
(2) output of photovoltaic system
PPV=Pn* η * S* ηinv (12)
Wherein, η is the efficiency of photovoltaic array, and S is the illuminating area of photovoltaic array, ηinvFor the efficiency of inverter.Formula 12
Electricity consumption mathematical model can be matched for photovoltaic.
Photovoltaic matches electricity consumption mathematical model, according to research purpose and demand, it can be deduced that the photovoltaic of each time scale goes out
Force curve.
2. blower
The power output temporal characteristics of wind power plant and the regional wind resource of planning have direct relation, the wind speed of Various Seasonal
Diurnal variation also has significantly different.Blower power output is inseparable with wind speed curve, and it is big that the active power output of system depends on wind speed
It is small.Wind speed time series is generated first with Wind speed model, then wind is obtained by corresponding wind speed-wind power functional relation
Electric time series.Based on real-time wind speed change curve, blower temporal model more accurateization of foundation and functionization.
(1) wind speed curve
The spatial and temporal distributions of wind direction and wind speed are extremely complex, show extremely strong randomness, the change curve of wind speed Vwind is such as
Shown in Fig. 2, change no any rule, and frequency is very fast, is difficult accurately to predict wind speed.Domestic and foreign scholars couple
The probability distribution of wind speed has conducted extensive research, and establishes various probability Distribution Models to describe the variation of wind speed.Wherein
Weibull distribution form is simple, preferably with the fitting of actual wind speed statistical distribution, is used widely.The present invention uses the distribution
The probabilistic model of wind speed is established, specific as follows:
Distribution function are as follows:
Probability density function are as follows:
In formula, c and k are respectively the scale parameter and form parameter of Weibull distribution；Scale parameter c reflects the wind power plant
Mean wind speed, V is given wind speed, unit m/s.
(2) blower power output model
The operation characteristic of Wind turbines causes the power output of blower to change with wind speed and change, but the output power and wind of blower
Speed is not simple linear relationship.When actual wind speed is less than the incision wind speed of Wind turbines or is greater than cut-out wind speed, blower
Power output is due to wind energy deficiency and wind speed is excessive that its output power is caused to be zero；When actual wind speed be located at incision, cut-out wind speed it
Between when, the power output of blower is under the limitation of Wind turbines rated power as wind speed constantly changes.
Generally reflect the rule that the power output of blower changes with wind speed in theory using the quadratic function of segmentation, such as Fig. 3 institute
Show.
In formula: Vci, Vr, Vco are respectively incision wind speed, rated wind speed and the cut-out wind speed of Wind turbines；Pr is wind turbine
The rated power of group, constant A, B, C are the characteristics of output power parameter of curve of blower, are calculated by following formula:
Based on the wind speed curve changed over time, it can be deduced that real-time blower power output model.I.e. blower match electricity consumption number
Learn model.The temporal model contributed using blower can emulate to obtain the curve that blower power output changes over time, really reflect wind
The randomness and intermittence of machine.
Two, energy storage device
The charging and recharging model for establishing energy-storage system is as follows:
Charge model:
Discharging model:
In formula: Pbat(t) energy-storage system t hours charge or discharge power, E are indicatedbat(t) energy-storage system t is indicated
The energy of the storage of hour；Pch-maxAnd Pdch-maxRespectively indicate the maximum charge power and maximum discharge power of energy-storage system；
EmaxAnd EminRespectively indicate minimum capacity and the maximum capacity limitation of energy-storage system.
The charging and recharging model of energy storage device is energy storage device with electricity consumption mathematical model.
Modeling method based on multiple space and time scales, combines with distributed energy, may be implemented to energy storage device charge and
The real-time control of discharge power, to meet the service requirement of power distribution network.
Three, electric car
1. battery model
Battery model can describe external characteristics when battery work, and the battery model of use is one and mutually goes here and there with constant resistance
The controllable voltage source of connection, as shown in figure 4, the model is using SoC as state parameter, SoC is related to battery status, is used to refer to electricity
The state-of-charge in pond, also known as remaining capacity, calculation method are as follows:
Wherein, Qr, Qc are respectively battery dump energy and total electricity.
In Fig. 4, E is battery floating voltage；E0For battery voltage rating；K is polarizing voltage；Q is battery capacity；R is electricity
Pond internal resistance；I is battery discharge current；VbattFor battery terminal voltage；A is index amplitude；B is time constant, and U is controllable voltage
Source, R are internal resistance.Battery terminal voltage VbattAnd charge power P can pass through a nonlinear equation relevant to state-of-charge SoC
It obtains:
In formula, i is battery discharge current；T is the time；It can be to different type electric car charge power by the model
And its SOC variation is emulated.
The load 2. electric car charges
Initial SoC: before electric car charging, the remaining capacity of battery and the distance dependent travelled, if electric car is every
The distance of its traveling is d, and the maximum distance that can be travelled is dr, then the SoC before charging can be obtained using formula (22):
Wherein, d can be obtained by the statistical data of traffic department；Dr can utilize battery capacity QbWith every kilometer of electric car
The ratio calculation of energy consumption Ce obtains.
The battery capacity Q of different type electric carbDifference, every kilometer of energy consumption Ce be not also identical.Research has shown that often
The battery capacity Q of seed type electric carbNormal Distribution in a certain range, every kilometer of energy consumption Ce is in a certain range
Dispersed distribution, probability density function such as formula (23), shown in (24):
Wherein, μ is average value, and σ is standard deviation, x1And x2For QbBound, x is battery capacity；A and b is the model of Ce
It encloses.
Charging moment: electric car starts to charge the trip habit and ride characteristic for depending primarily on car owner constantly, simultaneously
The influence of various uncertain factors is also suffered from, therefore, start to charge has randomness constantly.When electric car starts to charge
Carving directly is influenced by the last time trip return moment, according to US Department of Transportation in 2001 to the investigation statistics knot of the whole America vehicle
Fruit shows, starts to charge and meets following distribution constantly, probability density function, that is, matches electricity consumption mathematical model such as formula (25) institute
Show:
Wherein, μs=17.6h, σs=3.4h.
Charging and recharging model under 3.V2G control
Uniformly divide assuming that the beginning charge and discharge moment of schedulable electric car meets within the charge and discharge period in one day
Cloth starts to charge the probability density function f at momentc(x) with start discharging time probability density function fD(x) it is respectively as follows:
The probability density function of schedulable electric car daily travel meets normal distribution:
Wherein μM=16.58, σM=6.57.
The charge-discharge electric power characteristic of electric car realizes numerical simulation by Monte Carlo arbitrary sampling method.
Based on above to the time series modeling of electric car, available its discharges for 24 hours and the curve of charge power, will be electronic
Automobile is incorporated in power distribution network, carries out the voltage analysis and line losses management of timing, to study the grid-connected shadow to reliability of electric car
It rings and its ability is received to provide model basis.
S3, match electricity consumption mathematical model based on described, determine described wait increase the prediction operation data for matching electrical equipment；It is described to
Increase the prediction operation data with electrical equipment at any time or load value changes.
Specifically, with electricity consumption mathematical model give power generating value at any time, power generating value with load value, electricity consumption at any time
Situation of change.Based on the prediction operation data that can predict a period of time with electricity consumption mathematical model.
It is power output model with electricity consumption mathematical model, wait increase with electrical equipment such as when increasing with electrical equipment is photovoltaic
Prediction operation data changes over time, the photovoltaic energy that prediction operation data can generate for different time.
S4, basis match electricity consumption mathematical model, have the history data with electrical equipment and match electrical equipment wait increase
It predicts operation data, calculates the operating parameter of the adapted electric model.As shown in figure 5, specifically includes the following steps:
Step S4 may include:
S41, discontinuity surface when determining multiple；
Wherein, using certain a period of time T as research object, by being split to time series, wherein according to identical
Time interval extract m continuous time section Ti (i=0,1 ..., m-1), discontinuity surface at m is studied respectively.It determines
When discontinuity surface can be year, month, day, hour, minute etc..Preferably, can using hour as when discontinuity surface, can divide within 1 year
Discontinuity surface when being 8760.
It should be noted that the when discontinuity surface determined is the when discontinuity surface based on adapted electric model to determine.Match
With must include in electric model wait increase match electrical equipment circuit structure model.
S42, discontinuity surface corresponding history run having in the history data with electrical equipment when determining each
Data and the prediction wait increase in the prediction operation data with electrical equipment run subdata；
Specifically, when discontinuity surface determine after, it is necessary to determine analysis it is each when discontinuity surface used in data.To have and match
In the history data of electrical equipment with when the corresponding historical time section of discontinuity surface data as history run subdata.
As an example it is assumed that when discontinuity surface be on June 20th, 2018 this day in 12 points, if obtain history run number
According to the data between on January 1, -2018 years on the 1st January in 2017, then by 12 points of data on June 20th, 2018 this day
As when discontinuity surface be on June 20th, 2018 this day in 12 points of history run subdata.
The data wait increase the future that the prediction operation data with electrical equipment is prediction, such as January 1 in 2018 of prediction
Operation data between day on January 1st, 1, it is assumed that when discontinuity surface be 12 points on June 20th, 2018 this day, then will
Wait increase 12 points of data in 20 day June in 2018 this day in the prediction operation data for matching electrical equipment as prediction operation
Subdata.
It should be noted that be herein using a certain data in history data as when discontinuity surface it is corresponding have match
The data of electrical equipment, can also use have the electric model of the adapted with electrical equipment come the data of predicted time section as when
Discontinuity surface is corresponding to have the data with electrical equipment.
S43, based on electricity consumption mathematical model, it is each when the corresponding history run subdata of discontinuity surface and prediction operation son
Data calculate the operating parameter of the adapted electric model.Wherein, the operating parameter of the adapted electric model includes Load flow calculation knot
Fruit, network loss value and coefficient of reliability.
As shown in fig. 6, step S43 specifically includes the following steps:
S431, based on electricity consumption mathematical model, it is each when the corresponding history run subdata of discontinuity surface and prediction operation
Subdata, discontinuity surface corresponding circuit structure diagram when constructing each；
It, can be with specifically, after the corresponding history run subdata of discontinuity surface and prediction operation subdata are known when each
It determines have the circuit structure with electrical equipment according to history run subdata, and is determined according to prediction operation subdata
Wait increase the circuit structure for matching electrical equipment, wherein circuit structure can be to be made of electronic components such as resistance, inductance and reactance.
Has the circuit structure with electrical equipment and after increasing the circuit structure with electrical equipment and determining, so that it may according to
Have with electrical equipment and wait increase the connection structure for matching electrical equipment, discontinuity surface corresponding circuit structure diagram when determining.
S432, according to it is each when the corresponding circuit structure diagram of discontinuity surface, carry out Load flow calculation, obtain calculation of tidal current；
Load flow calculation refers under given power system network topology, component parameters and power generation, load parameter conditions, calculates
The distribution of active power, reactive power and voltage in power network；
Specifically, the detailed process of Load flow calculation may include:
In intelligent distribution system, the introducing meeting of distributed generation resource is so that PV, PI type node increase, for preceding pushing back
Dai Fa, processing PV node type load is difficult, need to handle PV node, solves the algorithm of intelligent distribution system trend,
Reach the convergent purpose of trend.
The DG (distributed power generation unit) of PV node type can be regarded as voltage-controlled current source first.In order to keep PV
The voltage magnitude of node type DG is constant, it is thus necessary to determine that suitable reactive power and reactive current injection, therefore problem is converted into
Idle Injection Current is found to the DG node of each PV type, keeps the voltage magnitude of each node equal with rated value, DG is same
Forward-order current and positive sequence voltage are only existed when step Generator Symmetric operation.The present embodiment is by calculating PV node voltage positive-sequence component
The difference of amplitude and specified amplitude finds out the forward-order current amplitude of injection PV node, reactive compensation is carried out to PV node, in this way by DG
PQ node moving model is converted to by PV node moving model.Specific step is as follows:
It sets the total active-power P of the initial three-phase of DG and holds voltage positive-sequence component amplitude Usc as certain value, initial reactive power
Q is zero, by above-mentioned power flow algorithm, PV node end voltage positive-sequence component amplitude and rated voltage amplitude is calculated after convergence, judges it
Whether difference is within the scope of allowable error.If difference in magnitude, within the scope of allowable error, the voltage of PV node converges on initially
Setting value；If difference in magnitude is more than the error range allowed, PV node is compensated by injecting reactive current, ties up voltage
It holds within the allowable range, idle injection forward-order current is calculated as follows:
ZvΔIq=Δ Uv (29)
In formula: Δ IqFor idle injection forward-order current vector；ZvFor positive sequence sensitivity impedance matrix, dimension is saved equal to PV
Points, diagonal entry be each PV node to branch between root node all positive sequence impedances and, off diagonal element be PV node i
And PV node j to branch identical between root node all positive sequence impedances and；ΔUvFor PV node positive sequence voltage and voltage rating width
Value difference vector.
Each mutually idle Injection Current is added in the initial Injection Current of i-th of node, then re-starts Load flow calculation, is examined
Look into new voltage amplitude value difference.If the idle injecting power of PV node DG exceeds fixed ceiling in iterative calculation, to guarantee power supply
The safe operation of equipment, should limit idle injecting power is specified minimum value or maximum value.
In conclusion the forward-backward sweep method based on current compensation has been used to calculate as the trend for solving intelligent distribution system
Method has handled the weakness of forward-backward sweep method processing PV node difficulty, has reached the convergent purpose of trend.
For the new business of other PQ nodes, tidal current analysis thinking is as follows:
The smart grids new business such as distributed generation resource, energy storage device, electric car charge and discharge power station and controllable burden
Maximum feature is can to receive the order of system traffic control to a certain extent, participates in the process of system requirements side response,
Under the premise of ensureing user power utilization comfort level as far as possible, the active and idle optimizing regulation of load is realized.Load is active and idle
Between mathematical relationship can be determined by power factor, connect from the own physical attribute and its power electronics of different electrical equipments
Mouth controller is related.Due to Load flow calculation reaction be one when discontinuity surface system performance, can by energy storage device and
The power output synthesis of distributed generation resource is considered as a power output source, i.e., one special PQ node.However unlike traditional load,
The PQ node has the ability of certain upper capacitance-adjustable and lower capacitance-adjustable, and all there is stronger coupled relation with time, space,
It is suitable for the application of timing Load flow calculation, as shown in Figure 7.
By such adjustable PQ model, have the advantages that
The convergence of Load flow calculation may be implemented: i.e. when trend is larger is unable to reach convergence, by up and down adjustment capacity
The numerical value of optimization P, Q, reach the convergent purpose of trend in range；
Given this Annual distribution characteristic of type load is very suitable to combine with timing trend and Probabilistic Load Flow, reaches and comment
Estimate the purpose of electric network security.
S433, according to calculation of tidal current, it is each when the corresponding history run subdata of discontinuity surface and prediction operation son
Data, the corresponding subnet damage value of discontinuity surface and reliability subsystem number when being calculated each；
Specifically, discontinuity surface when being directed to each, is calculated a sub- network loss value and reliability subsystem number.
Assuming that including photovoltaic, electric car charge and discharge power station and power distribution network in adapted electric model, electric car charge and discharge power station is
Match electrical equipment wait increase.
Then the history run subdata of photovoltaic can be power curve, and the prediction in electric car charge and discharge power station runs subdata
It can be load data, such as charging times, charging time data.The history run subdata of power distribution network can be voltage etc.
Grade has the data such as transless.
Node equivalent power method can be used by calculating subnet damage value, specific as follows:
In conjunction with the theory of timing trend, based on timing power flow algorithm, match for the intelligence for determining grid structure
Electricity system considers randomness, the intermittence of distributed generation resource power output, for different time section, carries out power distribution network operation tide
Stream calculation.To it is each when discontinuity surface under carry out line losses management, discontinuity surface summarizes to obtain the distribution of system sequence network loss when traversing each.Later
Network loss when by each under discontinuity surface is summarized, and the network loss result for belonging to the time interval is obtained.In the base of timing trend
On plinth, the shortcomings that node equivalent power method can make up its data poor synchronization, the line losses management suitable for intelligent distribution network.
Monte Carlo Analogue Method and Failure Mode Effective Analysis method can be used by calculating reliability subsystem number.It is specific as follows:
Analysis method of the Monte Carlo Analogue Method as intelligent distribution system state selection.Monte Carlo Analogue Method refers to
The state of element is sampled by the random number that computer generates, and then combines and obtains the state of whole system.In novel
The intelligent distribution system of service access, carrying out reliability assessment using Monte Carlo Analogue Method has a many advantages: first, it covers
The enchancement factors such as special Carlow simulation is easy simulation load random fluctuation, element random fault, weather change at random and system
Correct control strategy, calculated result more closing to reality.Second, under the requirement for meeting certain calculation accuracy, Monte Carlo mould
The frequency in sampling of quasi- method is unrelated with the scale of system, is therefore particularly suitable for the reliability assessment of NEW TYPE OF COMPOSITE complication system.The
Three, in addition to can be other than the index of computational representation system average behavior, Monte Carlo Analogue Method can also obtain the general of reliability index
Rate distribution, assessment result are more comprehensive.4th, the simulation process of Monte Carlo method is very simple and intuitive, is easy to by engineering skill
Art personnel understand and grasp.In conclusion this project is using Monte Carlo Analogue Method as intelligent distribution system state selection
Analysis method.
For the intelligent distribution system of new business access, carrying out system mode using Failure Mode Effective Analysis method can
There are a many advantages by property assessment: first, component type and load point all compare more, different elements failure influence in distribution system
It may be different；Even identity element failure, the load point of different location is also had opposite impacts on, it is necessary to failure shadow
It rings and carries out detailed analysis；Second, generally only consider that single order failure is influenced for Radial network, failure mode effect table may be only
It is the incremental value of a simple accident analysis matrix or a certain failure to reliability index, if using some fast search skills
Art is analyzed, such as Fault traversal, fault pervasion etc., not will increase too many calculation amount；Third, FMEA method are some other
It all include the process of impact analysis in the methods of basis of fault analytical method, such as Minimal Cut Set, reliability block diagram.To sum up institute
It states, analysis method of this project using Failure Mode Effective Analysis method as intelligent distribution system status assessment.
S434, based on it is each described when the corresponding subnet damage value of discontinuity surface and reliability subsystem number, calculate and described match electricity consumption
The network loss value and coefficient of reliability of model；
Optionally, on the basis of the present embodiment, step S434 may include:
1) the corresponding subnet damage value of discontinuity surface carries out integral operation when will be each described, obtains the net of the adapted electric model
Damage value；
Specifically, the subnet damage value of different time section may be constructed a strip network loss value change curve, according to the curve
Integral calculation is carried out, total network loss value, as the network loss value of adapted electric model are obtained.
2) when will be each described in the corresponding reliability subsystem number of discontinuity surface, the corresponding the smallest reliability subsystem number of numerical value
As the coefficient of reliability.
Specifically, choosing the smallest reliability subsystem after the corresponding reliability subsystem number of discontinuity surface when obtaining each described
Coefficient of reliability of the number as adapted electric model.
It should be noted that a kind of method for calculating network loss value and coefficient of reliability is only gived in the present embodiment, in addition,
Network loss value and coefficient of reliability can also be calculated using remaining method.
Optionally, further, after executing step S434, can also include:
1) the network loss value is compared with the history network loss value of the adapted electric model, obtains the first comparison result；
2) coefficient of reliability is compared with the historical reliability coefficient of the adapted electric model, obtains the second ratio
Relatively result；
3) it according to first comparison result and second comparison result, determines and matches electrical equipment wait increase described in increasing
Superiority-inferiority result.
Wherein, history network loss value and historical reliability coefficient be based on do not add wait increase match electrical equipment adapted electric model
History data be calculated.
If network loss value is greater than history network loss value, illustrates to be added after increasing and matching electrical equipment, will increase network loss；If network loss value is small
In history network loss value, illustrates to be added after increasing and matching electrical equipment, network loss can be reduced.
If coefficient of reliability is greater than historical reliability coefficient, illustrate to be added after increasing and matching electrical equipment, reliability enhancing；If
Coefficient of reliability is less than historical reliability coefficient, illustrates to be added after increasing and matching electrical equipment, reliability reduces.
The first comparison result and coefficient of reliability and historical reliability coefficient based on network loss value and history network loss value
Second comparison result is added wait increase the pros and cons for matching electrical equipment to determine, and then determines the need for being added and set wait increase with electricity consumption
It is standby.
It should be noted that whether be added wait increase match electrical equipment can also according to be added wait increase match electrical equipment after, when
It the electricity consumption situation of ground user and is added and matches electrical equipment wait increase the influence degree of local economy benefit is determined.
A kind of method for calculating network loss value and coefficient of reliability is given in the present embodiment, and then can be according to this implementation
Method in example calculates network loss value and coefficient of reliability, and then analyses whether to need to be added and should match electrical equipment wait increase.
In the present embodiment, can when determining after discontinuity surface, according to it is each when the corresponding data of discontinuity surface be calculated and match
With the operating parameter of electric model, whether the operating status to determine adapted electric model is normal.
In the present embodiment, electricity consumption mathematical model is matched based on described in, is determined described wait increase the prediction operation number for matching electrical equipment
According to, wherein it is described wait increase match electrical equipment prediction operation data at any time or load value variation, i.e., prediction operation data be root
According to the data that the service condition of newly-increased electrical equipment obtains, meets the operating status of newly-increased electrical equipment, be calculated in this way
The data such as network loss and reliability just meet the actual conditions of intelligent distribution system, and then the network loss being calculated and reliability etc.
The accuracy of data is higher.
Optionally, it corresponds to the above method, a kind of calculating distribution system is provided in another embodiment of the present invention
The system of network loss and reliability, as shown in figure 8, may include:
Model construction module 101, for constructing adapted electric model；It wherein, include existing adapted in the adapted electric model
The circuit structure model of electric equipment and wait increase match electrical equipment circuit structure model；
Data obtaining module 102 has history data and the adapted to be increased with electrical equipment for obtaining
Electric equipment matches electricity consumption mathematical model；Wherein, the electricity consumption mathematical model of matching is described wait increase the power output mathematics for matching electrical equipment
Model or electricity consumption mathematical model；
Data determining module 103, it is described with electricity consumption mathematical model for being based on, it determines described pre- with electrical equipment wait increase
Survey operation data；Wherein, it is described wait increase match electrical equipment prediction operation data at any time or load value variation；
Parameter calculating module 104, for according to the adapted electric model, the history run number having with electrical equipment
, wait increase the prediction operation data for matching electrical equipment, the operating parameter of the adapted electric model is calculated, wherein described to match according to described
It include calculation of tidal current, network loss value and coefficient of reliability with the operating parameter of electric model.
In the present embodiment, electricity consumption mathematical model is matched based on described in, is determined described wait increase the prediction operation number for matching electrical equipment
According to, wherein it is described wait increase match electrical equipment prediction operation data at any time or load value variation, i.e., prediction operation data be root
According to the data that the service condition of newly-increased electrical equipment obtains, meets the operating status of newly-increased electrical equipment, be calculated in this way
The data such as network loss and reliability just meet the actual conditions of intelligent distribution system, and then the network loss being calculated and reliability etc.
The accuracy of data is higher.
It should be noted that the course of work of the modules in the present embodiment, please refers to corresponding in above-described embodiment
Illustrate, details are not described herein.
Optionally, on the basis of the embodiment of above-mentioned parameter computing device, the parameter calculating module includes:
Time determines submodule, discontinuity surface when for determining multiple；
Data determine submodule, and discontinuity surface is corresponding when for determining each described has the history run with electrical equipment
History run subdata and the prediction wait increase in the prediction operation data with electrical equipment in data run subdata；
Parameter computation module, for based on the adapted electric model, it is each described when the corresponding history run of discontinuity surface
Subdata and prediction operation subdata, calculate the operating parameter of the adapted electric model.
In the present embodiment, can when determining after discontinuity surface, according to it is each when the corresponding data of discontinuity surface be calculated and match
With the operating parameter of electric model, whether the operating status to determine adapted electric model is normal.
It should be noted that the course of work of modules and submodule in the present embodiment, please refers to above-described embodiment
In respective description, details are not described herein.
Optionally, on the basis of a upper embodiment, the parameter computation module further include:
Structure chart construction unit, for based on the adapted electric model, it is each described when the corresponding history run of discontinuity surface
Subdata and prediction operation subdata, discontinuity surface corresponding circuit structure diagram when constructing each described；
First computing unit, for according to it is each described when the corresponding circuit structure diagram of discontinuity surface, carry out Load flow calculation, obtain
To calculation of tidal current；
Second computing unit, for according to calculation of tidal current, it is each described when the corresponding history run subnumber of discontinuity surface
Accordingly and prediction runs subdata, the corresponding subnet damage value of discontinuity surface and reliability subsystem number when being calculated each described；
Third computing unit, the corresponding subnet damage value of discontinuity surface and reliability subsystem number when for being based on each described, meter
Calculate the network loss value and coefficient of reliability of the adapted electric model；
Further, the third computing unit includes:
Computation subunit, for will each described when the corresponding subnet damage value of discontinuity surface carry out integral operation, obtain described in
The network loss value of adapted electric model；
Coefficient determines subelement, for will be each described when the corresponding reliability subsystem number of discontinuity surface in, corresponding numerical value
The smallest reliability subsystem number is as the coefficient of reliability.
Further, the third computing unit further include:
First comparing subunit, the corresponding subnet damage value of discontinuity surface and can when being based on each described for third computing unit
After temper coefficient, the network loss value and coefficient of reliability that calculate the adapted electric model, the network loss value is matched into electricity consumption with described
The history network loss value of model is compared, and obtains the first comparison result；
Second comparing subunit, for by the historical reliability coefficient of the coefficient of reliability and the adapted electric model into
Row compares, and obtains the second comparison result；
As a result subelement is determined, for determining and increasing institute according to first comparison result and second comparison result
It states wait increase the superiority-inferiority result for matching electrical equipment.
A kind of method for calculating network loss value and coefficient of reliability is given in the present embodiment, and then can be according to this implementation
Method in example calculates network loss value and coefficient of reliability, and then analyses whether to need to be added and should match electrical equipment wait increase.
It should be noted that the course of work of modules, submodule, unit and subelement in the present embodiment, please join
According to the respective description in above-described embodiment, details are not described herein.
Optionally, on the basis of the embodiment of above-mentioned calculating network loss value and the method and system of coefficient of reliability, this hair
Bright another embodiment provides a kind of electronic equipment for calculating distribution system network loss and reliability characterized by comprising
Memory and processor；
Wherein, the memory is for storing program；
Processor caller is simultaneously used for:
Construct adapted electric model；It wherein, include having the circuit structure model with electrical equipment in the adapted electric model
With wait increase match electrical equipment circuit structure model；
Obtain have history data with electrical equipment and it is described wait increase match electrical equipment with electricity consumption mathematical modulo
Type；Wherein, the electricity consumption mathematical model of matching is described wait increase the power output mathematical model or electricity consumption mathematical model of matching electrical equipment；
Match electricity consumption mathematical model based on described, determines described wait increase the prediction operation data for matching electrical equipment；Wherein, described
Wait increase match electrical equipment prediction operation data at any time or load value variation；
It described sets according to the adapted electric model, the history data having with electrical equipment and wait increase with electricity consumption
Standby prediction operation data calculates the operating parameter of the adapted electric model.
In the present embodiment, electricity consumption mathematical model is matched based on described in, is determined described wait increase the prediction operation number for matching electrical equipment
According to, wherein it is described wait increase match electrical equipment prediction operation data at any time or load value variation, i.e., prediction operation data be root
According to the data that the service condition of newly-increased electrical equipment obtains, meets the operating status of newly-increased electrical equipment, be calculated in this way
The data such as network loss and reliability just meet the actual conditions of intelligent distribution system, and then the network loss being calculated and reliability etc.
The accuracy of data is higher.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (10)
1. a kind of method for calculating distribution system network loss and reliability characterized by comprising
A1, building adapted electric model；Wherein, include in the adapted electric model have circuit structure model with electrical equipment and
Wait increase the circuit structure model for matching electrical equipment；
A2, acquisition have history data with electrical equipment and it is described wait increase match electrical equipment with electricity consumption mathematical modulo
Type；Wherein, the electricity consumption mathematical model of matching is described wait increase the power output mathematical model or electricity consumption mathematical model of matching electrical equipment；
A3, match electricity consumption mathematical model based on described, determine described wait increase the prediction operation data for matching electrical equipment；Wherein, described
Wait increase match electrical equipment prediction operation data at any time or load value variation；
A4, it described sets according to the adapted electric model, the history data having with electrical equipment and wait increase with electricity consumption
Standby prediction operation data calculates the operating parameter of the adapted electric model；
Wherein, the operating parameter of the adapted electric model includes calculation of tidal current, network loss value and coefficient of reliability.
2. the method according to claim 1, wherein the step A4 includes:
A41, discontinuity surface when determining multiple；
A42, discontinuity surface corresponding history run having in the history data with electrical equipment when determining each described
Data and the prediction wait increase in the prediction operation data with electrical equipment run subdata；
A43, based on the adapted electric model, it is each described when the corresponding history run subdata of discontinuity surface and prediction operation son
Data calculate the operating parameter of the adapted electric model.
3. according to the method described in claim 2, it is characterized in that, the step A43 includes:
A431, based on the adapted electric model, it is each described when the corresponding history run subdata of discontinuity surface and prediction operation
Subdata, discontinuity surface corresponding circuit structure diagram when constructing each described；
A432, according to it is each described when the corresponding circuit structure diagram of discontinuity surface, carry out Load flow calculation, obtain calculation of tidal current；
A433, according to calculation of tidal current, it is each described when the corresponding history run subdata of discontinuity surface and prediction operation son
Data, the corresponding subnet damage value of discontinuity surface and reliability subsystem number when being calculated each described；
A434, based on it is each described when the corresponding subnet damage value of discontinuity surface and reliability subsystem number, calculate the adapted electric model
Network loss value and coefficient of reliability.
4. according to the method described in claim 3, it is characterized in that, the step A434 includes:
The corresponding subnet damage value of discontinuity surface carries out integral operation when will be each described, obtains the network loss value of the adapted electric model；
When will be each described in the corresponding reliability subsystem number of discontinuity surface, the corresponding the smallest reliability subsystem number of numerical value be as institute
State coefficient of reliability.
5. according to the method described in claim 3, it is characterized in that, the step A434 further include:
The network loss value is compared with the history network loss value of the adapted electric model, obtains the first comparison result；
The coefficient of reliability is compared with the historical reliability coefficient of the adapted electric model, second is obtained and compares knot
Fruit；
According to first comparison result and second comparison result, determine described in increasing wait increase the superiority-inferiority for matching electrical equipment
As a result.
6. a kind of system for calculating distribution system network loss and reliability characterized by comprising
Model construction module, for constructing adapted electric model；It wherein, include having with electrical equipment in the adapted electric model
Circuit structure model and wait increase match electrical equipment circuit structure model；
Data obtaining module has history data with electrical equipment and described matches electrical equipment wait increase for obtaining
Match electricity consumption mathematical model；Wherein, it is described with electricity consumption mathematical model be it is described wait increase match electrical equipment power output mathematical model or
Electricity consumption mathematical model；
Data determining module determines described wait increase the prediction operation for matching electrical equipment for matching electricity consumption mathematical model based on described in
Data；Wherein, it is described wait increase match electrical equipment prediction operation data at any time or load value variation；
Parameter calculating module, for the adapted electric model, according to the history data having with electrical equipment and institute
It states wait increase the prediction operation data for matching electrical equipment, calculates the operating parameter of the adapted electric model.
Wherein, the operating parameter of the adapted electric model includes calculation of tidal current, network loss value and coefficient of reliability.
7. system according to claim 6, which is characterized in that the parameter calculating module includes:
Time determines submodule, discontinuity surface when for determining multiple；
Data determine submodule, and discontinuity surface is corresponding when for determining each described has the history data with electrical equipment
In history run subdata and it is described wait increase match electrical equipment prediction operation data in prediction operation subdata；
Parameter computation module, discontinuity surface corresponding history run subnumber when being used for the adapted electric model, being based on each described
Accordingly and prediction runs subdata, calculates the operating parameter of the adapted electric model.
8. system according to claim 7, which is characterized in that the parameter computation module includes:
Structure chart construction unit, for based on the adapted electric model, it is each described when the corresponding history run subnumber of discontinuity surface
Accordingly and prediction runs subdata, discontinuity surface corresponding circuit structure diagram when constructing each described；
First computing unit, for according to it is each described when the corresponding circuit structure diagram of discontinuity surface, carry out Load flow calculation, obtain tide
Stream calculation result；
Second computing unit, for according to calculation of tidal current, it is each described when the corresponding history run subdata of discontinuity surface with
And prediction operation subdata, the corresponding subnet damage value of discontinuity surface and reliability subsystem number when being calculated each described；
Third computing unit, the corresponding subnet damage value of discontinuity surface and reliability subsystem number when for being based on each described, calculates institute
State the network loss value and coefficient of reliability of adapted electric model.
9. system according to claim 8, which is characterized in that the third computing unit includes:
Computation subunit, for will each described when discontinuity surface corresponding subnet damage value progress integral operation, obtain the adapted
The network loss value of electric model；
Coefficient determines subelement, for will each described when the corresponding reliability subsystem number of discontinuity surface in, corresponding numerical value minimum
Reliability subsystem number as the coefficient of reliability.
10. system according to claim 8, which is characterized in that the third computing unit further include:
First comparing subunit, the corresponding subnet damage value of discontinuity surface and reliability when being based on each described for third computing unit
Subsystem number, after the network loss value and coefficient of reliability that calculate the adapted electric model, by the network loss value and the adapted electric model
History network loss value be compared, obtain the first comparison result；
Second comparing subunit, for comparing the coefficient of reliability and the historical reliability coefficient of the adapted electric model
Compared with obtaining the second comparison result；
As a result determine subelement, for according to first comparison result and second comparison result, determine increase it is described to
Increase the superiority-inferiority result for matching electrical equipment.
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