CN103995921A - Method for simulating and assessing micro-grid power supply system reliability - Google Patents
Method for simulating and assessing micro-grid power supply system reliability Download PDFInfo
- Publication number
- CN103995921A CN103995921A CN201410163286.5A CN201410163286A CN103995921A CN 103995921 A CN103995921 A CN 103995921A CN 201410163286 A CN201410163286 A CN 201410163286A CN 103995921 A CN103995921 A CN 103995921A
- Authority
- CN
- China
- Prior art keywords
- time
- year
- formula
- probability
- ttf
- 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.)
- Granted
Links
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for simulating and assessing micro-grid power supply system reliability. The method includes the three steps that (S1) an assessment model is determined, (S2) element state sampling is carried out, and (S3) system assessment is carried out. Historical operation data of various kinds of equipment serve as bases, distributed power supply fault points and fault time sequences are led in for analysis of system power supply reliability, the operation state of system distributed power supply equipment is simulated, influences of performance faults of the equipment on system power supply reliability are fully taken into consideration, and the analysis result can reflect power supply reliability of a micro-grid system truly.
Description
Technical field
The present invention relates to a kind of simulation evaluation method of microgrid electric power system reliability.
Technical background
Microgrid power supply technique develops rapidly at present, but less to the research by microgrid electric power system reliability aspect.In the ripe software of existing microgrid electric power system emulation, generally to use the concept of load satisfaction to weigh the reliability that micro-grid system is powered, it is mainly by the request for utilization of the configuration of microgrid and microgrid engineering, to the satisfaction of loading of the ratio calculation micro-grid system of section system power supply total amount and system load demand total amount sometime, it has the following disadvantages: only with meeting load (Unmet electric load), do not weigh the reliability that micro-grid system is powered, as homer software, this software has only been considered the deficiency of load power supply, do not relate to the impact of burning natural gas distributed power apparatus fault on system power supply reliability, its result is accurate not.
Summary of the invention
Technical matters to be solved by this invention, just be to provide a kind of simulation evaluation method of microgrid electric power system reliability, the method is usingd the history data of all kinds of burning natural gas distributed power apparatus of microgrid electric power system as basis, running status to each burning natural gas distributed power apparatus is simulated, taken into full account each impact of burning natural gas distributed power apparatus faults itself on microgrid electric power system power supply reliability, the result obtaining is more accurate.
Solve the problems of the technologies described above, the technical solution used in the present invention is:
A simulation evaluation method for microgrid electric power system reliability, comprises the following steps:
S1 determines assessment models;
The sampling of S2 element state;
S3 carries out system evaluation;
Described step S1 comprises following sub-step:
S1-1 sets up blower fan model
In formula, P
wfor exerting oneself in real time of blower fan, unit is kW, and parameter A, B, C are the coefficient of polynomial fitting of blower fan power curve non-linear partial, SW
tbe the real-time wind speed of t hour, unit is m/s, V
cifor starting wind speed, V
rfor wind rating, V
cofor excision wind speed;
SW wherein
tthe sequence of exerting oneself in real time adopt autoregressive moving average (ARMA) model to produce:
SW
t=μ
t+σ
ty
t; (2)
y
t=φ
1y
t-1+φ
2y
t-2+…φ
ny
t-n+α
t-α
t-1θ
1-α
t-2θ
2-…-α
t-mθ
m; (3)
μ
tfor the historical mean wind speed in somewhere, σ
tfor the standard deviation of wind speed profile, y
tfor time series, φ
i(i=1 ... n) be autoregressive coefficient, θ
j(j=1 ... m) be running mean coefficient, α
tfor white noise coefficient, obey average and be 0, variance is
independent normal distribution;
By formula (1), (2), (3), obtain horal air speed value in the whole year, and then try to achieve in the whole year the horal blower fan sequence of exerting oneself in real time;
S1-2 sets up photovoltage model
In formula, P
bfor exerting oneself in real time of photovoltaic, unit is kW; P
snfor the rated power of photovoltaic, be illustrated in the power that under standard test condition, unit light intensity can produce; G
stdfor specified intensity of illumination, unit is kW/m
2; R
cfor a certain particular light intensity, under this intensity of illumination, photovoltaic is exerted oneself and is started from the non-linear linearity that becomes with the relation of intensity of illumination; G
btbe the real-time lighting intensity of t hour, unit is kW/m
2;
G
btreal-time lighting intensity by the sampling of the probability distribution statistical of historical light intensity is obtained;
S1-3 sets up battery model
In formula, Δ W
tfor t outside charge/discharge electricity amount of accumulator in the period, it equals to discharge and recharge the product of power and period t; B
tfor discharging and recharging the residual capacity of front accumulator, B
t=B
norm* Soc (t), wherein B
normfor battery rating, Soc (t) is the state-of-charge before discharging and recharging, B
t+1for discharging and recharging the residual capacity that finishes rear accumulator; B
min, B
maxbe respectively maximum, the minimum capacity of accumulator;
S1-4 sets up load model
L
t=L
p×P
w×P
d×P
h(t);
In formula, L
pfor year load peak, P
wfor the value in year-all load curves corresponding with t hour, P
dfor the value in the week-daily load curve corresponding with t hour, P
h(t) be the value in the day-hour load curve corresponding with t hour; Load value when Lt is t;
Described step S2 comprises following sub-step:
S2-1 asks the front average operating time of component failure and mean repair time
In micro-grid system, most elements are repairable elements, and the cyclic process of " operation-stop transport-operation " that its state variation situation can be by stable state is simulated, and the parameter of element meets following relational expression:
In formula, MTTF is average operating time before component failure, the mean repair time that MTTR is element, is respectively the average of TTF and TTR; λ is crash rate, i.e. in Failure count/year, μ is repair rate, repairs number of times/year, and TTF is working time before losing efficacy, i.e. time between failures, and TTR is repair time, i.e. the out-of-service time;
S2-2 asks the time of front working time of the inefficacy of element and reparation
When micro-grid system is carried out to fail-safe analysis, it has been generally acknowledged that the next fault of element and fault are last time irrelevant, element fault has without memory; Therefore, think the equal obeys index distribution of time of working time and reparation before the inefficacy of element, its probability density function is:
In formula, f (t) represents the probability that element breaks down constantly at t; G (t) represents the probability that element has been repaired constantly at t; F (t) with the probability distribution function of g (t) is:
In formula, F (t) represents that element fault moment is less than the probability of t; G (t) represents that element reparation is less than the probability of t constantly;
Above formula is slightly done to change:
Wherein, F ' (t) represents the probability that the time between failures of element is t; G ' (t) represents the probability that repair time of element is t;
So, F ' (t) and G ' (t) be the number in interval [0,1]; Therefore, be positioned at the mode of the random number between [0,1] by generation, conversely the time between failures of sampling element and repair time, its sampling formula is:
In formula, R
1, R
2be equally distributed random number between [0,1]; Adopt said method, to TTF and TTR difference alternate sampling;
Described step S3 comprises following sub-step:
S3-1 initialization simulated clock simulation clock is 0, produces at random m the random number between 0-1, according to the crash rate parameter lambda in each element state model try to achieve m non-failure operation time TTF, TTF
ithe TTF that represents i element;
S3-2 finds out minimum TTF
i;
S3-3 produces a random number to i element, and according to its repair rate parameter, μ tries to achieve fault correction time TTR
i;
S3-4 reads FMEA table, the load point affecting while searching element i fault, record these dead electricity load point frequency of power cut, power off time, lack the information such as delivery.
S3-5 produces a new random number, is translated into the running time T TF that element i is new
i';
S3-6 judges that whether simulated clock simulation clock is across year, not across the load point power failure information of record being added in year then in load point reliability index; If across year, adopt across year formula, calculate load point reliability index and the Reliability Index of this year;
Judging whether simulated clock simulation clock has been advanced to meets the required time span of Evaluation accuracy, if reach, performs step S3-7, does not reach and returns to step S3-2;
S3-7 simulation process finishes, and adds up the load point in each simulation year and the reliability index of system;
The reliability index average of S3-8 and then calculating whole system.
Beneficial effect: the present invention uses the history data of various kinds of equipment as basis, introduce distributed power source trouble spot, fault-time sequence and carry out the analysis of system power supply reliability, running status to system burning natural gas distributed power apparatus is simulated, taken into full account the impact of breaking down on system power supply reliability because of equipment self performance, its analysis result can reflect the power supply reliability of micro-grid system more really.
Accompanying drawing explanation
The outage model of Fig. 1 repairable elements;
The state variation cyclic process figure of Fig. 2 repairable elements;
The process flow diagram of Fig. 3 microgrid electric power system reliability assessment.
Embodiment
Referring to Fig. 1 to Fig. 3.
The simulation evaluation method embodiment of microgrid electric power system reliability of the present invention, comprises the following steps:
S1 determines assessment models;
The sampling of S2 element state;
S3 carries out system evaluation;
Described step S1 comprises following sub-step:
S1-1 sets up blower fan model
In formula, P
wfor exerting oneself in real time of blower fan, unit is kW, and parameter A, B, C are the coefficient of polynomial fitting of blower fan power curve non-linear partial, SW
tbe the real-time wind speed of t hour, unit is m/s, V
cifor starting wind speed, V
rfor wind rating, V
cofor excision wind speed;
SW wherein
tthe sequence of exerting oneself in real time adopt autoregressive moving average (ARMA) model to produce:
SW
t=μ
t+σ
ty
t; (2)
y
t=φ
1y
t-1+φ
2y
t-2+…φ
ny
t-n+α
t-α
t-1θ
1-α
t-2θ
2-…-α
t-mθ
m; (3)
μ
tfor the historical mean wind speed in somewhere, σ
tfor the standard deviation of wind speed profile, y
tfor time series, φ
i(i=1 ... n) be autoregressive coefficient, θ
j(j=1 ... m) be running mean coefficient, α
tfor white noise coefficient, obey average and be 0, variance is
independent normal distribution;
These parameters can obtain by this area's historical wind speed statistics above; By formula (1), (2), (3), can obtain horal air speed value in the whole year, and then try to achieve in the whole year the horal blower fan sequence of exerting oneself in real time;
S1-2 sets up photovoltage model
In formula, P
bfor exerting oneself in real time of photovoltaic, unit is kW; P
snfor the rated power of photovoltaic, be illustrated in the power that under standard test condition, unit light intensity can produce; G
stdfor specified intensity of illumination, unit is kW/m
2; R
cfor a certain particular light intensity, under this intensity of illumination, photovoltaic is exerted oneself and is started from the non-linear linearity that becomes with the relation of intensity of illumination; G
btbe the real-time lighting intensity of t hour, unit is kW/m
2;
G
btreal-time lighting intensity can be by the sampling of the probability distribution statistical of historical light intensity be obtained;
S1-3 sets up battery model
In formula, Δ W
tfor t outside charge/discharge electricity amount (discharging and recharging the product of power and period t) of accumulator in the period; B
tfor discharging and recharging the residual capacity of front accumulator, B
t=B
norm* Soc (t), wherein B
normfor battery rating, Soc (t) is the state-of-charge before discharging and recharging, B
t+1for discharging and recharging the residual capacity that finishes rear accumulator; B
min, B
maxbe respectively maximum, the minimum capacity of accumulator;
S1-4 sets up load model
L
t=L
p×P
w×P
d×P
h(t);
In formula, L
pfor year load peak, P
wfor the value in year-all load curves corresponding with t hour, P
dfor the value in the week-daily load curve corresponding with t hour, P
h(t) be the value in the day-hour load curve corresponding with t hour; Load value when Lt is t.
Described step S2 comprises following sub-step:
S2-1 asks the front average operating time of component failure and mean repair time
In micro-grid system, most elements are repairable elements, and the cyclic process of " operation-stop transport-operation " that its state variation situation can be by stable state is simulated, as shown in Figure 2; These parameters meet following relational expression:
In formula, MTTF is average operating time before component failure, the mean repair time that MTTR is element, is respectively the average of TTF and TTR; λ is crash rate (Failure count/year), and μ is repair rate (repairing number of times/year), and TTF is working time (time between failures) before losing efficacy, and TTR is repair time (out-of-service time);
S2-2 asks the time of front working time of the inefficacy of element and reparation
When micro-grid system is carried out to fail-safe analysis, it has been generally acknowledged that the next fault of element and fault are last time irrelevant, element fault has without memory; Therefore, can think the equal obeys index distribution of time of working time and reparation before the inefficacy of element, its probability density function is:
In formula, f (t) represents the probability that element breaks down constantly at t; G (t) represents the probability that element has been repaired constantly at t; F (t) with the probability distribution function of g (t) is:
In formula, F (t) represents that element fault moment is less than the probability of t; G (t) represents that element reparation is less than the probability of t constantly;
Above formula is slightly done to change, can obtain:
Wherein, F ' (t) represents the probability that the time between failures of element is t; G ' (t) represents the probability that repair time of element is t;
Can find out, F ' (t) and G ' (t) be the number in interval [0,1]; Therefore, can be positioned at by generation the mode of the random number between [0,1], conversely the time between failures of sampling element and repair time, its sampling formula is:
In formula, R
1, R
2be equally distributed random number between [0,1]; Adopt said method, to TTF and TTR difference alternate sampling;
Described step S3 comprises following sub-step:
S3-1 initialization simulated clock simulation clock is 0, produces at random m the random number between 0-1, according to the crash rate parameter lambda in each element state model try to achieve m non-failure operation time TTF, TTF
ithe TTF that represents i element;
S3-2 finds out minimum TTF
i;
S3-3 produces a random number to i element, and according to its repair rate parameter, μ tries to achieve fault correction time TTR
i;
S3-4 reads FMEA table, the load point affecting while searching element i fault, record these dead electricity load point frequency of power cut, power off time, lack the information such as delivery.
S3-5 produces a new random number, is translated into the running time T TFi' that element i is new;
S3-6 judges that whether simulated clock simulation clock is across year, not across the load point power failure information of record being added in year then in load point reliability index; If across year, adopt across year formula, calculate load point reliability index and the Reliability Index of this year;
Judging whether simulated clock simulation clock has been advanced to meets the required time span of Evaluation accuracy, if reach, performs step S3-7, does not reach and returns to step S3-2;
S3-7 simulation process finishes, and adds up the load point in each simulation year and the reliability index of system;
The reliability index average of S3-8 and then calculating whole system.
Claims (3)
1. a simulation evaluation method for microgrid electric power system reliability, is characterized in that comprising the following steps:
S1 determines assessment models;
The sampling of S2 element state;
S3 carries out system evaluation;
Described step S1 comprises following sub-step:
S1-1 sets up blower fan model
In formula, P
wfor exerting oneself in real time of blower fan, unit is kW, and parameter A, B, C are the coefficient of polynomial fitting of blower fan power curve non-linear partial, SW
tbe the real-time wind speed of t hour, unit is m/s, V
cifor starting wind speed, V
rfor wind rating, V
cofor excision wind speed;
SW wherein
tthe sequence of exerting oneself in real time adopt autoregressive moving-average model to produce:
SW
t=μ
t+σ
ty
t; (2)
y
t=φ
1y
t-1+φ
2y
t-2+…φ
ny
t-n+α
t-α
t-1θ
1-α
t-2θ
2-…-α
t-mθ
m; (3)
μ
tfor the historical mean wind speed in somewhere, σ
tfor the standard deviation of wind speed profile, y
tfor time series, φ
i(i=1 ... n) be autoregressive coefficient, θ
j(j=1 ... m) be running mean coefficient, α
tfor white noise coefficient, obey average and be 0, variance is
independent normal distribution;
By formula (1), (2), (3), obtain horal air speed value in the whole year, and then try to achieve in the whole year the horal blower fan sequence of exerting oneself in real time;
S1-2 sets up photovoltage model
In formula, P
bfor exerting oneself in real time of photovoltaic, unit is kW; P
snfor the rated power of photovoltaic, be illustrated in the power that under standard test condition, unit light intensity can produce; G
stdfor specified intensity of illumination, unit is kW/m
2; R
cfor a certain particular light intensity, under this intensity of illumination, photovoltaic is exerted oneself and is started from the non-linear linearity that becomes with the relation of intensity of illumination; G
btbe the real-time lighting intensity of t hour, unit is kW/m
2;
G
btreal-time lighting intensity by the sampling of the probability distribution statistical of historical light intensity is obtained;
S1-3 sets up battery model
In formula, Δ W
tfor t outside charge/discharge electricity amount of accumulator in the period, it equals to discharge and recharge the product of power and period t; B
tfor discharging and recharging the residual capacity of front accumulator, B
t=B
norm* Soc (t), wherein B
normfor battery rating, Soc (t) is the state-of-charge before discharging and recharging, B
t+1for discharging and recharging the residual capacity that finishes rear accumulator; B
min, B
maxbe respectively maximum, the minimum capacity of accumulator;
S1-4 sets up load model
L
t=L
p×P
w×P
d×P
h(t);
In formula, L
pfor year load peak, P
wfor the value in year-all load curves corresponding with t hour, P
dfor the value in the week-daily load curve corresponding with t hour, P
h(t) be the value in the day-hour load curve corresponding with t hour; Load value when Lt is t.
2. the simulation evaluation method of microgrid electric power system reliability according to claim 1, is characterized in that: described step S2 comprises following sub-step:
S2-1 asks the front average operating time of component failure and mean repair time
In micro-grid system, most elements are repairable elements, and the cyclic process of " operation-stop transport-operation " that its state variation situation can be by stable state is simulated, and the parameter of element meets following relational expression:
In formula, MTTF is average operating time before component failure, the mean repair time that MTTR is element, is respectively the average of TTF and TTR; λ is crash rate, i.e. in Failure count/year, μ is repair rate, repairs number of times/year, and TTF is working time before losing efficacy, i.e. time between failures, and TTR is repair time, i.e. the out-of-service time;
S2-2 asks the time of front working time of the inefficacy of element and reparation
When micro-grid system is carried out to fail-safe analysis, it has been generally acknowledged that the next fault of element and fault are last time irrelevant, element fault has without memory; Therefore, think the equal obeys index distribution of time of working time and reparation before the inefficacy of element, its probability density function is:
In formula, f (t) represents the probability that element breaks down constantly at t; G (t) represents the probability that element has been repaired constantly at t; F (t) with the probability distribution function of g (t) is:
In formula, F (t) represents that element fault moment is less than the probability of t; G (t) represents that element reparation is less than the probability of t constantly;
Above formula is slightly done to change:
Wherein, F ' (t) represents the probability that the time between failures of element is t; G ' (t) represents the probability that repair time of element is t;
So, F ' (t) and G ' (t) be the number in interval [0,1]; Therefore, be positioned at the mode of the random number between [0,1] by generation, conversely the time between failures of sampling element and repair time, its sampling formula is:
In formula, R
1, R
2be equally distributed random number between [0,1]; Adopt said method, to TTF and TTR difference alternate sampling.
3. the simulation evaluation method of microgrid electric power system reliability according to claim 2, is characterized in that: described step S3 comprises following sub-step:
S3-1 initialization simulated clock simulation clock is 0, produces at random m the random number between 0-1, according to the crash rate parameter lambda in each element state model try to achieve m non-failure operation time TTF, TTF
ithe TTF that represents i element;
S3-2 finds out minimum TTF
i;
S3-3 produces a random number to i element, and according to its repair rate parameter, μ tries to achieve fault correction time TTR
i;
S3-4 reads FMEA table, the load point affecting while searching element i fault, record these dead electricity load point frequency of power cut, power off time, lack the information such as delivery;
S3-5 produces a new random number, is translated into the running time T TF that element i is new
i';
S3-6 judges that whether simulated clock simulation clock is across year, not across the load point power failure information of record being added in year then in load point reliability index; If across year, adopt across year formula, calculate load point reliability index and the Reliability Index of this year;
Judging whether simulated clock simulation clock has been advanced to meets the required time span of Evaluation accuracy, if reach, performs step S3-7, does not reach and returns to step S3-2;
S3-7 simulation process finishes, and adds up the load point in each simulation year and the reliability index of system; The reliability index average of S3-8 and then calculating whole system.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410163286.5A CN103995921B (en) | 2014-04-22 | 2014-04-22 | A kind of simulation evaluation method of microgrid electric power system reliability |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410163286.5A CN103995921B (en) | 2014-04-22 | 2014-04-22 | A kind of simulation evaluation method of microgrid electric power system reliability |
Publications (2)
Publication Number | Publication Date |
---|---|
CN103995921A true CN103995921A (en) | 2014-08-20 |
CN103995921B CN103995921B (en) | 2017-08-08 |
Family
ID=51310086
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201410163286.5A Active CN103995921B (en) | 2014-04-22 | 2014-04-22 | A kind of simulation evaluation method of microgrid electric power system reliability |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN103995921B (en) |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104504618A (en) * | 2014-12-29 | 2015-04-08 | 天津大学 | Micro-grid reliability evaluation data sampling method based on pair-copula function |
CN104698346A (en) * | 2015-03-31 | 2015-06-10 | 国网内蒙古东部电力有限公司通辽供电公司 | Method and device for analyzing faults of source-containing power distribution network |
CN107591833A (en) * | 2016-07-08 | 2018-01-16 | 华北电力大学(保定) | A kind of microgrid reliability estimation method of meter and different operation reserves |
CN107611966A (en) * | 2017-09-20 | 2018-01-19 | 天津大学 | A kind of active power distribution network evaluation of power supply capability method for considering difference reliability |
CN109447847A (en) * | 2018-12-24 | 2019-03-08 | 天津天电清源科技有限公司 | A kind of active power distribution network Reliability Estimation Method containing flexible Sofe Switch |
CN109884537A (en) * | 2018-12-05 | 2019-06-14 | 珠海许继电气有限公司 | A kind of Intelligent power distribution terminal backup battery state evaluating method and system |
CN110263446A (en) * | 2019-06-24 | 2019-09-20 | 广东工业大学 | A kind of production line reliability improvement analysis method, system based on improvement FMEA |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102682407A (en) * | 2012-04-06 | 2012-09-19 | 广东电网公司电力科学研究院 | Comprehensive reliability assessment method for 500kV terminal substation |
CN103093396A (en) * | 2013-01-29 | 2013-05-08 | 广东电网公司电力调度控制中心 | Method and system for assessing power grid panel point reliability |
CN103606969A (en) * | 2013-12-03 | 2014-02-26 | 国家电网公司 | Method for optimizing and dispatching sea island microgrid with new energy and sea water desalination loads |
CN103699805A (en) * | 2013-12-31 | 2014-04-02 | 国家电网公司 | Method of assessing reliability of micro-grid in isolated island operation state |
-
2014
- 2014-04-22 CN CN201410163286.5A patent/CN103995921B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102682407A (en) * | 2012-04-06 | 2012-09-19 | 广东电网公司电力科学研究院 | Comprehensive reliability assessment method for 500kV terminal substation |
CN103093396A (en) * | 2013-01-29 | 2013-05-08 | 广东电网公司电力调度控制中心 | Method and system for assessing power grid panel point reliability |
CN103606969A (en) * | 2013-12-03 | 2014-02-26 | 国家电网公司 | Method for optimizing and dispatching sea island microgrid with new energy and sea water desalination loads |
CN103699805A (en) * | 2013-12-31 | 2014-04-02 | 国家电网公司 | Method of assessing reliability of micro-grid in isolated island operation state |
Non-Patent Citations (1)
Title |
---|
李登峰: ""并网型微网电源容量优化配置模型及算法研究"", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104504618A (en) * | 2014-12-29 | 2015-04-08 | 天津大学 | Micro-grid reliability evaluation data sampling method based on pair-copula function |
CN104504618B (en) * | 2014-12-29 | 2017-12-15 | 天津大学 | Micro-capacitance sensor reliability assessment sampling of data method based on pair copula functions |
CN104698346A (en) * | 2015-03-31 | 2015-06-10 | 国网内蒙古东部电力有限公司通辽供电公司 | Method and device for analyzing faults of source-containing power distribution network |
CN107591833A (en) * | 2016-07-08 | 2018-01-16 | 华北电力大学(保定) | A kind of microgrid reliability estimation method of meter and different operation reserves |
CN107611966A (en) * | 2017-09-20 | 2018-01-19 | 天津大学 | A kind of active power distribution network evaluation of power supply capability method for considering difference reliability |
CN107611966B (en) * | 2017-09-20 | 2020-12-11 | 天津大学 | Active power distribution network power supply capacity evaluation method considering difference reliability |
CN109884537A (en) * | 2018-12-05 | 2019-06-14 | 珠海许继电气有限公司 | A kind of Intelligent power distribution terminal backup battery state evaluating method and system |
CN109447847A (en) * | 2018-12-24 | 2019-03-08 | 天津天电清源科技有限公司 | A kind of active power distribution network Reliability Estimation Method containing flexible Sofe Switch |
CN110263446A (en) * | 2019-06-24 | 2019-09-20 | 广东工业大学 | A kind of production line reliability improvement analysis method, system based on improvement FMEA |
Also Published As
Publication number | Publication date |
---|---|
CN103995921B (en) | 2017-08-08 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN103995921A (en) | Method for simulating and assessing micro-grid power supply system reliability | |
Dubarry et al. | Battery Energy Storage System battery durability and reliability under electric utility grid operations: Analysis of 3 years of real usage | |
CN104376504B (en) | A kind of distribution system probabilistic reliability appraisal procedure based on analytic method | |
CN100573166C (en) | Monitoring and prewarning method for power source internal resistance for vehicle and device thereof | |
CN104851053A (en) | Wind-photovoltaic-energy-storage-contained method for power supply reliability evaluation method of distribution network | |
Billinton | Impacts of energy storage on power system reliability performance | |
CN104156892A (en) | Active distribution network voltage drop simulation and evaluation method | |
CN103105585A (en) | Charge-discharge full-time online testing method for performance of storage battery | |
CN109001636B (en) | Method and device for determining battery health degree of battery pack, vehicle and computing equipment | |
CN107506331B (en) | Micro-grid reliability calculation method based on time correlation and element running time | |
CN112147530A (en) | Battery state evaluation method and device | |
CN104217113A (en) | Reliability evaluation method of independent wind and light storage system based on energy storage probability model | |
Rancilio et al. | BESS modeling: investigating the role of auxiliary system consumption in efficiency derating | |
CN102938024A (en) | Wind turbine generator unit state monitoring system performance assessment method | |
Hanna | Optimal investment in microgrids to mitigate power outages from public safety power shutoffs | |
CN111382518A (en) | Confidence capacity evaluation method of wind storage combined system | |
JP4401734B2 (en) | Secondary battery internal resistance detection method, internal resistance detection device, internal resistance detection program, and medium containing the program | |
CN103699805A (en) | Method of assessing reliability of micro-grid in isolated island operation state | |
Hutchinson et al. | Verification and analysis of a Battery Energy Storage System model | |
KR20180006264A (en) | Simulation apparatus and method of battery | |
KR101105458B1 (en) | Fault detection apparatus and method for photovoltaic power generation system | |
CN105610151A (en) | Extra-high voltage direct-current restart simulation optimization method | |
CN106410790B (en) | A kind of micro-capacitance sensor reliability estimation method that service condition is interdependent | |
CN116432978A (en) | Method for calculating power supply reliability index of highway self-consistent energy system | |
CN105866705A (en) | Measuring method for capacity of backup power source of alternating current variable pitch system of wind generating set |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CP01 | Change in the name or title of a patent holder |
Address after: 510080 water Donggang 8, Dongfeng East Road, Yuexiu District, Guangzhou, Guangdong. Patentee after: ELECTRIC POWER RESEARCH INSTITUTE, GUANGDONG POWER GRID CO., LTD. Address before: 510080 water Donggang 8, Dongfeng East Road, Yuexiu District, Guangzhou, Guangdong. Patentee before: Electrical Power Research Institute of Guangdong Power Grid Corporation |
|
CP01 | Change in the name or title of a patent holder |