CN109118035B - Grid early warning information-based typhoon and waterlogging disaster power distribution network risk assessment method - Google Patents
Grid early warning information-based typhoon and waterlogging disaster power distribution network risk assessment method Download PDFInfo
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Abstract
The invention discloses a risk assessment method for a power distribution network with typhoon, wind and flood disasters based on gridding early warning information, which comprises the following steps: (1) evaluating the fault probability; (2) generation of an expected failure: generating a direct expected fault set caused by typhoon and waterlogging damage, and supplementing to generate a complete expected fault set under the typhoon and waterlogging damage; (3) analyzing the network topology: network topology analysis is carried out on the power distribution network by combining with power distribution network state estimation data to obtain line information of a plant station and a root node of the plant station, and load loss area evaluation is carried out on the power distribution network under typhoon, waterlogging disaster and disaster damage to obtain a power distribution network load loss area; (4) risk assessment of the power distribution network: calculating to obtain the loss load probability of the regional power distribution network and the loss load of the station; and calculating to obtain the risk value of the distribution network in the area under the typhoon, wind and flood disasters according to the importance level of the load and the load loss probability of the plant station. The invention adopts the early warning information based on gridding, thereby improving the engineering practicability.
Description
Technical Field
The invention relates to a risk assessment method for a typhoon, flood and disaster prevention power distribution network based on gridding early warning information, and belongs to the field of power grid disaster prevention and reduction.
Background
The power distribution network is a high disaster object of typhoon disasters. Because the distribution network has natural structure and power supply fragility than transmission of electricity rack, along with the continuous increase of distribution network scale after the urbanization, its probability or the risk of suffering extreme natural disastrous weather damage are bigger and bigger. The main inducing factors of the power distribution network disaster in the typhoon environment are divided into wind and waterlogging. At present, a comprehensive detection system based on a satellite, a weather radar and a ground automatic weather observation station and a typhoon path numerical forecasting service system integrating national level, regional center and provincial level are preliminarily established in China, and the forecasting and early warning capability of typhoon service in China is greatly improved. Related scientific research institutions or enterprises and public institutions already have the capability of releasing the early warning information of the gridded power equipment under the typhoon disaster, and the grid precision can reach 3km x 3km, even 1km x 1 km.
Disclosure of Invention
The invention aims to provide a risk assessment method for a typhoon and waterlogging damage power distribution network based on gridding early warning information for a power distribution network disaster prevention and reduction system.
In order to achieve the purpose, the invention is realized by the following technical scheme:
the invention discloses a risk assessment method for a typhoon, flood and disaster damage power distribution network based on gridding early warning information, which comprises the following steps of:
(1) and (3) fault probability evaluation: accessing externally provided warning information of distribution network equipment under typhoon, waterlogging disaster based on meshing, realizing the conversion between the warning information of the distribution network equipment and the fault probability of the distribution network equipment, and calculating the fault probability of a line;
(2) generation of an expected failure: generating a direct expected fault set caused by typhoon, wind and flood according to the power distribution network line fault information obtained in the step (1); supplementing the direct expected fault set to generate a complete expected fault set under typhoon, wind and flood disasters;
(3) analyzing the network topology: based on the expected fault information of the power distribution network under the typhoon, the wind and the flood, and by combining with power distribution network state estimation data provided by an automation department of a power company, carrying out network topology analysis on the power distribution network to obtain line information of a plant station and a root node of the plant station, and further combining with the line fault information of the power distribution network, carrying out load loss area evaluation on the power distribution network under the typhoon, the flood and the flood to obtain a load loss area of the power distribution network;
(4) risk assessment of the power distribution network: calculating the loss load probability and the station loss load of the regional power distribution network based on the loss load region of the power distribution network obtained in the step (3); and calculating to obtain the risk value of the distribution network in the area under the typhoon, the wind and the flood by combining the important grade of the load, and outputting.
In the step (1), the accessed warning information of the power distribution network equipment under the grid typhoon, wind and flood disasters can be characterized as follows:
gridding the study area on a spatial scale of p (km) × q (km), using GaWherein a is the number of the grid;
grid G under typhoon and waterloggingaIn line I, the broken line fault alarm level is
The pole-breaking warning grade of the pole tower t is
The alarm level of the station s is
And
under the influence of the wind, the wind-driven generator is provided with a wind-driven generator,
caused by water logging.
In the step (1), the specific method for realizing the conversion between the alarm information of the power distribution network equipment and the fault probability of the power distribution network equipment is as follows:
setting the conversion relation between the alarm and the fault probability of the power grid equipment, namely, under the typhoon and waterlogging disasters, the grid GaLevel of alarm for broken line fault of internal and line l
Corresponding line break fault probability
Pole-breakage warning grade of pole tower t
For failure probability of reverse breaking rod
Alarm level of waterlogging of plant station s
Corresponding fault probability of waterlogging
In the step (1), the line fault probability calculation is divided into two parts, and the specific method is as follows:
firstly, calculating the fault probability of the lines in the grid:
grid G under typhoon and waterloggingaProbability of failure P of internal, line lI(a)：
Wherein x is the span number of the line l in the grid, y is the number of the pole towers of the line l in the grid, and z is the number of the stations associated with the line l in the grid;
then, the probability of the fault of the whole line is calculated:
the integral failure probability P of the line l under the typhoon and waterlogging disastersl：
Wherein n is the number of grids passed by the line l, Pl(a)For line l in grid GaProbability of failure within.
In the step (4), the specific calculation method of the station load loss probability is as follows:
under the typhoon, waterlogging disaster, the load loss probability P of the plant stations：
Where m is the number of lines associated with the plant s and its root node, PlAs is the probability of failure of the line l,
is the probability of flooding of the plant station s.
In the step (4), a specific calculation method of the risk value of the distribution network in the area under the typhoon, wind and flood disasters is as follows:
setting a regional distribution network corresponding to a station s, wherein the load associated with the station s is LsThen the risk value R of the distribution network in the areasThe definition is as follows:
Rs＝θs·Ps·Ls
in the formula, thetasFor adjustable weight coefficient, by applying LsThe importance degree is obtained by off-line analysis; l issMay be derived from node power statistics in the state estimate data.
Compared with the traditional power distribution network risk assessment method, the method has the advantages that the engineering practicability is improved due to the adoption of the early warning information based on gridding; when the risk of the power distribution network under typhoon disasters is evaluated, the influence of wind and waterlogging on the power distribution network is considered; in addition, the importance level of the load is also taken into consideration, and more comprehensive decision support is provided for the power distribution network disaster prevention system.
Drawings
Fig. 1 is a working flow chart of the risk assessment method for the power distribution network caused by typhoon, wind and flood based on the gridding early warning information.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
Referring to fig. 1, the method for evaluating the risk of the distribution network under the typhoon, waterlogging disaster and based on the gridding early warning information comprises the following steps:
step 1: fault probability assessment
Firstly, based on the early warning information of the power distribution network equipment gridded under typhoon, wind and flood disasters, the early warning level and the equipment fault probability are converted.
Gridding the study area on a spatial scale of p (km) × q (km), using GaWhere a is the number of the grid. Grid G under typhoon and waterloggingaIn line I, the broken line fault alarm level is
The pole-breaking warning grade of the pole tower t is
The alarm level of the station s is
And
is mainly affected by the "wind" and,
Primarycaused by "waterlogging".
Based on historical statistical analysis, data mining, expert experience and field operation experience, the conversion relation between the alarm and the fault probability of the power grid equipment can be obtained. Namely, under the typhoon and waterlogging disaster, the grid GaLevel of alarm for broken line fault of internal and line l
Corresponding line break fault probability
Pole-breakage warning grade of pole tower t
For failure probability of reverse breaking rod
Alarm level of waterlogging of plant station s
Corresponding fault probability of waterlogging
And then calculating the fault probability of the distribution network lines in the grid.
Grid G under typhoon and waterloggingaProbability of failure P of internal, line ll(a)：
Wherein x is the span number of the line l in the grid, y is the number of towers of the line l in the grid, and z is the number of stations associated with the line l in the grid. According to the scale and characteristics of the distribution line, the value of z is generally as follows: 1 or 2.
And calculating the integral fault probability of the distribution network line.
The integral failure probability P of the line l under the typhoon and waterlogging disastersl：
Wherein n is the number of grids passed by the line l, Pl(a)For line l in grid GaProbability of failure within.
Step 2: anticipatory fault generation
Generating power distribution network line fault information output by the fault probability evaluation function module, and generating a direct expected fault set caused by typhoon, wind and flood.
And then, on the basis of the directly expected fault set, further combining power distribution network equipment model data (for example, taking the influence of the double circuit lines on the loss load of the plant and related faults thereof into consideration), supplementing the directly expected fault set, and generating a complete expected fault set under the typhoon and waterlogging disasters.
And step 3: network topology analysis
Firstly, network topology analysis is carried out on the power distribution network based on expected fault information of the power distribution network under typhoon, wind and flood disasters and combined with power distribution network state estimation data, and line information of the plant station and a root node of the plant station is obtained.
And then carrying out load loss area evaluation (namely island evaluation) on the power distribution network under the typhoon, wind and flood disasters to obtain island area information.
And 4, step 4: power distribution network risk assessment
The method comprises the steps of firstly, calculating the station load loss probability related to the regional power distribution network.
Under the typhoon, waterlogging disaster, the load loss probability P of the plant stations：
Where m is the number of lines associated with the plant s and its root node, PlAs is the probability of failure of the line l,
for factoryProbability of flooding of station s.
Then, the plant station lost load L is obtained based on the island information (namely the power distribution network lost load area) evaluated in the step 3 and by combining the plant station carried load information corresponding to the islands(i.e., the amount of lost load in the grid area).
And combining the important level of the load and the plant station load loss probability to obtain a distribution network risk value in the area under the typhoon, waterlogging and disaster damage, and outputting the distribution network risk value to a distribution network disaster prevention and decision system. The risk value calculation method is as follows:
setting the regional power distribution network corresponding to a station s, wherein the load quantity associated with the station s is LsThen the risk value R of the distribution network in the areasThe definition is as follows:
Rs＝θs·Ps·Ls
in the formula, thetasFor adjustable weight coefficient, by applying LsThe importance degree is obtained by off-line analysis; l issMay be derived from node power statistics in the state estimate data.
Compared with the traditional power distribution network risk assessment method, the power distribution network risk assessment method under typhoon, waterlogging and disaster based on the gridding early warning information improves the engineering practicability due to the adoption of the gridding early warning information; when the risk of the power distribution network under typhoon disasters is evaluated, the influence of wind and waterlogging on the power distribution network is considered; in addition, the importance level of the load is also taken into consideration, and more comprehensive decision support is provided for the power distribution network disaster prevention system.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (1)
1. The method for evaluating the risk of the power distribution network caused by typhoon, flood and disaster based on the gridding early warning information is characterized by comprising the following steps:
(1) and (3) fault probability evaluation: accessing externally provided warning information of distribution network equipment under typhoon, waterlogging disaster based on meshing, realizing the conversion between the warning information of the distribution network equipment and the fault probability of the distribution network equipment, and calculating the fault probability of a line;
(2) generation of an expected failure: generating a direct expected fault set caused by typhoon, wind and flood according to the line fault probability obtained in the step (1); supplementing the direct expected fault set to generate a complete expected fault set under typhoon, wind and flood disasters;
(3) analyzing the network topology: based on the complete expected fault set of the power distribution network under the typhoon, the wind and the flood, and by combining with the power distribution network state estimation data, performing network topology analysis on the power distribution network to obtain line information of the plant station associated with a root node of the plant station, and further combining with the line fault probability, performing load loss area evaluation on the power distribution network under the typhoon, the flood and the flood to obtain a load loss area of the power distribution network;
(4) risk assessment of the power distribution network: calculating the loss load probability and the station loss load of the regional power distribution network based on the loss load region of the power distribution network obtained in the step (3); calculating the risk value of the distribution network in the area under the typhoon, wind and flood disasters by combining the important grade of the load, and outputting the risk value;
in the step (1), the accessed warning information of the power distribution network equipment under the grid typhoon, wind and flood disasters can be characterized as follows:
gridding the study area on a spatial scale of p (km) × q (km), using GaWherein a is the number of the grid;
grid G under typhoon and waterloggingaIn line I, the broken line fault alarm level is
The pole-breaking warning grade of the pole tower t is
The alarm level of the station s is
And
under the influence of the wind, the wind-driven generator is provided with a wind-driven generator,
caused by water logging;
in the step (1), the specific method for realizing the conversion between the alarm information of the power distribution network equipment and the fault probability of the power distribution network equipment is as follows:
setting the conversion relation between the alarm and the fault probability of the power grid equipment, namely, under the typhoon and waterlogging disasters, the grid GaLevel of alarm for broken line fault of internal and line l
Corresponding line break fault probability
Pole-breakage warning grade of pole tower t
For failure probability of reverse breaking rod
Alarm level of waterlogging of plant station s
Corresponding fault probability of waterlogging
In the step (1), the line fault probability calculation is divided into two parts, and the specific method is as follows:
firstly, calculating the fault probability of the lines in the grid:
grid G under typhoon and waterloggingaProbability of failure P of internal, line ll(a)：
Wherein x is the span number of the line l in the grid, y is the number of the pole towers of the line l in the grid, and z is the number of the stations associated with the line l in the grid;
then, the probability of the fault of the whole line is calculated:
the integral failure probability P of the line l under the typhoon and waterlogging disastersl：
Wherein n is the number of grids passed by the line l, Pl(a)For line l in grid GaThe probability of failure within;
in the step (4), the specific calculation method of the station load loss probability is as follows:
under the typhoon, waterlogging disaster, the load loss probability P of the plant stations：
Where m is the number of lines associated with the plant s and its root node, PlAs is the probability of failure of the line l,
the waterlogging probability of a station s;
in the step (4), a specific calculation method of the risk value of the distribution network in the area under the typhoon, wind and flood disasters is as follows:
regional distribution network and station sCorrespondingly, the station load loss quantity associated with the station s is LsThen the risk value R of the distribution network in the areasThe definition is as follows:
Rs＝θs·Ps·Ls
in the formula, thetasFor adjustable weight coefficient, by applying LsThe importance degree is obtained by off-line analysis; l issMay be derived from node power statistics in the state estimate data.
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| CN110097223B (en) * | 2019-04-30 | 2022-05-13 | 武汉理工大学 | Early warning method for damage of power transmission line under typhoon disaster |
| CN111062604B (en) * | 2019-12-12 | 2023-09-22 | 国家电网有限公司大数据中心 | Meteorological disaster-based power grid business risk assessment method, device and equipment |
| CN112421767B (en) * | 2020-09-30 | 2023-04-07 | 云南电网有限责任公司西双版纳供电局 | Power grid disaster prevention emergency panoramic monitoring method and device |
| CN113344238B (en) * | 2021-03-29 | 2022-06-24 | 广西电网有限责任公司电力科学研究院 | Power distribution network security risk assessment and reduction method, device and storage medium |
| CN113177858B (en) * | 2021-03-31 | 2022-11-08 | 国网浙江省电力有限公司温州供电公司 | Multi-dimensional power grid typhoon resistance evaluation method |
| CN114444886B (en) * | 2021-12-30 | 2024-08-27 | 国网浙江省电力有限公司温州供电公司 | Analysis method-based damage probability evaluation method for power distribution network under typhoon disaster |
| CN115830813B (en) * | 2023-02-15 | 2023-05-16 | 江西和壹科技有限公司 | Natural disaster monitoring and early warning system based on AI technology |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103942729A (en) * | 2014-04-15 | 2014-07-23 | 国网辽宁省电力有限公司沈阳供电公司 | Urban distribution network disaster risk assessment method based on minimum distribution area |
| CN104966147A (en) * | 2015-05-19 | 2015-10-07 | 国网河南省电力公司 | Power grid operating risk analyzing method in view of base state and accident state |
| CN105243459A (en) * | 2015-11-12 | 2016-01-13 | 华北电力大学 | Power transmission line mountain fire tripping risk control method integrating weather and artificial fire prevention measures |
| CN105654414A (en) * | 2015-12-25 | 2016-06-08 | 浙江大学城市学院 | Urban multi-disaster risk loss evaluation system based on open source system framework and building spatial database and method thereof |
| CN106022953A (en) * | 2016-05-13 | 2016-10-12 | 国网浙江省电力公司台州供电公司 | Power grid infrastructure rainstorm risk assessment method |
| CN106651166A (en) * | 2016-12-15 | 2017-05-10 | 中国南方电网有限责任公司电网技术研究中心 | Natural disaster risk processing method and system based on Internet of Things |
| CN206235747U (en) * | 2016-12-15 | 2017-06-09 | 中国南方电网有限责任公司电网技术研究中心 | Natural disaster risk processing device based on Internet of things |
| CN107578169A (en) * | 2017-09-04 | 2018-01-12 | 广东电网有限责任公司惠州供电局 | The recognition methods of power network critical circuits and device under the conditions of a kind of typhoon disaster |
-
2018
- 2018-06-25 CN CN201810661486.1A patent/CN109118035B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103942729A (en) * | 2014-04-15 | 2014-07-23 | 国网辽宁省电力有限公司沈阳供电公司 | Urban distribution network disaster risk assessment method based on minimum distribution area |
| CN104966147A (en) * | 2015-05-19 | 2015-10-07 | 国网河南省电力公司 | Power grid operating risk analyzing method in view of base state and accident state |
| CN105243459A (en) * | 2015-11-12 | 2016-01-13 | 华北电力大学 | Power transmission line mountain fire tripping risk control method integrating weather and artificial fire prevention measures |
| CN105654414A (en) * | 2015-12-25 | 2016-06-08 | 浙江大学城市学院 | Urban multi-disaster risk loss evaluation system based on open source system framework and building spatial database and method thereof |
| CN106022953A (en) * | 2016-05-13 | 2016-10-12 | 国网浙江省电力公司台州供电公司 | Power grid infrastructure rainstorm risk assessment method |
| CN106651166A (en) * | 2016-12-15 | 2017-05-10 | 中国南方电网有限责任公司电网技术研究中心 | Natural disaster risk processing method and system based on Internet of Things |
| CN206235747U (en) * | 2016-12-15 | 2017-06-09 | 中国南方电网有限责任公司电网技术研究中心 | Natural disaster risk processing device based on Internet of things |
| CN107578169A (en) * | 2017-09-04 | 2018-01-12 | 广东电网有限责任公司惠州供电局 | The recognition methods of power network critical circuits and device under the conditions of a kind of typhoon disaster |
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