CN105552880A - Electric power system typical fault set determination method based on state enumeration method - Google Patents

Electric power system typical fault set determination method based on state enumeration method Download PDF

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CN105552880A
CN105552880A CN201510892153.6A CN201510892153A CN105552880A CN 105552880 A CN105552880 A CN 105552880A CN 201510892153 A CN201510892153 A CN 201510892153A CN 105552880 A CN105552880 A CN 105552880A
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electric power
power system
load
cluster
formula
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谢开贵
贺海磊
钟隽
周勤勇
胡博
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STATE GRID JIANGXI ELECTRIC POWER Co
Chongqing University
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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Chongqing University
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention provides an electric power system typical fault set determination method based on a state enumeration method. The method includes the steps of: S1. performing clustering of electric power system node load and generated output data according to a Kmeans clustering method, thereby determining typical operation modes of an electric power system; S2. enumerating and generating a fault-free working state and a fault working state of each component, thereby obtaining the states of the whole system and probabilities of the states; and S3. sorting the system states based on performance indexes. The method considers different operation modes in the electric power system, overcomes the defect of not considering state fault probability risks in the prior art, is high in efficiency of screening typical faults of the electric power system, and can provide a beneficial reference for power grid planning.

Description

Based on the electric power system typical fault set defining method of State enumeration method
Technical field
The present invention relates to power system planning technical field, be specifically related to a kind of electric power system typical fault set defining method based on State enumeration method.
Background technology
Electric power is the basic energy resource industry involved the interests of the state and the people in Chinese national economy development, and electric power is to promoting that China's economic growth and social progress have extremely crucial effect.Eleventh Five-Year Plan period, China's in-depth power system reform, carry out the separation of government functions from enterprise management further, separate the factory and network, generation bidding is surfed the Net.Along with opening gradually of China's network system, the operation of electrical network also presents some new features: the generation of distributed power generation causes many users can select to install distributed power generation to carry out electric energy supply, thus the demand of load, power supply exert oneself and Power Exchange between interconnected systems all makes the uncertainty of operation of power networks increase to some extent.Along with the day by day exhaustion of global non-renewable resources, a lot of country is all selected exploitation regenerative resource as Major Strategic.In order to balance energy security and problem of environmental pollution, China selects to greatly develop the wind-powered electricity generation that development course in renewable energy utilization is the longest, technology is also ripe, determine and be developed as auxiliary Wind Power Development route based on concentrated scale development, distributing, and planning in Gansu, Jilin, Jiangsu, Hebei, Xinjiang, the Inner Mongol, the several provinces and cities in Shandong set up 8 ten million multikilowatt wind power base.Wind-force by the far-reaching resource of natural weather condition, himself just has larger uncertain and intermittent as one.The stable operation of large-scale grid connection to China's electrical network of the wind-powered electricity generation of weak controllability, strong randomness proposes new challenge.Meanwhile, the unexpected change of electric load, the enchancement factor such as impact, operating personnel's misoperation, protective device misoperation, original paper fault of exceedingly odious weather bring hidden danger also to the safe and stable operation of electrical network.Domestic and foreign experience shows, the extremely strong massive blackout accident of much harmfulness all causes a certain equipment fault by enchancement factor, brings impact and do not control in time thus occur caused by extensive chain reaction to system.Therefore in today that random factor day by day increases, be badly in need of setting up the risk that electric network fault screening technique that a kind of background therewith adapts controls and reduces electric power system design and exist in running.
But the following a few class of main existence is not enough in existing power system planning technology: one, power system operation mode is divided into four kinds of modes, does not consider the probability that various mode occurs; Two, overall risk is not considered in electric power system fault screening; Three, the shape probability of state that electric power system is broken down lacks quantification;
Summary of the invention
The application, by providing a kind of Circuits System typical fault diversity method based on State enumeration method, can take into account the factor such as typical operation modes in electric power system, system risk level, to solve in prior art the technical problem not considering status fault probability risk.
For solving the problems of the technologies described above, the application is achieved by the following technical solutions:
Based on an electric power system typical fault set defining method for State enumeration method, comprise the following steps:
S1: according to kmeans clustering method, cluster is carried out to electric power system node load, generated output data, determine the typical operation modes of electric power system;
S2: according to the failure rate of each element in electrical network, enumerates the no-failure operation state and fail operation state that generate each element, obtains the state of whole system and corresponding probability thereof thus;
S3: system mode is sorted based on performance index.
Further, step S1 specifically comprises:
S11: according to sequential load curve, determines the load power L of each node of electric power system;
S12: according to generator output characteristic, determines the generated output G of each node of electric power system;
S13: i-th cluster average M of setting jth bar load curve ijinitial value, wherein, the span of cluster i is i=1,2 ..., the span of NL, load curve j is j=1,2 ..., NC;
S14: according to formula calculate Euler's distance, in formula, D kibe Euler's distance of a kth load point to a i-th cluster average, j is load curve, and NC is load curve sum, L kjthe load power of a kth load point in load curve j, G kjit is the generated output value of a kth load point in load curve j;
S15: load point is assigned to nearest cluster, it is organized into groups again, according to upgrade cluster average, in formula, the load power of a kth load point in i-th cluster of jth bar load curve, N iit is the load point number in i-th cluster;
S16: repeat step S14 and step S15, until whole cluster average M ijtill remaining unchanged in iteration;
S17: use the cluster average M after convergence ijas the load level of each cluster of load curve each in multi-class workload model, meanwhile, the average of corresponding generated output classification is multistage generated output level.
Further, step S2 specifically comprises:
S21: the unavailability ratio U determining each element in electric power system, in formula, λ is the failure rate of element, and μ is the repair rate of element;
S22: according to the unavailability ratio of each element, determines the probability P of each malfunction of electric power system c, in formula, U lbe the stoppage in transit probability of l element, n is component population order in system, n dbe component number of stopping transport in forecast failure event, if only consider branch road, n is number of branches, if consider branch road and generator, n is the total number of branch road and generator, for single element fault, and n dequal 1.
Further, step S3 specifically comprises:
S31: based on branch power index, each state of system is sorted, based on branch power index be: in formula, S ithe apparent power of branch road i, S i maxthe apparent power limit value of branch road i, w sibe the weight factor of branch road i, NL is number of branches in system, m sbranch power index PI sintegral indices;
S32: based on probability risk index, each state of system is sorted, based on probability risk index be: in formula, P cit is each malfunction probability of electric power system.
Further, according to generator output characteristic in described step S12, determine that the step of the power output P (v) of Wind turbines is:
First, based on the historical data of wind speed, adopt Weibull distribution model matching actual wind speed probability distribution, the cumulative distribution function of wind speed is: the probability density function of wind speed is: f (v)=k/c (v/c) k-1, in formula, v is wind speed, and k is the form parameter of Weibull distribution, and c is the scale parameter of Weibull distribution;
Then, set up Wind turbines sequential according to following formula to exert oneself model:
in formula, v cifor the incision wind speed of Wind turbines, v rfor rated wind speed, v cofor cut-out wind speed, P rfor the rated power of Wind turbines, A, B, C are parameter type, and A = 1 ( V c i - V r ) 2 [ V c i ( V c i + V r ) - 4 V c i V r ( V c i + V r 2 V r ) 3 ] B = 1 ( V c i - V r ) 2 [ 4 ( V c i + V r ) ( V c i + V r 2 V r ) 3 - ( 3 V c i + V r ) ] C = 1 ( V c i - V r ) 2 [ 2 - 4 ( V c i + V r 2 V r ) 3 ] .
Compared with prior art, the technical scheme that the application provides, the technique effect had or advantage are: the screening effeciency of this method to electric power system typical fault is high, can be Electric Power Network Planning and provide beneficial reference.
Accompanying drawing explanation
Fig. 1 is method flow diagram of the present invention;
Fig. 2 is cluster result schematic diagram;
Fig. 3 is IEEE reliability test system network topological diagram.
Embodiment
The embodiment of the present application is by providing a kind of Circuits System typical fault diversity method based on State enumeration method, the factor such as typical operation modes in electric power system, system risk level can be taken into account, to solve in prior art the technical problem not considering status fault probability risk.
In order to better understand technique scheme, below in conjunction with Figure of description and concrete execution mode, technique scheme is described in detail.
Embodiment
As shown in Figure 1, a kind of electric power system typical fault set defining method based on State enumeration method, comprises the following steps:
S1: according to kmeans clustering method, cluster is carried out to electric power system node load, generated output data, determine the typical operation modes of electric power system;
S2: according to the failure rate of each element in electrical network, enumerates the no-failure operation state and fail operation state that generate each element, obtains the state of whole system and corresponding probability thereof thus;
S3: system mode is sorted based on performance index.
Figure 2 shows that the result after cluster.
Further, step S1 specifically comprises:
S11: according to sequential load curve, determines the load power L of each node of electric power system;
S12: according to generator output characteristic, determines the generated output G of each node of electric power system;
S13: i-th cluster average M of setting jth bar load curve ijinitial value, wherein, the span of cluster i is i=1,2 ..., the span of NL, load curve j is j=1,2 ..., NC;
S14: according to formula calculate Euler's distance, in formula, D kibe Euler's distance of a kth load point to a i-th cluster average, j is load curve, and NC is load curve sum, L kjthe load power of a kth load point in load curve j, G kjit is the generated output value of a kth load point in load curve j;
S15: load point is assigned to nearest cluster, it is organized into groups again, according to upgrade cluster average, in formula, the load power of a kth load point in i-th cluster of jth bar load curve, N iit is the load point number in i-th cluster;
S16: repeat step S14 and step S15, until whole cluster average M ijtill remaining unchanged in iteration;
S17: use the cluster average M after convergence ijas the load level of each cluster of load curve each in multi-class workload model, meanwhile, the average of corresponding generated output classification is multistage generated output level.
Further, step S2 specifically comprises:
S21: the unavailability ratio U determining each element in electric power system, in formula, λ is the failure rate of element, and μ is the repair rate of element;
S22: according to the unavailability ratio of each element, determines the probability P of each malfunction of electric power system c, in formula, U lbe the stoppage in transit probability of l element, n is component population order in system, n dbe component number of stopping transport in forecast failure event, if only consider branch road, n is number of branches, if consider branch road and generator, n is the total number of branch road and generator, for single element fault, and n dequal 1.
Further, step S3 specifically comprises:
S31: based on branch power index, each state of system is sorted, based on branch power index be: in formula, S ithe apparent power of branch road i, S i maxthe apparent power limit value of branch road i, w sibe the weight factor of branch road i, NL is number of branches in system, m sbranch power index PI sintegral indices;
S32: based on probability risk index, each state of system is sorted, based on probability risk index be: in formula, P cit is each malfunction probability of electric power system.
Further, according to generator output characteristic in described step S12, determine that the step of the power output P (v) of Wind turbines is:
First, based on the historical data of wind speed, adopt Weibull distribution model matching actual wind speed probability distribution, the cumulative distribution function of wind speed is: the probability density function of wind speed is: f (v)=k/c (v/c) k-1, in formula, v is wind speed, and k is the form parameter of Weibull distribution, and c is the scale parameter of Weibull distribution;
Then, set up Wind turbines sequential according to following formula to exert oneself model:
in formula, v cifor the incision wind speed of Wind turbines, v rfor rated wind speed, v cofor cut-out wind speed, P rfor the rated power of Wind turbines, A, B, C are parameter type, and A = 1 ( V c i - V r ) 2 [ V c i ( V c i + V r ) - 4 V c i V r ( V c i + V r 2 V r ) 3 ] B = 1 ( V c i - V r ) 2 [ 4 ( V c i + V r ) ( V c i + V r 2 V r ) 3 - ( 3 V c i + V r ) ] C = 1 ( V c i - V r ) 2 [ 2 - 4 ( V c i + V r 2 V r ) 3 ] .
The present embodiment adopts the reliability test system of IEEE shown in Fig. 3 (RTS) to be IEEEPowerEngineeringSociety exploitation, is used for comparing the common test system of the result adopting distinct methods gained.This transmission system is made up of 24 bus nodes and 38 transmission lines and transformer.Wherein basic year load peak of IEEE reliability test system RTS is 2850MW.
Table 1 is the percentage value of all load peaks in annual gas load peak value.If first week is counted as January, what table 1 described is the peak value system in certain winter.If within first week, be taken as certain moon in summer, then what describe is the peak value system in summer.Table 2 is the percentage value of daily load peak period in all load peaks.Assuming that all have same all load peak cycles all seasons, the data in table 1 and table 2 and annual gas load peak value together define the daily load peak value model of 52 × 7=364.The percentage value that table 3 is hour load peak in day peak value.Table 2 and table 3 are the day hour load model at Sunday and weekend.Table 1,2 and 3 combine the load value of arbitrary hour together defined in 364 × 24=8736 hour.Table 4 is generating set grade and reliability data.Table 5 is the position of generating set.Table 6 is the bus load data of system when peak value.Table 7 is length of transmission line and forced outage data.Table 8 is impedance and the capacitance grade of circuit and transformer.Table 9 is for pressing the result of power index sequence first 30 of fault set.
The percentage value of all load peaks of table 1 in year peak value
The percentage value of table 2 daily load peak value in all peak values
The percentage value of table 3 hour load peak in day peak value
Table 4 Generating Unit Operation Reliability data
The position of table 5 generating set
Table 6 bus load
Table 7 length of transmission line and forced outage data
Table 8 impedance and capacity limit
Front 30 results of power index sequence fault set pressed by table 9
In above-described embodiment of the application, by providing a kind of Circuits System typical fault diversity method based on State enumeration method, comprise the steps: S1: according to the typical operation method of Kmeans clustering procedure determination electric power system, S2: enumerate the no-failure operation state and fail operation state that generate each element, obtain state and the probable value thereof of whole system, S3: system mode is sorted based on performance index.The method has taken into account operational modes different in electric power system, solves in prior art the deficiency not considering status fault probability risk, high to the screening effeciency of electric power system typical fault, can be Electric Power Network Planning and provides beneficial reference.
It should be noted that; above-mentioned explanation is not limitation of the present invention; the present invention is also not limited in above-mentioned citing, the change that those skilled in the art make in essential scope of the present invention, modification, interpolation or replacement, also should belong to protection scope of the present invention.

Claims (5)

1., based on an electric power system typical fault set defining method for State enumeration method, it is characterized in that, comprise the following steps:
S1: according to kmeans clustering method, cluster is carried out to electric power system node load, generated output data, determine the typical operation modes of electric power system;
S2: according to the failure rate of each element in electrical network, enumerates the no-failure operation state and fail operation state that generate each element, obtains the state of whole system and corresponding probability thereof thus;
S3: system mode is sorted based on performance index.
2. the electric power system typical fault set defining method based on State enumeration method according to claim 1, it is characterized in that, step S1 specifically comprises:
S11: according to sequential load curve, determines the load power L of each node of electric power system;
S12: according to generator output characteristic, determines the generated output G of each node of electric power system;
S13: i-th cluster average M of setting jth bar load curve ijinitial value, wherein, the span of cluster i is i=1,2 ..., the span of NL, load curve j is j=1,2 ..., NC;
S14: according to formula calculate Euler's distance, in formula, D kibe Euler's distance of a kth load point to a i-th cluster average, j is load curve, and NC is load curve sum, L kjthe load power of a kth load point in load curve j, G kjit is the generated output value of a kth load point in load curve j;
S15: load point is assigned to nearest cluster, it is organized into groups again, according to upgrade cluster average, in formula, the load power of a kth load point in i-th cluster of jth bar load curve, N iit is the load point number in i-th cluster;
S16: repeat step S14 and step S15, until whole cluster average M ijtill remaining unchanged in iteration;
S17: use the cluster average M after convergence ijas the load level of each cluster of load curve each in multi-class workload model, meanwhile, the average of corresponding generated output classification is multistage generated output level.
3. the electric power system typical fault set defining method based on State enumeration method according to claim 1, it is characterized in that, step S2 specifically comprises:
S21: the unavailability ratio U determining each element in electric power system, in formula, λ is the failure rate of element, and μ is the repair rate of element;
S22: according to the unavailability ratio of each element, determines the probability P of each malfunction of electric power system c, in formula, U lbe the stoppage in transit probability of l element, n is component population order in system, n dbe component number of stopping transport in forecast failure event, if only consider branch road, n is number of branches, if consider branch road and generator, n is the total number of branch road and generator, for single element fault, and n dequal 1.
4. the electric power system typical fault set defining method based on State enumeration method according to claim 1, it is characterized in that, step S3 specifically comprises:
S31: based on branch power index, each state of system is sorted, based on branch power index be: in formula, S ithe apparent power of branch road i, S i maxthe apparent power limit value of branch road i, w sibe the weight factor of branch road i, NL is number of branches in system, m sbranch power index PI sintegral indices;
S32: based on probability risk index, each state of system is sorted, based on probability risk index be: in formula, P cit is each malfunction probability of electric power system.
5. the electric power system typical fault set defining method based on State enumeration method according to claim 2, is characterized in that, according to generator output characteristic in described step S12, determines that the step of the power output P (v) of Wind turbines is:
First, based on the historical data of wind speed, adopt Weibull distribution model matching actual wind speed probability distribution, the cumulative distribution function of wind speed is: the probability density function of wind speed is: f (v)=k/c (v/c) k-1, in formula, v is wind speed, and k is the form parameter of Weibull distribution, and c is the scale parameter of Weibull distribution;
Then, set up Wind turbines sequential according to following formula to exert oneself model:
in formula, v cifor the incision wind speed of Wind turbines, v rfor rated wind speed, v cofor cut-out wind speed, P rfor the rated power of Wind turbines, A, B, C are parameter type, and A = 1 ( V c i - V r ) 2 [ V c i ( V c i + V r ) - 4 V c i V r ( V c i + V r 2 V r ) 3 ] B = 1 ( V c i - V r ) 2 [ 4 ( V c i + V r ) ( V c i + V r 2 V r ) 3 - ( 3 V c i + V r ) ] C = 1 ( V c i - V r ) 2 [ 2 - 4 ( V c i + V r 2 V r ) 3 ] .
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107256415A (en) * 2017-08-04 2017-10-17 国网北京经济技术研究院 A kind of computational methods and computing system of power system operation mode scene
CN109344875A (en) * 2018-08-31 2019-02-15 中国南方电网有限责任公司电网技术研究中心 Based on clustering day wind power output timing generation method, device and medium
CN110019407A (en) * 2017-12-29 2019-07-16 北京金风科创风电设备有限公司 Processing method, device, equipment and storage medium for enumeration data in wind power plant
CN114254838A (en) * 2022-01-07 2022-03-29 深圳供电局有限公司 Method for determining short-term power load prediction influence factor

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8875255B1 (en) * 2012-09-28 2014-10-28 Emc Corporation Preventing user enumeration by an authentication server
CN104599189A (en) * 2014-12-25 2015-05-06 国家电网公司 Power grid planning scheme risk evaluation method considering power system operation mode

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8875255B1 (en) * 2012-09-28 2014-10-28 Emc Corporation Preventing user enumeration by an authentication server
CN104599189A (en) * 2014-12-25 2015-05-06 国家电网公司 Power grid planning scheme risk evaluation method considering power system operation mode

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
丁施尹: "地区电网运行风险评估及控制", 《CNKI硕士学位论文》 *
杨贺钧: "计及多因素的含风能电力系统可靠性评估及优化研究", 《万方学位电气工程》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107256415A (en) * 2017-08-04 2017-10-17 国网北京经济技术研究院 A kind of computational methods and computing system of power system operation mode scene
CN110019407A (en) * 2017-12-29 2019-07-16 北京金风科创风电设备有限公司 Processing method, device, equipment and storage medium for enumeration data in wind power plant
CN110019407B (en) * 2017-12-29 2021-07-13 北京金风科创风电设备有限公司 Processing method, device, equipment and storage medium for enumeration data in wind power plant
CN109344875A (en) * 2018-08-31 2019-02-15 中国南方电网有限责任公司电网技术研究中心 Based on clustering day wind power output timing generation method, device and medium
CN114254838A (en) * 2022-01-07 2022-03-29 深圳供电局有限公司 Method for determining short-term power load prediction influence factor

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