CN104331788A - Rapid severe fault set screening method for power generation system reliability evaluation. - Google Patents
Rapid severe fault set screening method for power generation system reliability evaluation. Download PDFInfo
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- CN104331788A CN104331788A CN201410722944.XA CN201410722944A CN104331788A CN 104331788 A CN104331788 A CN 104331788A CN 201410722944 A CN201410722944 A CN 201410722944A CN 104331788 A CN104331788 A CN 104331788A
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Abstract
The invention relates to a state screening method, and in particular relates to a rapid severe fault set screening method for power generation system reliability evaluation, belonging to the technical field of power. The rapid severe fault set screening method comprises the following steps: I, confirming a probability factor of a machine set; II, confirming a capacity factor of the machine set; III, confirming an importance degree factor of the machine set; IV, confirming a machine set sequence; and V, rapidly screening a severe fault state set. The rapid severe fault set screening method aims at screening fault sets which can most severely affect the reliability of a system according to the influence of double factors, namely, the capacity and the reliability of the machine set. The rapid severe fault set screening method is fast in convergence speed, short in calculation time, capable of reflecting the reliability level of the system relatively actually, and wide in prospect in power generation system reliability evaluation.
Description
Technical field
The present invention relates to a kind of system states filter method of technical field of electric power, be specially the catastrophic failure collection rapid screening method of Generation System Reliability assessment.
Background technology
The basic task of electric system is economical as far as possible and reliably electric power is supplied user.Modern power systems is constantly to high voltage, remote and Large Copacity future development, and the systemic-function caused due to random occurrence is lost, and will bring huge economy and social loss.Along with the raising of Power System Interconnection scale and complicacy, domestic and international large-scale blackout frequently occurs.The a national survey report of American Electric Power academy of sciences discloses, and is often only because electric system has a power failure the economic loss that causes entire United States economy namely up to 1040 ~ 1,640 hundred million dollars.Since 2003, U.S.A adds " 8.14 ", and significant blackouts events such as London " 8.28 " more causes countries in the world to the growing interest of large power system operational reliability.Can say that Power System Reliability has become the key factor affecting a national economy and social sustainable development and affect national security.The reliability level of real assessment electric system, and instruct traffic control to be the key issue of safeguards system reliability service on this basis.
Power System Reliability research achieves a series of success in vocabulary of terms definition, index system, model and algorithm, software and engineering etc., and Model in Reliability Evaluation of Power Systems has been successfully applied to the field such as planning, design of electricity generation system, composite power systems, distribution system and generating plant, primary wiring of transformer substation.In recent years, some progress are also achieved in the field such as reliability testing, venture analysis containing regenerative resource.
The most important thing is certainty annuity malfunction collection in Model in Reliability Evaluation of Power Systems, the main two kinds of large-scale electrical power system reliability assessment condition selecting methods adopted are State enumeration method and Monte Carlo simulation approach at present.Along with electric system popularization, the element in real system is hundreds and thousands of, due to the restriction of computing time and computational resource, can not assess all malfunctions.Therefore, State enumeration method only screens contributes large malfunction to assess to system reliability, as adopted the restriction of fault tuple, the technology such as probability, failure modes of blocking to reduce calculated amount.But in systems in practice, some high heavy faults can be larger than the probability of happening of low heavy fault, adopt cut-off fault tuple to carry out system states filter and can neglect the large and heavy malfunction of the height that consequence is serious of probability, make the reliability assessment result of system too optimistic.For this problem, Haitao Liu, Sun Yuanzhang, " taking into account the system state fast sorting technique of multi-state component model " that Cheng Lin etc. deliver on " Automation of Electric Systems " the 1st phase in 2008 proposes a kind of system state fast sorting technique taking into account multi-state component model, the derate state of all elements and shut down condition are carried out descending sort according to probability coefficent, determine based on neighboring system state the minimum state set comprising the larger state of next probability, determine the larger state of next probability with minimum calculation cost.Jia Yanbing, " the Generation System Reliability assessment based on improving quick sort " that solemn and just grade is delivered on the 6th phase " electric power network technique " in 2010 proposes alternative adjacent states and minimum adjacent states collection, substantially increases Evaluation accuracy and estimating velocity.But these quicksort methods only consider the probability coefficent of response element reliability level, carry out selective system malfunction collection according to event occurrence rate sequence.But the genset in practical power systems runs the impact of the factors such as the time limit, manufacturer, running environment, operational factor, load capacity due to it, the reliability of each unit is all different.The reliability level of usual large sized unit is higher, if only consider the reliability level of unit, the malfunction collection screened may be caused to only considered the lower little unit fault of reliability level, and have ignored the higher large-sized unit fault of reliability level, and according to practical operating experiences, large sized unit probability of malfunction is low, but it will be once fault will be far longer than little unit fault to the impact of Power System Reliability.Such as, in certain electric system, the reliability level of 100MW unit is 0.9974, and the reliability level of 1000MW unit is 0.9986, large sized unit probability of malfunction is lower than small-load generators by 0.0012, but its fault causes system removal of load amount to be at least small-load generators fault causes 10 times, if consider, system transmission line capability blocks the impact caused again, and the system that it causes loses load will be larger.Therefore, when determining malfunction collection, only can not consider the reliability level of element, also must take into account the impact of element volume on system reliability.
Summary of the invention
The present invention, in order to solve the single and problem that causes assessment errors large of existing Model in Reliability Evaluation of Power Systems method Consideration, provides the catastrophic failure collection rapid screening method of a kind of Generation System Reliability assessment.
The present invention adopts following technical scheme to realize: the catastrophic failure collection rapid screening method of Generation System Reliability assessment, comprises the following steps:
Determine the probability factor of unit: electricity generation system comprises n online unit, and wherein the stoppage in transit probability of unit j is p
dj, the operation probability of unit j is p
uj, then the probability factor of unit j is θ
j=p
dj/ p
uj,
Determine the capacity factor measure of unit: the available generating capacity of unit j is P
j, then the capacity factor measure of unit j
or
wherein P
maxfor the maximum available generating capacity of unit in electricity generation system;
Determine the importance factors of unit: according to unit j probability factor and capacity factor measure, draw the importance factors of unit j
or
or
or
a is that (each electricity generation system has a weight factor determined a) for the weight factor of electricity generation system;
Determine unit sequence: according to the importance factors of each unit in electricity generation system, sorted from big to small by unit according to importance factors, unit sequence meets
i=1 or 2 or 3 or 4;
Carry out severe fault condition collection rapid screening: choose electricity generation system initial state, according to unit sequence, selected fault unit in order, calculate probability and the importance degree of electricity generation system malfunction, determine minimum adjacent states collection, line ordering of going forward side by side, in order selected malfunction collection.
This method is on the basis of State enumeration method and quicksort method, take into account the impact of unit capacity and unit reliability double factor, propose unit importance factors, by the sequence to unit importance factors, determine electricity generation system malfunction collection, this catastrophic failure collection rapid screening method enhances counting yield, selects to assess the larger state of system reliability impact, improve Evaluation accuracy, accelerate assessment speed of convergence.
Embodiment
The catastrophic failure collection rapid screening method of Generation System Reliability assessment, comprises the following steps:
Determine the probability factor of unit: electricity generation system comprises n online unit, and wherein the stoppage in transit probability of unit j is p
dj, the operation probability of unit j is p
uj, then the probability factor of unit j is θ
j=p
dj/ p
uj,
Determine the capacity factor measure of unit: the available generating capacity of unit j is P
j, then the capacity factor measure of unit j
or
wherein P
maxfor the maximum available generating capacity of unit in electricity generation system;
Determine the importance factors of unit: according to unit j probability factor and capacity factor measure, draw the importance factors of unit j
or
or
or
a is the weight factor of electricity generation system;
Determine unit sequence: according to the importance factors of each unit in electricity generation system, sorted from big to small by unit according to importance factors, unit sequence meets
i=1 or 2 or 3 or 4;
Carry out severe fault condition collection rapid screening: choose electricity generation system initial state, according to unit sequence, selected fault unit in order, calculate probability and the importance degree of electricity generation system malfunction, determine minimum adjacent states collection, line ordering of going forward side by side, according to the selected malfunction collection of sequence.During concrete enforcement, simulation optimization algorithm is carried out for Power System Reliability test macro, and contrast adopts the method for the present invention's proposition and conventional failure screening technique to contrast, judge using the EENS value of reliability assessment as convergence effect, under the screening malfunction collection of equal number, assess the EENS value drawn larger, illustrate that the effect that malfunction collection screens is better; The EENS=15.2MW that classic method draws, adopt method of the present invention, the EENS value that four kinds of unit importance factorses draw is respectively: 15.31MW, 15.74MW, 15.90MW and 15.86MW, show that malfunction collection of the present invention screens effective thus, and this method fast convergence rate, computing time is shorter, is the catastrophic failure collection rapid screening method of very promising a kind of Generation System Reliability assessment.
Claims (1)
1. the catastrophic failure collection rapid screening method of Generation System Reliability assessment, is characterized in that comprising the following steps:
Determine the probability factor of unit: electricity generation system comprises n online unit, and wherein the stoppage in transit probability of unit j is p
dj, the operation probability of unit j is p
uj, then the probability factor of unit j is θ
j=p
dj/ p
uj,
Determine the capacity factor measure of unit: the available generating capacity of unit j is P
j, then the capacity factor measure of unit j
Or
Wherein P
maxfor the maximum available generating capacity of unit in electricity generation system;
Determine the importance factors of unit: according to unit j probability factor and capacity factor measure, draw the importance factors of unit j
or
or
or
a is the weight factor of electricity generation system;
Determine unit sequence: according to the importance factors of each unit in electricity generation system, sorted from big to small by unit according to importance factors, unit sequence meets
i=1 or 2 or 3 or 4;
Carry out severe fault condition collection rapid screening: choose electricity generation system initial state, according to unit sequence, selected fault unit in order, calculate probability and the importance degree of electricity generation system malfunction, determine minimum adjacent states collection, line ordering of going forward side by side, according to the selected malfunction collection of sequence.
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| CN201410722944.XA CN104331788B (en) | 2014-12-03 | 2014-12-03 | Rapid severe fault set screening method for power generation system reliability evaluation. |
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|---|---|---|---|
| CN201410722944.XA CN104331788B (en) | 2014-12-03 | 2014-12-03 | Rapid severe fault set screening method for power generation system reliability evaluation. |
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| CN104331788A true CN104331788A (en) | 2015-02-04 |
| CN104331788B CN104331788B (en) | 2017-05-10 |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106451441A (en) * | 2016-11-25 | 2017-02-22 | 国网浙江省电力公司电力科学研究院 | Method for screening N-k fault set of power system rapidly |
| CN109886834A (en) * | 2019-01-25 | 2019-06-14 | 重庆大学 | A kind of generating and transmitting system reliability track algorithm based on greateset risk analysis |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7105946B2 (en) * | 2001-03-27 | 2006-09-12 | Sanyo Electric Co., Ltd. | Distributed power generation system, and power supply system and power supply method utilizing the same |
| CN104036427A (en) * | 2013-03-07 | 2014-09-10 | 长沙理工大学 | Method for evaluating power supply reliability of power grid in snow disasters based on meteorological parameters |
-
2014
- 2014-12-03 CN CN201410722944.XA patent/CN104331788B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7105946B2 (en) * | 2001-03-27 | 2006-09-12 | Sanyo Electric Co., Ltd. | Distributed power generation system, and power supply system and power supply method utilizing the same |
| CN104036427A (en) * | 2013-03-07 | 2014-09-10 | 长沙理工大学 | Method for evaluating power supply reliability of power grid in snow disasters based on meteorological parameters |
Non-Patent Citations (1)
| Title |
|---|
| 贾燕冰 等: "基于改进快速排序法的发电***可靠性评估", 《电网技术》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106451441A (en) * | 2016-11-25 | 2017-02-22 | 国网浙江省电力公司电力科学研究院 | Method for screening N-k fault set of power system rapidly |
| CN106451441B (en) * | 2016-11-25 | 2019-02-05 | 国网浙江省电力公司电力科学研究院 | A kind of rapid screening method of electric system N-k fault set |
| CN109886834A (en) * | 2019-01-25 | 2019-06-14 | 重庆大学 | A kind of generating and transmitting system reliability track algorithm based on greateset risk analysis |
| CN109886834B (en) * | 2019-01-25 | 2023-01-10 | 重庆大学 | Power generation and transmission system reliability tracking algorithm based on maximum risk analysis |
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