CN106786524A - Load model parameters discrimination method based on noise-like signal and improved differential evolution - Google Patents
Load model parameters discrimination method based on noise-like signal and improved differential evolution Download PDFInfo
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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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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
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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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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The present invention relates to a kind of load model parameters discrimination method based on noise-like signal and improved differential evolution, belong to the research field of power system load modeling.The method voltage magnitude, voltage phase angle, active power, the measurement curve of reactive power are obtained first from emulation or actual measurement and verify active power curves fluctuation amplitude whether meet identification require；If meeting identification to require, after selecting load model structure, minimum optimization is carried out to the sum of square of deviations between active power and the predicted value and actual measured value of reactive power using improved differential evolution algorithm, obtain load model parameters；Finally measure curve using another section beyond identification curve to verify the validity of identification result, by verification, then identification is completed.Tracking to load model parameters time variation can be realized using the inventive method, gained identification result can be used for the simulation analysis of power system stability and control, there is directive significance to Power System Planning, operation.
Description
Technical field
The invention belongs to power system load model parameter identification field, one kind is related generally to based on noise-like signal and is changed
Enter the load model parameters discrimination method of differential evolution.
Background technology
The load model structure and parameter of power system has material impact to electric system simulation result.It is irrational negative
Lotus model structure and parameter can produce guard or optimism result, for Power System Stability Analysis, or even can produce
Complete opposite conclusion, therefore load modeling is an importance of Power System Analysis.
The selection of load model structure and the identification of model parameter are two importances of load modeling.In recent years, often
A kind of integrated load model characterizes static load characteristic by constant-impedance, constant current and invariable power model, by induction conductivity
Model characterizes dynamic load characteristic.If invariable power and constant current composition are less in static load, it is also possible to further ignore, only use
Constant-impedance model characterizes static load characteristic.
After selected model structure, to obtain load model parameters, depending on the Measurement-based approach of actual metric data turns into
The focus of current research.In research before this, Measurement-based approach is mainly based upon noisy data realization.But based on disturbance number
According to discrimination method shortcoming be not often occur that short circuit, broken string, sustainable growth of load etc. are obvious to be disturbed in system, pole
May not have substantially to disturb in some time in the case of end.Therefore, the discrimination method based on noisy data is a kind of dependence
The method that whether there is is disturbed in system, if the presence not disturbed, the discrimination method based on noisy data cannot just be held
OK.However, load model has randomness and time variation in itself.Same place load model parameters not in the same time are that have very
Big difference.If being based entirely on noisy data to be recognized, then cannot perfect tracking load model parameters with the time
Change and random fluctuation, identification parameters obtained also just can only correspond to disturbance occur when etching system part throttle characteristics, can not
Enough part throttle characteristics to represent other moment, in some instances it may even be possible to can there is very big difference with the part throttle characteristics at other moment.
The content of the invention
The purpose of the present invention is that, to overcome the weak point of prior art, it is a kind of poor based on noise-like signal and improvement to propose
Divide the load model parameters discrimination method evolved.The noise-like signal that this method is obtained using emulation or actual measurement, it is poor by improving
Divide evolution algorithm, complete the identification of load model parameters；Using this method can realize to load model parameters time variation with
Track, final gained load model parameters identification result can be used for the simulation analysis of power system stability and control, to power system
Planning, operation have directive significance.
A kind of load model parameters discrimination method based on noise-like signal and improved differential evolution proposed by the present invention, its
It is characterised by, the method obtains voltage magnitude, voltage phase angle, active power, the amount of reactive power first from emulation or actual measurement
Whether the fluctuation amplitude surveyed curve and verify active power curves meets identification requirement；If meeting identification to require, load is selected
After model structure, using improved differential evolution algorithm between active power and the predicted value and actual measured value of reactive power
Sum of square of deviations carries out minimum optimization, obtains load model parameters；Finally measured using another section beyond identification curve bent
Line is verified to the validity of identification result, and by verification, then identification is completed.
The method specifically may include following steps：
1) by emulation or survey obtain the load bus voltage magnitude of setting time length, voltage phase angle, active power,
The measurement curve of reactive power, and the curve is divided into two sections, wherein one section as load model parameters identification, another section is used for
Identification result is verified；
2) to step 1) obtain as load model parameters identification active power curves, check the active power curves
Fluctuation amplitude whether meet identification require, if the fluctuation amplitude of active power curves be more than 1%, into step 3) start
Identification；Otherwise return to step 1), reselect metric data；
3) load model structure is selected, is recognized by improved differential evolution algorithm and is obtained corresponding load model parameters；Distinguish
During knowledge, using voltage magnitude curve and voltage phase angle curve as input, active power curves and reactive capability curve conduct
Output, obtained by active power is predicted with the actual measured value of reactive power and using mechanism load model and parameter
Predicted value between sum of square of deviations as object function to be optimized, using improved differential evolution algorithmic minimizing target letter
Number obtains load model parameters identification result；
4) validity of identification result is verified；Take step 1) obtain for the voltage amplitude verified to identification result
Value, voltage phase angle, active power, the measurement curve of reactive power, using step 3) obtained by parameter identification result had
Work(power, reactive power prediction, if the degree of fitting between predicted value and metric data is more than the degree of fitting for pre-setting, distinguish
Result is known by verification, and identification process is completed；Otherwise, return to step 1), re-start identification.
The features of the present invention and beneficial effect are：
Load model parameters discrimination method based on noise-like signal and improved differential evolution proposed by the present invention, relative to
Tradition is mainly reflected in based on the load model parameters discrimination method responded after disturbance, its advantage：The present invention can be realized to negative
The periodicity of lotus model parameter is repeatedly recognized, without in consideration system whether with the presence of disturbance, it is possible to achieve to load mould
The tracking of shape parameter time variation；Final gained load model parameters identification result of the invention can be used for power system stability and control
Simulation analysis, to Power System Planning, operation have certain directive significance.
Brief description of the drawings
Fig. 1 is the FB(flow block) of the inventive method.
Fig. 2 is power system single line structural representation in the embodiment of the present invention.
Fig. 3 is parameter identification gained active power prediction in the embodiment of the present invention and actual measurement curve synoptic diagram.
Specific embodiment
Load model parameters discrimination method based on noise-like signal and improved differential evolution proposed by the present invention, ties below
Close drawings and Examples further description as follows.
Load model parameters discrimination method based on noise-like signal and improved differential evolution proposed by the present invention, flow chart element
Figure is as shown in figure 1, comprise the following steps：
1) load bus voltage magnitude, the voltage of setting time length (typically taking 5-20s) are obtained by emulation or actual measurement
Phase angle, active power, the measurement curve of reactive power, and the curve is divided into two sections, wherein one section is used as load model parameters
Identification, another section is used to verify identification result；
2) to step 1) obtain as load model parameters identification active power curves, check the active power curves
Fluctuation amplitude whether meet identification require, if the fluctuation amplitude of active power curves be more than 1%, into step 3) start
Identification；Otherwise return to step 1), reselect metric data；
3) load model structure is selected, is recognized by improved differential evolution algorithm and is obtained corresponding load model parameters；Distinguish
During knowledge, using voltage magnitude curve and voltage phase angle curve as input, active power curves and reactive capability curve conduct
Output, obtained by active power is predicted with the actual measured value of reactive power and using mechanism load model and parameter
Predicted value between sum of square of deviations as object function to be optimized, using improved differential evolution algorithmic minimizing target letter
Number obtains load model parameters identification result；
4) validity of identification result is verified；Take step 1) obtain for the voltage amplitude verified to identification result
Value, voltage phase angle, active power, the measurement curve of reactive power, using step 3) parameter identification result that obtains carry out it is active
Power, reactive power prediction, if the degree of fitting between predicted value and metric data is more than the degree of fitting for pre-setting (typically taken
80%-90%), then identification result passes through verification, and identification process is completed；Otherwise, return to step 1), re-start identification.
The load model parameters discrimination method of one embodiment of the present of invention is further described below：
The middle power system single line structure of the present embodiment is as shown in Fig. 2 it includes 3 generators, 6 transmission lines of electricity and 3
The load of individual load bus, wherein BUS-5 and BUS-7 uses invariable power model, and the load of BUS-9 is using constant-impedance sensing in parallel
Motor model.
The discrimination method of the load model based on noise-like signal and improved differential evolution of the present embodiment includes following step
Suddenly：
1) power system shown in Fig. 2 is emulated, obtains load bus voltage magnitude, the voltage phase of setting time length
Angle, active power, the measurement curve of reactive power, and the curve is divided into two sections, wherein one section is distinguished as load model parameters
Know, another section is used to verify identification result；It is load in power system shown in Fig. 2 that curve used is recognized in the present embodiment
The measurement curve of node Bus-9, it is 10s to set (emulation) time, and step-length is 0.01s, and the curve of 0-5s carries out parameter identification, 5-
The curve of 10s verifies the validity of identification result；
2) to step 1) active power curves of 0-5s that obtain, check whether the fluctuation amplitude of active power curves meets
Identification is required, if the fluctuation amplitude of active power curves is more than 1%, into step 3) start identification；Otherwise return to step
1) metric data, is reselected；The fluctuation amplitude of the active power curves in this example is 2%, more than 1%, then into step
3)；
3) load model structure is selected；The integrated load model of this example selection constant-impedance parallel connection induction conductivity, while
Constant-impedance part is in the form of the resistor coupled in parallel reactance and reaction component conversion is in induction conductivity, therefore is treated in this model
The parameter of identification has four, is respectively rotor open circuit reactance X, rotor transient state reactance X', rotor open circuit time constant Td0, Yi Jijing
Load resistance R, by improved differential evolution algorithm, identification obtains corresponding load model parameters；In identification process, with voltage amplitude
Value curve and voltage phase angle curve as input, active power curves and reactive capability curve as output, by active power with
The actual measured value of reactive power and using mechanism load model and parameter be predicted obtained by predicted value between it is inclined
Difference quadratic sum as object function to be optimized, using improved differential evolution algorithmic minimizing object function obtaining load model
Parameter identification result；Comprise the following steps that：
Differential evolution algorithm with reference to Darwinian natural selection theory, using initialization of population, variation, intersect and selection
To realize the acquisition of global optimum.Wherein, each " individuality " is an one-dimensional vector in population, each element in vector
Referred to as " gene ", " individuality " is all load model parameters to be identified in the present invention, and " gene " is then for each is to be identified
Load model parameters.
3-1) according to the span of load model parameters, random generation initialization population, for follow-up evolutionary process；
J-th expression formula of parameter such as formula (1) is shown in remembering i-th individuality：
In formula, D is the sum of load model parameters to be identified, and value is 4 in the present embodiment；N is differential evolution algorithm
Population scale, in the present embodiment value be 40；The upper dividing value and floor value of respectively j-th parameter, rand (0,1)
It is a random number between 0~1；
Individuality 3-2) make a variation by the individuality of previous generation, three individualities are taken out at random, with the individual difference of two of which to
Amount adds after being weighted according to mutagenic factor with the 3rd body phase, produces follow-on variation individual, then i-th in G+1 generations
Shown in body expression formula such as formula (2)：
In formula, r1,r2,r3It is three individualities and i ≠ r randomly selected in individuality from population1≠r2≠r3, xr1(G)
It is r in G generations1The numerical value of individual individual ownership load model parameters, xr2(G) it is r in G generations2Individual individual ownership load mould
The numerical value of shape parameter, xr3(G) it is r in G generations3The numerical value of individual individual ownership load model parameters, F is mutagenic factor, this reality
Value is 0.9 in applying example；During variation, it is possible that the individual span of variation (is joined more than boundary constraint
Several upper dividing values and floor value) situation, in this case, then select boundary value as the individual numerical value of variation；
3-3) to each parameter to be identified in each gene in each individuality, i.e. each individuality, one is generated at random
Number, is intersected if the numerical value of random number is less than or equal to crossing-over rate, is not intersected otherwise；Then i-th in G+1 generations
In body shown in j-th parameter expression such as formula (3)：
In formula, CR is crossing-over rate, and value is 0.9 in the present embodiment；
The individuality u of new generation for 3-4) obtaining after the intersectioni(G+1) with previous generation individualities xi(G) between, two are calculated respectively
Individual respective objective function value, in two individualities less one of selection target function value be used for it is follow-on enter
Change, the object function is the sum of square of deviations between active power and the predicted value and actual measured value of reactive power；
3-5) selected, obtained population x of new generationi(G+1) after, optimum individual therein and previous generation kinds are selected
The optimum individual of group is contrasted, if the Euclidean distance of two generation optimum individuals is less than given threshold, proceeds by termination meter
Number；If terminated, counting has begun to and this time Euclidean distance, still less than threshold value, terminates counting and Jia one, otherwise terminates counting
Reset；If terminating counting reaches setting quantity (typically taking 50-300), the present embodiment value is 200, then stop differential evolution
Process, the result that current optimum individual is recognized as load model parameters.The present embodiment parameter identification acquired results such as table 1
It is shown；
The present embodiment load model parameters identification result table of table 1
4) validity of identification result is verified；In order to verify the validity of load model parameters identification result, step 1 is taken)
Obtain for identification result is verified voltage magnitude, voltage phase angle, active power, reactive power measurement curve,
The present embodiment takes voltage magnitude, voltage phase angle, active power, the measurement curve of reactive power of 5-10s to step 3) obtain
Parameter identification result is verified that carrying out active power, reactive power using resulting parameter identification result predicts, if in advance
Degree of fitting between measured value and actual measured value is more than the degree of fitting (typically taking 80%-90%) for pre-setting, and the present embodiment takes
90%, then it is assumed that identification result is completed by verification, identification process；Otherwise, return to step 1), re-start identification.This implementation
The contrast of the prediction of parameter identification gained active power and actual active measurement is as shown in figure 3, two fittings of curve simultaneously in example
It is 99.52% to spend, and more than 90%, thus demonstrates the validity of the present embodiment identification result.
Claims (3)
1. a kind of load model parameters discrimination method based on noise-like signal and improved differential evolution, it is characterised in that the party
Method obtains voltage magnitude, voltage phase angle, active power, the measurement curve of reactive power and verification first from emulation or actual measurement to be had
Whether the fluctuation amplitude of work(power curve meets identification requires；If meeting identification to require, after selecting load model structure, utilize
Improved differential evolution algorithm is carried out to the sum of square of deviations between active power and the predicted value and actual measured value of reactive power
Optimization is minimized, load model parameters are obtained；Curve is finally measured to identification result using another section beyond identification curve
Validity is verified, and by verification, then identification is completed.
2. the method for claim 1, it is characterised in that the method is comprised the following steps：
1) the load bus voltage magnitude of setting time length, voltage phase angle, active power, idle is obtained by emulation or survey
The measurement curve of power, and the curve is divided into two sections, wherein one section as load model parameters identification, another section is used for distinguishing
Know result to be verified；
2) to step 1) obtain as load model parameters identification active power curves, check the ripple of the active power curves
Whether dynamic amplitude meets identification requires, if the fluctuation amplitude of active power curves is more than 1%, into step 3) start to distinguish
Know；Otherwise return to step 1), reselect metric data；
3) load model structure is selected, is recognized by improved differential evolution algorithm and is obtained corresponding load model parameters；Recognized
Cheng Zhong, using voltage magnitude curve and voltage phase angle curve as input, active power curves and reactive capability curve as output,
By the actual measured value of active power and reactive power be predicted using mechanism load model and parameter obtained by it is pre-
Sum of square of deviations between measured value is obtained as object function to be optimized using improved differential evolution algorithmic minimizing object function
To load model parameters identification result；
4) validity of identification result is verified；Take step 1) obtain for voltage magnitude, the electricity verified to identification result
Pressure phase angle, active power, the measurement curve of reactive power, using step 3) obtained by parameter identification result carry out wattful power
Rate, reactive power prediction, if the degree of fitting between predicted value and metric data is more than the degree of fitting for pre-setting, identification knot
Fruit is completed by verification, identification process；Otherwise, return to step 1), re-start identification.
3. method as claimed in claim 2, it is characterised in that the step 3) in recognized by improved differential evolution algorithm
To corresponding load model parameters, comprise the following steps that：
3-1) according to the span of load model parameters, random generation initialization population, for follow-up evolutionary process；Note the
In i individuality shown in j-th expression formula of parameter such as formula (1)：
In formula, D is the sum of load model parameters to be identified；N is the population scale of differential evolution algorithm；Respectively
The upper dividing value and floor value of j-th parameter, rand (0,1) are a random number between 0~1；
Individuality 3-2) make a variation by the individuality of previous generation, three individualities are taken out at random, pressed with the individual difference vector of two of which
Add with the 3rd body phase after being weighted according to mutagenic factor, produce follow-on variation individual, then i-th body surface in G+1 generations
Up to formula such as formula (2) Suo Shi：
In formula, r1,r2,r3It is three individualities and i ≠ r randomly selected in individuality from population1≠r2≠r3, xr1(G) it is the
R in G generations1The numerical value of individual individual ownership load model parameters, xr2(G) it is r in G generations2Individual individual ownership load model ginseng
Several numerical value, xr3(G) it is r in G generations3The numerical value of individual individual ownership load model parameters, F is mutagenic factor；
3-3) to each parameter to be identified in each gene in each individuality, i.e. each individuality, a random number is generated, such as
The numerical value of fruit random number is then intersected less than or equal to crossing-over rate, is not intersected otherwise；Then in G+1 generations in i-th individuality
Shown in j-th parameter expression such as formula (3)：
In formula, CR is crossing-over rate；
The individuality u of new generation for 3-4) obtaining after the intersectioni(G+1) with previous generation individualities xi(G) between, two individualities are calculated respectively
Respective objective function value, less one of selection target function value is used for follow-on evolution, institute in two individualities
State the sum of square of deviations between the predicted value and actual measured value of object function as active power and reactive power；
3-5) selected, obtained population x of new generationi(G+1) after, optimum individual therein is selected with previous generation populations
Optimum individual is contrasted, if the Euclidean distance of two generation optimum individuals is less than given threshold, is proceeded by termination and is counted；Such as
Fruit termination counting has begun to and this time Euclidean distance still less than threshold value, then terminates counting Jia one, otherwise terminates counting and resets；
If terminating counting reaches setting quantity, stop differential evolution process, using current optimum individual as load model parameters
The result of identification.
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| CN112800676B (en) * | 2021-01-28 | 2023-12-26 | 中国三峡新能源(集团)股份有限公司 | Wind farm parameter step-by-step identification dynamic equivalence method based on IDE-BAS algorithm |
| CN112989581A (en) * | 2021-02-25 | 2021-06-18 | 国网重庆市电力公司电力科学研究院 | Frequency dynamic simulation method and device, electronic equipment and storage medium |
| CN115062947A (en) * | 2022-06-09 | 2022-09-16 | 国网湖南省电力有限公司 | Power grid load model parameter identification method based on noise-like data |
| CN115062947B (en) * | 2022-06-09 | 2024-05-31 | 国网湖南省电力有限公司 | Power grid load model parameter identification method based on noise-like data |
| CN115408949A (en) * | 2022-11-02 | 2022-11-29 | 广东电网有限责任公司中山供电局 | Load model parameter identification method, system, equipment and medium |
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Application publication date: 20170531 Assignee: Beijing Qingda Zhixin Technology Co.,Ltd. Assignor: TSINGHUA University Contract record no.: X2023990000575 Denomination of invention: A Parameter Identification Method for Load Model Based on Noise-like Signals and Improved Differential Evolution Granted publication date: 20190726 License type: Common License Record date: 20230602 |
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