CN107357978A - A kind of synchronous generator excited system performance estimating method - Google Patents
A kind of synchronous generator excited system performance estimating method Download PDFInfo
- Publication number
- CN107357978A CN107357978A CN201710504443.8A CN201710504443A CN107357978A CN 107357978 A CN107357978 A CN 107357978A CN 201710504443 A CN201710504443 A CN 201710504443A CN 107357978 A CN107357978 A CN 107357978A
- Authority
- CN
- China
- Prior art keywords
- mtd
- msub
- mrow
- mtr
- factor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The present invention relates to a kind of synchronous generator excited system performance estimating method, this method comprises the following steps：(1) the tree-shaped assessment models of synchronous generator excited system are built, the model top layer is motor excitation systematic function to be assessed, and bottom includes multiple basal evaluation factors, and intermediate layer includes multiple classification assessment factors at least provided with one layer, every layer；(2) weight of all basal evaluation factors and assessment factor of classifying is determined；(3) the basal evaluation result of all basal evaluation factors of bottom is obtained；(4) top layer motor excitation systematic function total evaluation result to be assessed is successively calculated since tree-shaped assessment models bottom according to basal evaluation result and each weight for assessing factor.Compared with prior art, the present invention can more accurately, stably, easily assess the performance of excitation system, more perfect, reliable, timely foundation be provided for electric power system dispatching, to instructing work on the spot and parameter optimization to have important theory and practice meaning.
Description
Technical field
The present invention relates to a kind of generator performance appraisal procedure, more particularly, to a kind of synchronous generator excited system performance
Appraisal procedure.
Background technology
Expanding day by day for power network scale is more disturbed with a large amount of grid-connected brought to power system of the fluctuation energy such as wind-powered electricity generation
Dynamic, the safe and stable operation problem of power network becomes increasingly conspicuous, and the adjustment effect of excitation system has obtained more attention, to excitation system
System carries out comprehensive and accurate Performance Evaluation and applied to instructing the importance of work on the spot and electric power system dispatching to be also more and more
People are of interest.Synchronous generator excited system points to the power supply and its auxiliary device that synchronous generator provides excitation.Excitation Adjustment
The output that device controls exciting power unit according to input signal and adjustment criteria is saved, realizes control function and defencive function：Control
Function improves the stability that generating set is run by maintaining synchronous generator set end voltage and distributing reactive power；Protect work(
Can by it is low encourage limitation, cross encourage limitation, demagnetization act etc. measure, protection generator and other equipment be no more than capacity limitation.
The Performance Evaluation of synchronous generator excited system and parameter optimization workflow are as follows at present.Marked first according to country
Accurate and professional standard requirement carries out every experiment, and with standard requirement operate mark, and then judges excitation system performance
It is whether qualified；Then by the identification of excitation system model parameter, the check of excitation system equivalent performance is realized；Finally by imitative
Very with optimization, improve the performance of excitation system.Improve the implication of performance including 2 points, first, adjust the model of field regulator
Parameter, make performance excitation system not up to standard up to standard；Second, on the basis of up to standard, the parameter of field regulator is entered to advance
One-step optimization, make the performance of excitation system more excellent, to adapt to the regulation requirement of higher level.Existing excitation system performance estimating method
Operating excitation system, which meets scheduling basic demand, to be ensured to mark assessment by excitation system performance parameter.In this process
In, judge that the good and bad method of excitation system performance indications turns into a research direction.
In conventional work, the parameter optimization of excitation system model uses the appraisal procedure based on expertise more.I.e.
Using the empirical value of electric power system dispatching as foundation, parameters of excitation system is adjusted by senior technical specialist.This parameter is excellent
The pattern of change can improve according to historical data to parameters of excitation system, play the work for instructing optimization produced on-site work
With achieving good effect within a very long time.But traditional parameters Optimizing Mode has certain limitation：By
In the strategy for lacking a set of reasonably quantitative classified estimation, the validity of parameter adjustment can not be evaluated；And to senior
The dependence of technical specialist is stronger, working experience deficiency employee can not complete independently parameter optimization operation.These problems exist
In traditional power network performance and unobvious, with power network scale gradual expansion and fluctuation regenerative resource it is a large amount of grid-connected, give
Traditional power network brings certain fluctuation and impact, and higher requirement is proposed to electric power system dispatching, only provides excitation system
The whether qualified needs that can not fully meet safe operation of effective dynamic behavior.For example, the excitation system of performance " relatively good "
There is stronger regulation stabilizing power than the excitation system of performance " just meeting standard ", when power network occurs compared with large disturbances, very
Obviously the excitation system that should preferentially put into performance " relatively good " carries out power system stability regulation.
The content of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of synchronous generator is encouraged
Magnetic system performance estimating method.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of synchronous generator excited system performance estimating method, this method comprise the following steps：
(1) the tree-shaped assessment models of synchronous generator excited system are built, the model top layer is motor excitation system to be assessed
Unite performance, bottom include it is multiple be used to assess the basal evaluation factors of the excitation system performance, intermediate layer at least provided with one layer,
Every layer includes multiple classification assessment factors；
(2) weight of all basal evaluation factors and assessment factor of classifying is determined；
(3) the basal evaluation result of all basal evaluation factors of bottom is obtained；
(4) successively calculated since tree-shaped assessment models bottom according to basal evaluation result and each weight for assessing factor
The motor excitation systematic function total evaluation result to be assessed to top layer.
Described intermediate layer sets one layer, and the classification assessment factor in this layer as father node and is connected in bottom at least
One basal evaluation factor.
Step (3) is specially：
(31) set and assess collection K=[k1, k2..., kn], assess p-th of element k in collection KpRepresent p-th of evaluation etc.
Level, p=1,2 ... ... n, n are opinion rating total number；
(32) baseline ratings of each basal evaluation factor are obtained, described baseline ratings are corresponding to assess collection K
In an opinion rating, and then the basal evaluation matrix of each basal evaluation factor is obtained, for i-th of classification assessment factor
The basal evaluation matrix of j-th of the basal evaluation factor connected is denoted as Kij'：
Kij'=[kij1 kij 2… kijn],
Kij' in p-th of element kijpJ-th of basal evaluation factor that i-th of classification assessment factor of expression is connected is for commenting
Estimate the evaluation of estimate for concentrating p-th of rating level, i=1,2 ... ... I, j=1,2 ... ... J, I represent intermediate layer classification assessment factor
Total number, J represents the total number of basal evaluation factor that i-th of classification assessment factor is connected.
Step (4) is specially：
(41) the basal evaluation matrix of consequence K corresponding to i-th of classification assessment factor is obtainedij：
(42) the weight matrix W of the basal evaluation factor corresponding to i-th of classification assessment factor is obtainediJ：
WiJ=[wi1 … wij … wiJ],
wijThe weight of j-th of the basal evaluation factor connected by i-th of classification assessment factor；
(43) i-th of classification assessment factor assessment result matrix K is obtained according to following formulai'：
kipRepresent that i-th of classification assessment factor concentrates the evaluation of estimate of p-th of rating level for assessing；
(44) by I assessment factor assessment result matrix group synthetic mesophase layer assessment result matrix K of classifyingi：
(45) intermediate layer weight matrix W is obtainedi：
Wi=[w1 … wi … wI],
wiRepresent the weight of i-th of classification assessment factor；
(46) motor excitation systematic function total evaluation matrix K to be assessed is asked for according to following formula：
kpRepresent that motor excitation systematic function to be assessed is subordinate to angle value for assessment p-th of rating level of concentration；
(47) it is motor excitation systematicness to be assessed to choose the opinion rating in total evaluation matrix K corresponding to maximum
Can total evaluation grade.
The classification assessment factor in intermediate layer includes static voltage adjustment performance, static reactive performance, transient state microvariations performance, transient state
At least one of large disturbances performance and troubleshooting performance.
Basal evaluation factor corresponding to static voltage adjustment performance includes generator voltage static difference rate and controllable silicon adjustment angle
At least one of with the uniformity of factory settings value.
Basal evaluation factor corresponding to static reactive performance includes reactive current permanent speed regulation.
Basal evaluation factor corresponding to described transient state microvariations performance includes the voltage overshoot under unloaded 10% step
Amount, the number of oscillation under unloaded 10% step, the regulating time under unloaded 10% step, with the resistance under the step of nominal load 3%
Buddhist nun than, with the step of nominal load 3% P fluctuation number, with the regulating time under the step of nominal load 3% and containing PSS's
At least one of system damping.
Basal evaluation factor corresponding to described transient state large disturbances performance includes AC excitation ceiling voltage multiple, excitation
Top value electric current multiple, allow top value current duration, AC excitation nominal response multiple, from shunt excitation static excitation respond when
Between, the overshoot under unloaded 100% excitation, get rid of voltage overshoot under rated load, get rid of the number of oscillation under rated load with
And get rid of at least one of regulating time under rated load.
Can the basal evaluation factor corresponding to described troubleshooting performance includes reliable demagnetization.
Compared with prior art, the invention has the advantages that：
(1) present invention establishes tree-shaped assessment models, realizes the grading evaluation of excitation system performance, meets excitation system performance
The development trend of assessment technology, the performance of excitation system can more accurately, stably, be easily assessed, is electric power system dispatching
More perfect, reliable, timely foundation is provided；
(2) the tree-shaped assessment models basal evaluation factor of the present invention is assessed using basal evaluation matrix, such a assessment side
Formula is simple and convenient, while assigns different weights to the assessment factor in each layer, towards electric power system dispatching sensitive indicator such as
Transient state microvariations performance, transient state large disturbances performance, larger weight is assigned in classified estimation, and then cause assessment result more
Meet reality, it is as a result relatively reliable；
(3) the tree-shaped assessment models of excitation system performance are built to instructing work on the spot and parameter optimization to have important theory
And practice significance, for the angle of parameter identification, excitation system grading performance, which is assessed, can be used for judging that different groups of parameters are corresponding
Excitation system performance quality, thus can be by the adjustment and improvement to simulation parameter, to instruct actual excitation system model
Parameter；For the angle of electric power system dispatching, excitation system carries different adjustment effects at the node of different status, right
The evaluation of excitation system performance progress Comprehensive can avoid the occurrence of the situation of waist performance and performance deficiency.
Brief description of the drawings
Fig. 1 is the structural representation of the tree-shaped assessment models of the present invention；
Fig. 2 is the flow chart of synchronous generator excited system performance estimating method of the present invention；
Fig. 3 is two, the present embodiment power plant DCgenerator motor field system transient modelling microvariations performance comparison radar map；
Fig. 4 is two, the present embodiment power plant DCgenerator motor field system transient modelling large disturbances performance comparison radar map.
Embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment
A kind of synchronous generator excited system performance estimating method, this method comprise the following steps：
(1) the tree-shaped assessment models of synchronous generator excited system are built, the model top layer is motor excitation system to be assessed
Unite performance, bottom include it is multiple be used to assess the basal evaluation factors of the excitation system performance, intermediate layer at least provided with one layer,
Every layer includes multiple classification assessment factors, and intermediate layer sets one layer, and the classification assessment factor in this layer as father node and connects
At least one basal evaluation factor in bottom；
(2) weight of all basal evaluation factors and assessment factor of classifying is determined；
(3) the basal evaluation result of all basal evaluation factors of bottom is obtained；
(4) successively calculated since tree-shaped assessment models bottom according to basal evaluation result and each weight for assessing factor
The motor excitation systematic function total evaluation result to be assessed to top layer.
Be as shown in Figure 1 the tree-shaped assessment models schematic diagram of synchronous generator excited system, in Fig. 2, A represents bottom, B expressions
Intermediate layer, the circle in A represent 1 basal evaluation factor, and the circle in B represents 1 classification assessment factor.
When carrying out excitation system Performance Evaluation, generally first by experiment and other method, to every basal evaluation because
Element is given a mark, then by data mining and the processing of big data, according to each assessment factor in each layer to excitation system
The effect of performance and significance level, the weight of each assessment factor is established respectively；Last weighted calculation goes out the comprehensive of excitation system
Close performance.
It is as follows to the appraisal procedure of each assessment factor when excitation system performance indications are assessed：
By evaluation criteria excitation system performance is divided into five grades, and (quantity of grade can also in other embodiments
Any other value), set evaluating matrix as：
K=[outstanding good medium qualified unqualified],
Five numerical value of evaluating matrix represent the degree of membership of five grades respectively.
Excitation system performance indications can be divided into qualitative class requirement and quantitative class requires two major classes.Qualitative class requirement refers to performance
Standard requires the excitation system equipment that must be installed or the performance requirement that can not be quantified.It is characterized in：Performance requirement is only
Have two it is extreme, quantitative analysis can not be carried out, having has, and nothing is nothing, situations such as in the absence of partly having, partly meeting.Quantitative class
It is required that referring to the requirement by performance indications acquired in Site Detection or engine cut off test, it is characterized in：Can be in base up to standard
The further grade classifications such as excellent middle difference are carried out on plinth.
For qualitative class index, only qualified and unqualified two grades, thus its evaluating matrix is：
It is qualified to be：[0 001 0],
Or it is：
[0.25 0.25 0.25 0.25 0]；
It is unqualified to be：
[0 0 0 0 1]。
The assessment result that the difference of two kinds of qualified appraisal procedures is to meet standard requirement is designated as qualified grade also completely
It is to say to divide equally in outstanding, good, medium, qualified four grades.Both methods of marking can produce to final assessment result
Raw to influence, the former can slightly reduce final scoring, and the latter can slightly improve final scoring.This method is assessed using the first
Rating result of the method as qualitative class index, to keep the uniformity of the basal evaluation matrix of basal evaluation factor.For fixed
Measure for class index, the threshold value for the index in standard being carried out first five grades divides, and determines to locate further according to test data
In in which grade threshold.Index in standard meets the requirement of China's power network actual motion, can be used as excitation system performance point
Level is assessed and ruling.
Therefore, step (3) is specially：
(31) set and assess collection K=[k1, k2..., kn], assess p-th of element k in collection KpRepresent p-th of evaluation etc.
Level, p=1,2 ... ... n, n are opinion rating total number, n=5 in the present embodiment, and then correspondingly：K=is [outstanding good medium
It is qualified unqualified]；
(32) baseline ratings of each basal evaluation factor are obtained, baseline ratings are corresponding to assess one collected in K
Individual opinion rating, and then the basal evaluation matrix of each basal evaluation factor is obtained, connected for i-th of classification assessment factor
The basal evaluation matrix of j-th of basal evaluation factor be denoted as Kij'：
Kij'=[kij1 kij 2… kijn],
Kij' in p-th of element kijpJ-th of basal evaluation factor that i-th of classification assessment factor of expression is connected is for commenting
Estimate the evaluation of estimate for concentrating p-th of rating level, i=1,2 ... ... I, j=1,2 ... ... J, I represent intermediate layer classification assessment factor
Total number, J represents the total number of basal evaluation factor that i-th of classification assessment factor is connected.
Step (4) is specially：
(41) the basal evaluation matrix of consequence K corresponding to i-th of classification assessment factor is obtainedij：
(42) the weight matrix W of the basal evaluation factor corresponding to i-th of classification assessment factor is obtainediJ：
WiJ=[wi1 … wij … wiJ],
wijThe weight of j-th of the basal evaluation factor connected by i-th of classification assessment factor；
(43) i-th of classification assessment factor assessment result matrix K is obtained according to following formulai'：
kipRepresent that i-th of classification assessment factor concentrates the evaluation of estimate of p-th of rating level for assessing；
(44) by I assessment factor assessment result matrix group synthetic mesophase layer assessment result matrix K of classifyingi：
(45) intermediate layer weight matrix W is obtainedi：
Wi=[w1 … wi … wI],
wiRepresent the weight of i-th of classification assessment factor；
(46) motor excitation systematic function total evaluation matrix K to be assessed is asked for according to following formula：
kpRepresent that motor excitation systematic function to be assessed is subordinate to angle value for assessment p-th of rating level of concentration；
(47) it is motor excitation systematicness to be assessed to choose the opinion rating in total evaluation matrix K corresponding to maximum
Can total evaluation grade.
The classification assessment factor in intermediate layer includes static voltage adjustment performance, static reactive performance, transient state microvariations performance, transient state
At least one of large disturbances performance and troubleshooting performance.
Wherein, the basal evaluation factor corresponding to static voltage adjustment performance includes generator voltage static difference rate and thyristor regulating
Save at least one of uniformity of angle and factory settings value.
Basal evaluation factor corresponding to static reactive performance includes reactive current permanent speed regulation.
Basal evaluation factor corresponding to transient state microvariations performance includes the voltage overshoot under unloaded 10% step, zero load
The number of oscillation under 10% step, the regulating time under unloaded 10% step, the damping ratio with the step of nominal load 3%, band
P under the step of nominal load 3% fluctuates number, with the regulating time under the step of nominal load 3% and the system damping containing PSS
At least one of.
Basal evaluation factor corresponding to transient state large disturbances performance includes AC excitation ceiling voltage multiple, excitation limit electricity
Flow multiple, allow top value current duration, AC excitation nominal response multiple, from shunt excitation static excitation response time, zero load
Overshoot under 100% excitation, voltage overshoot under rated load is got rid of, the number of oscillation under rated load is got rid of and gets rid of specified
At least one of regulating time under load.
Can the basal evaluation factor corresponding to troubleshooting performance includes reliable demagnetization.
Such scheme is commented using form of the evaluating matrix of corresponding evaluation grade as assessment result, this form is characterized
Evaluation grade can be reflected in a manner of membership function by estimating result, realize the classified estimation of excitation system performance.Concrete principle
It may be referred to specific embodiment part related content.In above-mentioned excitation system performance estimating method, it will belong in assessment factor
At least one of weight of non-accidentally class, qualitative class and insensitive class is set to 0.It is described it is non-accidentally class, qualitative class and
The weight of insensitive class would generally greatly strengthen the degree of membership in qualified grade.Optionally weighed according to actual conditions
0 is reset to, then can eliminate the uneven influence to caused by qualified grade.And then by the static voltage adjustment performance, static reactive
At least one of weight of performance and troubleshooting performance is set to 0.
Sum it up, synchronous generator excited system performance estimating method of the present invention specifically comprises the following steps：
Step 110：Two layers of tree-shaped assessment models is built according to above-mentioned principle, two layers are carried out to excitation system performance indications
Secondary arrangement and classification, structure basal evaluation factor and classification assessment factor；
Step 120：It is that above-mentioned basal evaluation factor and each assessment factor of classification assessment factor assign according to above-mentioned principle
Corresponding weight：Determine the weight matrix W of the basal evaluation factor corresponding to i-th of classification assessment factoriJWith intermediate layer weight
Matrix Wi；
Step 130：The assessment result of each basal evaluation factor is obtained according to above-mentioned principle：To every basal evaluation factor
Given a mark, obtain the basal evaluation matrix of consequence K of basal evaluation factorij。
Step 140：The assessment result of the basal evaluation factor is substituted into two layers of assessment models according to above-mentioned principle
Bottom and calculate its last layer assessment result：Calculate intermediate layer assessment result matrix Ki。
Step 150：The final acquisition total evaluation result of calculating is continued up according to above-mentioned principle：It is calculated to be assessed
Motor excitation systematic function total evaluation matrix K.
The present embodiment, according to above-mentioned principle and step, divides first by taking the excitation system of certain two generating set of power plant as an example
The other excitation system to two units carries out performance classified estimation, according to assessment result to the excellent of two DCgenerator motor field systematic functions
It is bad to be analyzed, and suggest can focusing on comparative's transient state microvariations performance and transient state large disturbances performance both sensitive indicators.
The excitation system of two generating sets of certain power plant (unit 1 and unit 2) is chosen as analysis object.Two generators
Group is 600MW units, carries out adjustment of field excitation using the type excitation controllers of ABB UNITROL 5000, excitation mode is from shunt excitation
Static excitation.
Unloaded 10% step, 3% step of load, PSS systems are can obtain in Modeling of excitation system and the report of PSS regulation experiments
The data of three experiments of damping.Collect as shown in table 1.
The excitation system step disturbance measured data of table 1
Unloaded 100% excitation experiment Simulink emulation data are as shown in table 2.
The overshoot result of calculation of 2 100% unloaded excitation of table
| Symbol | Excitation system performance | The test data of unit 1 | The test data of unit 2 |
| U46 | Unloaded 100% excitation, overshoot | 8.8% | 9.1% |
It is as shown in table 3 to get rid of running test BPA emulation data.
The excitation system of table 3 gets rid of rated load step response emulation data
According to the measured data in excitation system nameplate parameter and test report, by being carried out with table 1, table 2, the content of table 3
After contrast, the basal evaluation matrix of obtained each basal evaluation factor, basal evaluation matrix is as shown in table 4.Wherein vacant number
Slash represents according to this.
The excitation system Performance Evaluation matrix of table 4
The present embodiment firstly evaluates the combination property of unit 1, then contrasts the large and small perturbation of transient state of unit 1 and unit 2
Energy.
The classification assessment factor assessment result matrix K of unit 1i' be：
K2'=W21·K21=[1] [0 001 0]=[0 001 0],
K5'=W51·K51=[1] [0 001 0]=[0 001 0].
The motor excitation systematic function total evaluation matrix K of unit 1 is：
The analysis of deciding grade and level is assessed according to grading performance, grade of the maximum value of degree of membership as the excitation system should be chosen.
The maximum of the evaluation result degree of membership of unit 1 is located at qualified place, therefore comprehensive evaluation result is qualified.
Found after being further analyzed to grading performance assessment result, static voltage adjustment performance, the static nothing of excitation system
Work(performance, troubleshooting Performance Evaluation matrix are [0 001 0], and this three weights added up have reached 0.2257, pole
The earth strengthens the degree of membership in qualified grade.Two units also have certain degree of membership in the dimension of remaining grade, so
And the degree of membership of these evaluation grades is less than the degree of membership of qualified grade, it is caused not obtain body in final appraisal results
It is existing.Theoretically analyze, static voltage adjustment performance, static reactive performance, the troubleshooting performance this three of excitation system assesses knot
Fruit for it is qualified be not necessarily all accidental, but because normal can reach qualified level, and this by the excitation system of factory testing
A little indexs, which either belong to qualitative class index, to be further classified, or belong to insensitive index and need not enter traveling one
Step classification.If that is, cutting this three indexs in final assessment result, can just eliminate to caused by qualified grade not
The influence of balance.From the perspective of from this angle, this example equivalent to by static voltage adjustment performance, static reactive performance, troubleshooting performance this
The weighted value of three is set to 0.
The transient state microvariations performance of unit 2, the corresponding classification assessment of the two classification assessment factors of transient state large disturbances performance
Factors assessment matrix of consequence Ki' be：
If only considering the two classification assessment factors of the transient state microvariations performance of two units, transient state large disturbances performance, comment
Valency result is summarized as shown in table 5.
The excitation system transient state microvariations performance of table 5 and transient state large disturbances Evaluation results are summarized
In transient state microvariations performance, the outstanding and medium index ratio unit 2 of unit 1 is much higher, and good index is slightly below machine
Group 2；In transient state large disturbances performance, the qualified index of unit 1 is slightly above unit 2, and outstanding index is slightly below unit 2.Two kinds of performances
Weight be respectively 0.5128 and 0.2615, about twice of relation, therefore the performance of unit 1 is better than unit 2.
In order to more intuitively carry out the judgement of performance, transient state microvariations performance comparison radar map such as Fig. 3 of two units is drawn,
And transient state large disturbances performance comparison radar map such as Fig. 4.From the point of view of radar map, the transient state microvariations performance of unit 1 is substantially better than
Unit 2, the transient state large disturbances performance and the gap of unit 2 of unit 1 are little, consistent with quantitative analysis results.
Claims (10)
1. a kind of synchronous generator excited system performance estimating method, it is characterised in that this method comprises the following steps：
(1) the tree-shaped assessment models of synchronous generator excited system are built, the model top layer is motor excitation systematicness to be assessed
Can, bottom includes multiple basal evaluation factors for being used to assess the excitation system performance, and intermediate layer is at least provided with one layer, every layer
Including multiple classification assessment factors；
(2) weight of all basal evaluation factors and assessment factor of classifying is determined；
(3) the basal evaluation result of all basal evaluation factors of bottom is obtained；
(4) top is successively calculated since tree-shaped assessment models bottom according to basal evaluation result and each weight for assessing factor
Layer motor excitation systematic function total evaluation result to be assessed.
2. a kind of synchronous generator excited system performance estimating method according to claim 1, it is characterised in that described
Intermediate layer sets one layer, classification assessment factor in this layer as father node and connect at least one basal evaluation in bottom because
Element.
A kind of 3. synchronous generator excited system performance estimating method according to claim 2, it is characterised in that step
(3) it is specially：
(31) set and assess collection K=[k1, k2..., kn], assess p-th of element k in collection KpRepresent p-th of opinion rating, p=
1,2 ... ... n, n are opinion rating total number；
(32) baseline ratings of each basal evaluation factor are obtained, described baseline ratings are corresponding to be assessed in collection K
One opinion rating, and then the basal evaluation matrix of each basal evaluation factor is obtained, connect for i-th of classification assessment factor
The basal evaluation matrix of j-th of the basal evaluation factor connect is denoted as Kij'：
Kij'=[kij1 kij2 … kijn],
Kij' in p-th of element kijpRepresent that j-th of basal evaluation factor that i-th of classification assessment factor is connected collects for assessing
In p-th of rating level evaluation of estimate, i=1,2 ... ... I, j=1,2 ... ... J, I represent the total of intermediate layers classification assessment factors
Number, J represent the total number for the basal evaluation factor that i-th of classification assessment factor is connected.
A kind of 4. synchronous generator excited system performance estimating method according to claim 3, it is characterised in that step
(4) it is specially：
(41) the basal evaluation matrix of consequence K corresponding to i-th of classification assessment factor is obtainedij：
<mrow>
<msub>
<mi>K</mi>
<mrow>
<mi>i</mi>
<mi>J</mi>
</mrow>
</msub>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msup>
<msub>
<mi>K</mi>
<mrow>
<mi>i</mi>
<mn>1</mn>
</mrow>
</msub>
<mo>&prime;</mo>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msup>
<msub>
<mi>K</mi>
<mrow>
<mi>i</mi>
<mi>J</mi>
</mrow>
</msub>
<mo>&prime;</mo>
</msup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mn>11</mn>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mn>12</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mn>1</mn>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mi>J</mi>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mi>J</mi>
<mn>2</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mi>J</mi>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>,</mo>
</mrow>
(42) the weight matrix W of the basal evaluation factor corresponding to i-th of classification assessment factor is obtainediJ：
WiJ=[wi1 … wij … wiJ],
wijThe weight of j-th of the basal evaluation factor connected by i-th of classification assessment factor；
(43) i-th of classification assessment factor assessment result matrix K is obtained according to following formulai'：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msup>
<msub>
<mi>K</mi>
<mi>i</mi>
</msub>
<mo>&prime;</mo>
</msup>
<mo>=</mo>
<msub>
<mi>W</mi>
<mrow>
<mi>i</mi>
<mi>J</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>W</mi>
<mrow>
<mi>i</mi>
<mi>J</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>w</mi>
<mrow>
<mi>i</mi>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>w</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>w</mi>
<mrow>
<mi>i</mi>
<mi>J</mi>
</mrow>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mn>11</mn>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mn>12</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mn>1</mn>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mi>J</mi>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mi>J</mi>
<mn>2</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mi>J</mi>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mi>p</mi>
</mrow>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>,</mo>
</mrow>
kipRepresent that i-th of classification assessment factor concentrates the evaluation of estimate of p-th of rating level for assessing；
(44) by I assessment factor assessment result matrix group synthetic mesophase layer assessment result matrix K of classifyingi：
<mrow>
<msub>
<mi>K</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msup>
<msub>
<mi>K</mi>
<mn>1</mn>
</msub>
<mo>&prime;</mo>
</msup>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>...</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msup>
<msub>
<mi>K</mi>
<mi>i</mi>
</msub>
<mo>&prime;</mo>
</msup>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>...</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msup>
<msub>
<mi>K</mi>
<mi>I</mi>
</msub>
<mo>&prime;</mo>
</msup>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>k</mi>
<mn>11</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mn>12</mn>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mn>1</mn>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>I</mi>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>I</mi>
<mn>2</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>I</mi>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>,</mo>
</mrow>
(45) intermediate layer weight matrix W is obtainedi：
Wi=[w1 … wi … wI],
wiRepresent the weight of i-th of classification assessment factor；
(46) motor excitation systematic function total evaluation matrix K to be assessed is asked for according to following formula：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<mi>K</mi>
<mo>=</mo>
<msub>
<mi>W</mi>
<mi>i</mi>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>K</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>w</mi>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>w</mi>
<mi>i</mi>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>w</mi>
<mi>I</mi>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>k</mi>
<mn>11</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mn>12</mn>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mn>1</mn>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>I</mi>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>I</mi>
<mn>2</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mrow>
<mi>I</mi>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>k</mi>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mi>p</mi>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>k</mi>
<mi>n</mi>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>,</mo>
</mrow>
kpRepresent that motor excitation systematic function to be assessed is subordinate to angle value for assessment p-th of rating level of concentration；
(47) opinion rating chosen in total evaluation matrix K corresponding to maximum is whole for motor excitation systematic function to be assessed
Body evaluation grade.
A kind of 5. synchronous generator excited system performance estimating method according to claim 2, it is characterised in that intermediate layer
Classification assessment factor include static voltage adjustment performance, static reactive performance, transient state microvariations performance, transient state large disturbances performance and
At least one of troubleshooting performance.
6. a kind of synchronous generator excited system performance estimating method according to claim 5, it is characterised in that static state is adjusted
The basal evaluation factor corresponding to performance is pressed to include generator voltage static difference rate and controllable silicon adjustment angle and factory settings value
At least one of uniformity.
A kind of 7. synchronous generator excited system performance estimating method according to claim 5, it is characterised in that static nothing
Basal evaluation factor corresponding to work(performance includes reactive current permanent speed regulation.
8. a kind of synchronous generator excited system performance estimating method according to claim 5, it is characterised in that described
Basal evaluation factor corresponding to transient state microvariations performance includes voltage overshoot, unloaded 10% step under unloaded 10% step
Under the number of oscillation, the regulating time under unloaded 10% step, with the damping ratio under the step of nominal load 3%, band nominal load
Under 3% step P fluctuation number, with the regulating time under the step of nominal load 3% and the system damping containing PSS at least
It is a kind of.
9. a kind of synchronous generator excited system performance estimating method according to claim 5, it is characterised in that described
Basal evaluation factor corresponding to transient state large disturbances performance include AC excitation ceiling voltage multiple, excitation limit electric current multiple,
Allow top value current duration, AC excitation nominal response multiple, from shunt excitation static excitation response time, zero load 100%
Overshoot under encouraging, voltage overshoot under rated load is got rid of, the number of oscillation under rated load is got rid of and gets rid of under rated load
At least one of regulating time.
10. a kind of synchronous generator excited system performance estimating method according to claim 5, it is characterised in that described
Troubleshooting performance corresponding to basal evaluation factor include can reliable demagnetization.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710504443.8A CN107357978A (en) | 2017-06-28 | 2017-06-28 | A kind of synchronous generator excited system performance estimating method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710504443.8A CN107357978A (en) | 2017-06-28 | 2017-06-28 | A kind of synchronous generator excited system performance estimating method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107357978A true CN107357978A (en) | 2017-11-17 |
Family
ID=60273252
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710504443.8A Pending CN107357978A (en) | 2017-06-28 | 2017-06-28 | A kind of synchronous generator excited system performance estimating method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107357978A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108681817A (en) * | 2018-05-17 | 2018-10-19 | 中电普瑞电力工程有限公司 | A kind of excitation system performance estimating method, device and storage medium |
| CN109308529A (en) * | 2018-10-11 | 2019-02-05 | 国网山东省电力公司电力科学研究院 | A kind of DCgenerator motor field function synthesized method of evaluating performance applied in net source platform |
| CN109435955A (en) * | 2018-10-22 | 2019-03-08 | 百度在线网络技术（北京）有限公司 | A kind of automated driving system performance estimating method, device, equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101272116A (en) * | 2008-04-30 | 2008-09-24 | 山东电力研究院 | Generator excitation system modeling method |
| CN101359026A (en) * | 2008-09-17 | 2009-02-04 | 中国电力科学研究院 | Full-time-domain voltage stability synthesis assessment method |
| CN102457411A (en) * | 2011-10-14 | 2012-05-16 | 中国人民解放军国防科学技术大学 | Network security situation fuzzy evaluation method based on uncertain data |
| CN104730458A (en) * | 2015-04-01 | 2015-06-24 | 贵州电力试验研究院 | Method for monitoring state of generator excitation system |
| CN104965983A (en) * | 2015-06-25 | 2015-10-07 | 国家电网公司 | Comprehensive evaluation method for dynamic performance of excitation system |
| CN105610360A (en) * | 2016-01-22 | 2016-05-25 | 国网上海市电力公司 | Parameter identification method of synchronous generator excitation system |
-
2017
- 2017-06-28 CN CN201710504443.8A patent/CN107357978A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101272116A (en) * | 2008-04-30 | 2008-09-24 | 山东电力研究院 | Generator excitation system modeling method |
| CN101359026A (en) * | 2008-09-17 | 2009-02-04 | 中国电力科学研究院 | Full-time-domain voltage stability synthesis assessment method |
| CN102457411A (en) * | 2011-10-14 | 2012-05-16 | 中国人民解放军国防科学技术大学 | Network security situation fuzzy evaluation method based on uncertain data |
| CN104730458A (en) * | 2015-04-01 | 2015-06-24 | 贵州电力试验研究院 | Method for monitoring state of generator excitation system |
| CN104965983A (en) * | 2015-06-25 | 2015-10-07 | 国家电网公司 | Comprehensive evaluation method for dynamic performance of excitation system |
| CN105610360A (en) * | 2016-01-22 | 2016-05-25 | 国网上海市电力公司 | Parameter identification method of synchronous generator excitation system |
Non-Patent Citations (4)
| Title |
|---|
| 傅磊等: "一种发电机励磁系统性能的模糊综合评估算法", 《电机与控制应用》 * |
| 刘茂才等: "《科学学与管理科学概要》", 30 September 1982 * |
| 张虹等: "同步发电机励磁系统控制性能评估研究", 《计算机仿真》 * |
| 李梧桐等: "同步发电机励磁系统建模技术分析", 《仪器仪表与分析监测》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108681817A (en) * | 2018-05-17 | 2018-10-19 | 中电普瑞电力工程有限公司 | A kind of excitation system performance estimating method, device and storage medium |
| CN109308529A (en) * | 2018-10-11 | 2019-02-05 | 国网山东省电力公司电力科学研究院 | A kind of DCgenerator motor field function synthesized method of evaluating performance applied in net source platform |
| CN109435955A (en) * | 2018-10-22 | 2019-03-08 | 百度在线网络技术（北京）有限公司 | A kind of automated driving system performance estimating method, device, equipment and storage medium |
| CN109435955B (en) * | 2018-10-22 | 2020-08-25 | 百度在线网络技术（北京）有限公司 | Performance evaluation method, device and equipment for automatic driving system and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106505593B (en) | A kind of analysis of distribution transforming three-phase imbalance and the method for load adjustment based on big data | |
| CN101789598B (en) | Power system load modelling method | |
| CN103683277B (en) | A kind of electric power system transient stability cutting load controls validity evaluation index computational methods | |
| CN103400009B (en) | Based on the wind energy turbine set Dynamic Equivalence of the semi-supervised spectral clustering of division level | |
| CN103366096B (en) | Electric power communication device methods of risk assessment | |
| CN110070282A (en) | A kind of low-voltage platform area line loss analysis of Influential Factors method based on Synthesis Relational Grade | |
| US20110202191A1 (en) | Energy generating system and control thereof | |
| CN107358542A (en) | A kind of parameter determination method of excitation system Performance Evaluation Model | |
| CN105139095A (en) | Power distribution network running state evaluation method based on attribute area module | |
| CN107357978A (en) | A kind of synchronous generator excited system performance estimating method | |
| CN108053128A (en) | A kind of Power Network Transient Stability fast evaluation method based on ELM and TF | |
| JP2016189664A (en) | System stabilization controller and power system control system | |
| CN103033716B (en) | Calculation method of proportion of each lode component in power grid comprehensive load model | |
| EP2381094B1 (en) | Energy network and control thereof | |
| CN105375516A (en) | Online grouping equivalent modeling method for large photovoltaic power station | |
| CN103544542A (en) | Power system transient stability margin predicting method | |
| CN109784755A (en) | A kind of smart grid level evaluation method based on analytic hierarchy process (AHP) | |
| CN105006819A (en) | Black-start scheme evaluation method | |
| CN104156889A (en) | Wind farm performance evaluation system based on WAMS data and evaluation method thereof | |
| CN107784442A (en) | A kind of Assessment method of power system comprehensive benefit | |
| CN104407273A (en) | Electric energy quality disturbance source positioning method considering monitoring reliability | |
| CN104466957A (en) | Load model parameter identification method based on measured small disturbance data | |
| CN108074198A (en) | A kind of electric system PMU bad data recognition methods | |
| CN103020729A (en) | Comprehensive evaluation method for short-circuit current limitation of Gansu power grid | |
| CN107480862A (en) | A kind of Distribution Network Reliability marginal cost marginal analysis method based on big data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20171117 |