CN115392551A - Method and system for predicting power failure range of flood disaster-causing power supply station area - Google Patents
Method and system for predicting power failure range of flood disaster-causing power supply station area Download PDFInfo
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Abstract
The invention discloses a method for predicting the power failure range of a flood disaster-causing power supply station area, wherein the method acquires short-term weather forecast information and studies and judges whether to start a process; when the process needs to be started, acquiring an electric power geographic information map, and judging whether a power supply area in the electric power geographic information map is in a boundary range of a heavy rainfall influence area; when the power supply areas are located in the boundary range of the strong rainfall influence area, calculating the individual area of the power distribution facility where the power supply areas affected by the flood disaster are located, the total area of each power supply area and the power failure pre-evaluation accurate probability of each power supply area respectively; and constructing a neural network for measuring the power outage range of the power supply station area due to disasters, and solving and outputting a prediction result of the power outage range due to the flood disasters by the neural network. The function of measuring the flood disaster-causing power outage area by using the big data based on the power outage historical data and the rainfall real-time data is realized, and the problem that the method for predicting the power distribution facility submergence risk only by using historical experience in the related technology is unreliable is solved.
Description
Technical Field
The invention relates to the technical field of power disaster prevention and reduction, in particular to a method and a system for power failure range and power failure risk level of a power supply station area caused by flood disasters.
Background
Heavy rainfall natural disasters can affect power distribution network facilities, and the conditions of pole falling, pole inclining, distribution transformer soaking and the like occur, so that power failure of users is caused.
On the one hand, heavy rainfall is a direct factor causing disasters such as mountain torrents, medium and small river floods and the like, and even evolves to large-area power failure. The origin of the former flood season of south China is closely related to the wind activities in summer in south China sea, and at the moment, strong rainfall is generated by a warm and humid air mass with single property and is often called as warm area strong rainfall. At the moment, if the cold air activity still goes deep into the south China area and the cold air flow and the hot air flow are mixed for a long time in the south China area to form frontal surface precipitation when the wind breaks out in the early summer season. Under the common influence of monsoon precipitation and frontal precipitation, strong precipitation weather with large range and concentration may occur in the south China, which affects the reliable supply of electric power. On the other hand, the rainfall intensity and the spatial-temporal distribution relation of the power supply station area submerged in the rainfall process are complex, and the current method for predicting the power distribution facility submerged risk only by means of historical experience is not reliable, so that the method is not beneficial to emergency response work such as early deployment load transfer, facility reinforcement, emergency repair and power restoration and the like.
Practice shows that flood disasters caused by heavy rainfall have great influence on reliable power supply, and particularly, a measurement method of the heavy rainfall on power failure areas needs to be researched. In view of this, it is necessary to utilize advanced algorithms and systems in the power generation command center to solve the power outage risk level of the power supply station area caused by heavy rainfall, and technically support emergency disposal work.
Disclosure of Invention
The embodiment of the invention provides a method and a system for predicting the power failure range of a flood disaster-causing power supply area, which are used for at least solving the technical problem that a method for predicting the submergence risk of a power distribution facility only by relying on historical experience in the related art is unreliable.
According to an aspect of the embodiments of the present invention, a method for predicting a power outage range of a flood disaster-causing power supply area is provided, including:
acquiring short-term weather forecast information, and judging whether a process for measuring power failure of a power supply area due to disaster is started or not;
when the process needs to be started, acquiring an electric power geographic information map, and judging whether a power supply area in the electric power geographic information map is in a boundary range of a heavy rainfall influence area or not;
when the power supply areas are located in the boundary range of the strong rainfall influence area, calculating the individual area of the power distribution facility where the power supply areas affected by the flood disaster are located, the total area of each power supply area and the power failure pre-evaluation accurate probability of each power supply area respectively;
according to the individual area of a power distribution facility where power supply areas affected by flood disasters are located, the total area of each power supply area, the power outage pre-evaluation accuracy probability of each power supply area, the flood grade, the station variable and the number of users, a neural network for measuring the power outage range of the power supply areas due to the flood is constructed, and the neural network is used for solving and outputting the prediction result of the power outage range of the power supply areas due to the flood disasters.
Optionally, the prediction result comprises: the individual area of the power distribution facility where the power supply platform area influenced by the flood disaster is located, the total area of each power supply platform area, the power failure risk level of the power supply platform area caused by heavy rainfall and a real-time display map.
Optionally, the process of judging whether to start measuring power failure of the power supply station area due to disaster includes: and judging whether the rainfall in the set time exceeds a threshold value.
Optionally, a spatial association rule is used to determine whether the power supply area is in a boundary range of a heavy rainfall influence area.
Optionally, the method for judging whether the power supply area is in the boundary range of the heavy rainfall influence area by using the spatial association rule specifically includes:
condition section A (M) u ，M v ) Generating point coordinate cluster of strong rainfall influence area for provincial region weather forecast image and rainfall situation image, and confidence interval B (M) x ，M y ) Generating a point coordinate cluster of a power supply area for the electric power geographic information map;
for rule A → B, the conditional and confidence interval calculation formulas are:
Support(A→B)＝Support(A∪B)＝P(A∪B)
Feasible(A→B)＝P(B|A)
the expression for judging whether the power supply area is in the boundary range of the heavy rainfall influence area is as follows:
optionally, calculating the individual area of the power distribution facility where the power supply platform area affected by the flood disaster is located specifically includes:
the individual area M of the distribution facility at power supply district place that flood disaster influenced is for using district distribution transformer of place to be the area of geometric center to distribution transformer's power supply boundary within range, and the individual area M of the distribution facility at power supply district place that flood disaster influenced is irregular shape, and has n boundary points, and the expression is:
in the above formula, x i 、y i Are the plane coordinates of each boundary point.
Optionally, the total area of the power supply areas affected by the flood disaster is the sum of the individual areas of the power distribution facilities where the power supply areas affected by the flood disaster are located.
Optionally, the expression of the power outage pre-evaluation accuracy probability F of the power supply station area is as follows:
in the above formula, β represents a weighting coefficient for the grid of the power outage history, R e Refers to the proportion, i.e. sensitivity, P, correctly identified as a blackout grid among all actual blackout grid samples r The accuracy is the proportion of the actual blackout grid in the samples identified as the blackout grid.
Optionally, the neural network input comprises an input layer, a hidden layer and an output layer,
the input layer nodes comprise the individual area of the power distribution facility where each power supply area affected by the flood disasters exists, the total area of each power supply area and the power failure pre-evaluation accurate probability of the corresponding power supply area within the possible duration of the flood;
the hidden layer is used for solving the power failure risk level of each power supply area caused by heavy rainfall;
the output layer is used for outputting display diagrams for identifying power supply areas in different power failure risk levels.
According to another aspect of the embodiments of the present invention, there is also provided a system for predicting a power outage range of a flood disaster-induced power supply area, including:
the acquisition layer is used for acquiring short-term weather forecast information through the front-end acquisition server and judging whether to start a process for measuring power failure of the power supply area due to disasters; when the process needs to be started, acquiring an electric power geographic information map, and judging whether a power supply area in the electric power geographic information map is in a boundary range of a heavy rainfall influence area or not; meanwhile, the preposed acquisition server is positioned in a safe access area, and the safe access area meets the network safety requirement of accessing data when a communication mode of a public communication network and a wireless communication network is used, wherein the public communication network does not comprise the Internet;
the data layer comprises data used for storing data related to the prediction of the power failure range of the flood disaster-causing power supply area;
the processing layer constructs a neural network for measuring the power supply area power failure range due to the flood disaster through the application server according to the individual area of the power distribution facility where the power supply area is located, the total area of each power supply area, the power failure pre-evaluation accurate probability of each power supply area, the flood level, the station variable number and the number of users, wherein the power supply area is influenced by the flood disaster; and
the application layer is used for outputting and displaying a prediction result of the power failure area range caused by the flood disaster; and the prediction information of the power failure range of the power supply area caused by the flood is issued to related technicians in related enterprises through the website server.
Compared with the prior art, the invention has the following beneficial effects:
in the embodiment of the invention, short-term weather forecast information is acquired, and whether a process for measuring power failure of a power supply area due to disaster is started or not is judged; when the process needs to be started, acquiring an electric power geographic information map, and judging whether a power supply area in the electric power geographic information map is in a boundary range of a heavy rainfall influence area or not; when the power supply areas are located in the boundary range of the strong rainfall influence area, calculating the individual area of the power distribution facility where the power supply areas affected by the flood disaster are located, the total area of each power supply area and the power failure pre-evaluation accurate probability of each power supply area respectively; according to the individual area of the power distribution facility where the power supply areas affected by the flood disasters are located, the total area of each power supply area, the power outage pre-evaluation accuracy probability of each power supply area, the flood level, the station variable and the number of users, a neural network for measuring the power outage range of the power supply areas due to the flood is constructed, and the neural network solves and outputs the prediction result of the power outage range of the power supply areas due to the flood disasters. The power failure area unbiased estimation method has the advantages that the function of measuring the disaster-causing power failure area of flood based on power failure historical data and rainfall real-time data is realized, the technical problem that the method for predicting the power distribution facility submergence risk only by means of historical experience in the related technology is unreliable and the problem of processing high-dimensional characteristic input samples are solved, the unbiased estimation result of the power failure area has excellent accuracy, and the production command center is facilitated to guide and develop emergency response work such as load transfer, facility reinforcement, emergency repair and power restoration.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a method for predicting a power outage range of a flood disaster-induced power supply area according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a neural network for measuring a power outage range of a power supply area according to an embodiment of the invention;
fig. 3 is a schematic diagram of a system for predicting a power outage range of a flood disaster-induced power supply area according to an embodiment of the present invention.
Detailed Description
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to an embodiment of the present invention, there is provided an embodiment of a method for predicting blackout areas of flood-induced power stations, where the steps illustrated in the flowcharts of the figures may be performed in a computer system, such as a set of computer-executable instructions, and where a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than the order illustrated.
Fig. 1 is a flowchart of a method for predicting a power outage range of a flood disaster-induced power supply area according to an embodiment of the present invention, where as shown in fig. 1, the method includes the following steps:
and S10, acquiring short-term weather forecast information, and judging whether a process for measuring power failure of the power supply area due to disaster needs to be started or not.
As an optional embodiment, the short-term weather forecast information is acquired by a weather station short-term and medium-term early warning center; the elements contained in the short-term weather forecast information include: weather of 3 days in the future and regional single-station daily rainfall meteorological elements, provincial regional weather forecast maps and rainfall actual conditions maps of all time periods of the weather.
As an optional embodiment, the process of determining whether to start to measure the power outage due to disaster in the power supply area includes: whether the amount of rain within a set time exceeds a threshold. For example, the starting process needs to satisfy one of the following criteria: the rainfall is more than or equal to 30 mm in 12 hours or more than or equal to 50 mm in 24 hours.
S20, when the process needs to be started, acquiring an electric power geographic information map, and judging whether a power supply area of the electric power geographic information map is in a boundary range of a strong rainfall influence area; and otherwise, ending the prediction method of the power failure range of the flood disaster-causing power supply area.
As an alternative embodiment, the power geographic information map may be obtained by a power geographic information system.
Specifically, the power geographic information map may be a provincial power geographic information map corresponding to the starting prediction process.
As an optional embodiment, whether the power supply station area in the power geographic information map is in the boundary range of the heavy rainfall influence area is judged by using a spatial association rule.
The method specifically comprises the following steps: under the space association rule, whether the power supply area is in the boundary range of the heavy rainfall influence area or not is judged according to two indexes that a condition interval (Support) and a confidence interval (Feasible) are the space association rule. Therefore, the method for judging whether the power supply area is in the boundary range of the heavy rainfall influence area by using the spatial association rule comprises the following steps:
condition section A (M) u ，M v ) Generating point coordinate cluster of strong rainfall influence area for provincial region weather forecast graph and rainfall actual situation graph, and confidence interval B (M) x ，M y ) Generating a point coordinate cluster of a power supply area for the electric power geographic information map;
for rule A → B, the formula for the condition interval and confidence interval are:
Support(A→B)＝Support(A∪B)＝P(A∪B)
Feasible(A→B)＝P(B|A)
the expression for judging whether the power supply area (concerned) in the power geographic information map is in the boundary range of the heavy rainfall influence area is as follows:
and S30, when the power supply area is in the boundary range of the strong rainfall influence area, calculating the independent area of the power distribution facility where the power supply area influenced by the flood disaster is located.
As an optional embodiment, for a power supply transformer in a flood area caused by heavy rainfall, calculating an individual area of a power distribution facility where the power supply transformer affected by the flood disaster is located, specifically including:
the individual area M of the distribution facility at power supply district place that flood disaster influenced is for using district distribution transformer of place to be the area of geometric center to distribution transformer's power supply boundary within range, and the individual area M of the distribution facility at power supply district place that flood disaster influenced is irregular shape, and has n boundary points, and the expression is:
in the above formula, x i 、y i Are the plane coordinates of each boundary point.
And S40, calculating the total area of each power supply area influenced by the flood disaster.
As an alternative embodiment, the total area of the power supply stations affected by the flood disaster is the sum of the individual areas of the power distribution facilities where the power supply stations affected by the flood disaster are located.
Specifically, the expression of the total area S of the power supply area affected by the flood disaster is as follows:
in the above formula, n is the number of the individual areas of the power distribution facility where the power supply station area affected by the flood disaster is located.
And S50, calculating the power failure pre-evaluation accurate probability of each power supply area influenced by the flood disaster.
As an optional embodiment, the power outage pre-evaluation accurate probability F of the power supply area obtained according to the historical sample can be solved and input through a prediction method model, and the expression of F is as follows:
in the above formula, β represents a weighting coefficient for the grid of the power outage history, R e Is the proportion (sensitivity), P, of all the actual blackout grid samples which is correctly identified as the blackout grid r This is the ratio (accuracy) of the actual blackout grid in the samples identified as blackout grids.
Preferably, the weighting coefficient β of the grid of the historical blackouts is referred to the partition principle of the power supply density, as shown in table 1;
table 1 power supply density division table for power supply station area
Preferably, σ is the load density (MW/km) of the power supply region 2 )
Preferably, the sensitivity R e And accuracy P r The calculation formula of (c) is as follows:
in the above formula, S TP The method refers to the number of samples of an actual power failure grid and a forecast power failure grid; s FN The number of samples of actual blackout grids and prediction of blackout grids is the number of samples of the blackout grids; s FP The number of samples is the actual uninterrupted grid and the predicted uninterrupted grid.
Step S60, according to the individual area of the power distribution facility where the power supply station area is located, the total area S of each power supply station area, the power failure pre-evaluation accuracy probability F of each power supply station area and the rainfall-flood grade X, which are influenced by the flood disaster 1 Table variable X 2 And number of users X 3 And constructing a neural network for measuring the power outage range of the power supply station area due to the disaster, and solving and outputting a prediction result of the power outage range due to the flood disaster by the neural network.
As an alternative embodiment, fig. 2 is a schematic diagram of a neural network for measuring a power outage range of a power supply station area due to disaster according to an embodiment of the present invention, where the neural network input for measuring the power outage range of the power supply station area due to disaster includes: the network comprises an input layer, a hidden layer and an output layer, wherein the whole network belongs to a multi-input and single-output type network.
In particular, the method comprises the following steps of,the input layer node x is included in the flood probable duration T over The individual area M of the power distribution facility where the power supply transformer area influenced by each flood disaster is located, the total area S of each power supply transformer area and the power failure pre-evaluation accuracy probability F of the corresponding power supply transformer area.
Wherein the flood is likely to last for a time T over The calculation formula is as follows:
T over ＝T e -T s +12
in the above formula, T e Is the end time of the rainfall process, T s Refers to the beginning time of the rainfall process, and the time unit is hour.
The hidden layer is used for solving the power failure risk level R of each power supply area caused by heavy rainfall, and the expression of R is as follows:
in the above formula, w 1 、w 2 、w 3 Respectively mean the rain and waterlogging level X 1 Table variable X 2 And the number X of users 3 The corresponding weight. Rain and flood grade X 1 For important factors causing power failure of users, the corresponding division principle is shown in table 2; number of desk variables X 2 When the device is used for power restoration after disaster, the transformer in the area is considered to be maintained; number of users X 3 The method is used for considering the aim of taking the user power restoration as first-aid repair.
TABLE 2 rain-waterlogging grade X 1 Division table
| Grade | Value range | Description of the invention | Colour(s) |
| Class I | [0，0.5) | Can accept | Green colour |
| Stage II | [0.5，1.0) | Slight, it is a little | Blue color |
| Stage II | [0.1，1.5) | Of moderate degree | Yellow colour |
| Class III | [1.5，2.0) | Severe severity of disease | Orange colour |
| IV stage | [2.0，∞) | Is particularly serious | Red colour |
The output layer is used for outputting display diagrams for identifying power supply areas in different power failure risk levels.
As an alternative embodiment, the prediction result includes: the individual area of the power distribution facility where the power supply transformer area affected by flood disasters is located, the total area of each power supply transformer area, the power failure risk level of the power supply transformer area caused by heavy rainfall and a real-time display diagram.
As an alternative embodiment, the prediction result may be issued according to the release specification. Specifically, the prediction results are published by referring to the specification of "meteorological disaster warning information website propagation specification" of QX/T549 for each individual submerged area M, the power outage risk level R of the power supply area corresponding to the individual submerged area M, and the total submerged area S of the power supply area.
Example 2
According to another aspect of the embodiments of the present invention, there is also provided a system for predicting a power outage range of a flood disaster-induced power supply area, where the system includes: the device comprises an acquisition layer, a data layer, a processing layer and an application layer. The prediction system is explained in detail below.
The acquisition layer is used for acquiring short-term weather forecast information through the front acquisition server and judging whether a process for measuring power failure of the power supply area due to disaster is started or not; when the process needs to be started, acquiring an electric power geographic information map, and judging whether a power supply area in the electric power geographic information map is in a boundary range of a heavy rainfall influence area or not; meanwhile, the preposed acquisition server is positioned in a safe access area, and the safe access area meets the network safety requirement of accessing data when a communication mode of a public communication network and a wireless communication network is used, wherein the public communication network does not comprise the Internet.
Specifically, short-term weather forecast information (including a provincial region weather forecast map and a rainfall actual situation map) of a short-term and medium-term early warning center of a provincial weather station at the location, an electric power geographic information map of an electric power geographic information system, the rainfall level of the distribution and transformation position of each power supply station area and the power failure pre-evaluation accuracy probability of the power supply station area are collected through a front-end collection server, and a coordinate matrix A (Mu, mv) of a strong rainfall influence area is generated in the provincial region weather forecast map and the rainfall actual situation map. Wherein, the rainfall data accords with the reference QX/T52 part 8 of the ground meteorological observation standard: and the specification of precipitation observation, and the interface specification of the acquisition layer and the power geographic information system conforms to the relevant specification of Q/CSG 1204012 communication network production application interface technical specification.
Optionally, the power geographic information map is obtained through a power geographic information system.
Optionally, the wireless communication network comprises: GPRS, CDMA, 230MHz, WLAN, etc.
And the data layer is used for storing data related to prediction of power failure range of the flood disaster-causing power supply area.
Specifically, the data layer comprises a relational library and a real-time library server, wherein the relational library is used for storing a coordinate matrix A (M) which is superposed on a weather forecast chart and a rainfall situation chart to generate a heavy rainfall influence area u ，M v ) And a power supply station area coordinate matrix B (M) generated on the power geographical information chart x ，M y ) Data; the real-time library is used for storing daily rainfall and hourly rainfall data in short-term weather forecast information and power failure pre-evaluation accurate probability F of each power supply station area.
And the processing layer constructs a neural network for measuring the power failure range of the power supply station area due to the flood disaster through the application server according to the individual area of the power distribution facility where the power supply station area is located, the total area of each power supply station area, the power failure pre-evaluation accurate probability of each power supply station area, the flood grade, the station variable and the number of users, and the neural network solves and outputs the prediction result of the power failure area range due to the flood disaster.
The application layer is used for outputting and displaying a prediction result of the power failure area range caused by the flood disaster; and the forecast information of the power failure range of the relevant flood disaster-causing power supply area is issued to relevant technical personnel in relevant enterprises through a website server.
Optionally, the prediction result comprises: the method comprises the steps of identifying pictures of power supply districts at different power failure risk levels, individual areas M of each submerged district, power failure risk levels R of the corresponding power supply districts and total areas S of the submerged districts.
As an optional embodiment, the pre-acquisition server, the application server, the database server, and the website server are deployed in an information machine room of a provincial power grid production command center.
As an alternative embodiment, the application server is a NF5270M52U rack server configured with 4 CPUs in the 10 core to strong Xeon-Bank family.
As an alternative embodiment, the database server and the website server are NF5180M51U rack servers configured with 2 8-core Xeon E7V 4 series CPUs.
Example 3
According to another aspect of the embodiments of the present invention, there is further provided a system for predicting a power outage range of a flood disaster-induced power supply platform, and fig. 3 is a schematic diagram of the system for predicting a power outage range of a flood disaster-induced power supply platform according to the embodiments of the present invention, and as shown in fig. 3, the system for predicting includes: the system comprises a front-mounted acquisition server, a database server, an application server, a website server, an engineer station, an operator station, an internal network switch and an external network switch, and is deployed in a provincial power grid production command center.
The outer network switch is deployed in a communication machine room of a provincial power grid production command center and used for interacting data and instructions with a provincial weather station short-term and medium-term early warning center at the location, and data interaction and analysis accord with GB/T35965.1 part 1 of an emergency information interaction protocol: relevant regulations of early warning information.
The number of the preposed acquisition servers, the application servers and the website servers is 1, the number of the database servers is 2, and the preposed acquisition servers, the application servers and the website servers are all deployed in an information machine room of a provincial power grid production command center.
The pre-acquisition server, the website server and the database server of the flood inundation power supply area range prediction system are NF5280M52U rack servers, are configured with 2 8-core Xeon E7V 4 series CPUs, support hyper-threads, have the cache not less than 25 megabytes and have the original main frequency not less than 1.9 GHz; the memory is configured into a DDR4 type memory with the size not less than 128 gigabytes, and the total number of the maximum memory slots is not less than 64; the hard disk is configured into 4 serial connection SCSI hard disks with 600 gigabytes and 12000 rpm; the network card is provided with 8 independent 10/100/1000M-BaseT Ethernet ports.
The prepositive acquisition servers bear 1 set of acquisition layers, the number of the acquisition layers is 1, the acquisition layers are deployed in an information machine room of a provincial power grid production command center, the data exchange, the customization protocol, the deployment architecture, the data transmission safety specification and the protection mechanism of the prepositive acquisition servers meet the specifications of Q/CSG 1210017 technical specification of an internal and external network data safety exchange platform, Q/CSG 1210007 safety standard for data transmission and Q/CSG 1204009 technical specification for safety protection of a power monitoring system, and provincial levels of locations are acquired by an external network switchShort-term weather forecast information (including provincial region weather forecast images and rainfall actual conditions images) issued by the weather station short-term and medium-term early warning center, the rainfall level of the distribution and transformation position of each power supply station area, the power failure pre-evaluation accurate probability of each power supply station area and the data service for a database server (a relational database and a real-time database); the method comprises the steps of collecting an electric power geographic information graph and a power supply area coordinate matrix variable B (Mx, my) thereof in an intermediate library server of the electric power geographic information system through an intranet switch, and providing data service for a database server (a relational library). Collecting short-term weather forecast information from a provincial weather station short-term and medium-term early warning center, wherein the formats of characters, tables, images, data or other elements of the short-term and medium-term early warning information all accord with the specification of QX/T325 'power grid operation weather forecast early warning service product'; flood possible duration T over Refer to the QX/T341-2016 rainfall intensity rating for the specified calculation.
The database server bears a data layer, comprises 1 relational database and 1 real-time library server, is deployed in an information machine room of a provincial power grid production command center and is used for storing relevant data required by measuring the flooding power supply area range; the data exchange, the customization protocol, the data transmission safety specification and the protection mechanism of the system are in accordance with the regulations of GB/T20273 database management system safety technical requirement and Q/CSG 1210007 data transmission safety standard, and the relational database is used for storing a provincial region weather forecast picture, a rainfall actual situation picture, an electric power geographic information picture and a power supply station area coordinate matrix thereof in short-term weather forecast information; the real-time library is used for storing daily rainfall and hourly rainfall data in short-term weather forecast information and power outage pre-evaluation accurate probability F of a power supply area, and provides data service for the application server through an intranet switch.
The website servers bear application layers, the number of the website servers is 1, the website servers are deployed in an information machine room of a provincial power grid production command center, access control measures of the website servers are required to be in accordance with the specification of Q/CSG 1204009 safety protection technical specification of an electric power monitoring system, relevant elements such as a map and a graph of an early warning service graph are required to be in accordance with the specification of QX/T481 weather risk early warning service graph of medium and small river flood, mountain flood and geological disaster induced by strong rainfall, the individual area M of each submerged area, the graphic requirement of a total area S measurement graph of the submerged area, and the specification of a layout load SL/T483 flood risk graph compilation guide rule, flood data monitoring services are provided for all levels of electric power production monitoring command and emergency response related personnel through an intranet switch, and when a user accesses the website server of a flood power supply submerged area range prediction system, the access verification requirement of the system for the user is required to be in accordance with the specification of GB/T20272 safety technical requirement of an operating system.
The application servers bear processing layers, the number of the application servers is 1, the application servers are deployed in an information machine room of a provincial power grid production command center, the servers belong to an NF5270M52U rack type, 4 CPUs (central processing units) from 10 cores to a strong Xeon-Bank brand series are configured, hyper-threads are supported, the cache is not less than 20 megabytes, and the original dominant frequency is not less than 2.0 GHz; the memory is configured into a DDR4 type memory with the size not less than 128 gigabytes, and the total number of the maximum memory slots is not less than 64; the hard disk is configured as a 2 block 600 gigabyte, 12000 rpm serial attached SCSI hard disk.
The method comprises the steps that a flood submerging power supply area range neural network constructed by the neural network is deployed through an application server, the individual area M of each submerged area at a specific time t and the total area S of the submerged areas are input into an input layer, and the power failure pre-evaluation accuracy probability F of the corresponding power supply area is obtained; measuring the power failure risk level R of each power supply area in real time at the hidden layer; outputting the individual area M of each submerged platform area, the power failure risk level R of the power supply platform area corresponding to the individual area M and the total area S of the submerged platform area on an output layer; and provides data services for the web server through the switch.
The physical interface, protocol, interconnection and intercommunication and compatibility requirements of the intranet switch are in accordance with Q/CSG1204016.3 part 3: specification of data network equipment requirements for connecting database servers, application servers, web servers, engineer stations, operator stations, extranet switches via an electrical integrated data network consisting of optical fibers.
The number of the external network switches is 1, the external network switches are deployed in a communication machine room of a provincial power grid production command center, 24 10/100/1000 megabyte self-adaptive interfaces are configured, the exchange capacity is not less than 150 megabits/second, the forwarding capacity of two-layer and three-layer packets is not less than 95 megabits/second, the statistic number of concurrent flows is not less than 40 ten thousand, the forwarding delay of data messages is less than 1 millisecond, and LDP MD5, VRRP MD5 and NTP MD5 encryption authentication is supported.
The physical interfaces, protocols, interconnection and intercommunication and compatibility requirements of the internal network switch and the external network switch are in accordance with Q/CSG1204016.3 part 3: according to the specification of the technical requirements of data network equipment, the data interaction and instruction analysis of the external network switch conform to GB/T35965.1 part 1 of Emergency information interaction protocol: and (4) relevant regulations of early warning information. The internal network switch and the external network switch are used for connecting the database server, the application server, the website server, the engineer station, the operator station and the internal network switch through an electric comprehensive data network formed by optical fibers.
The number of engineer stations is 1, the engineer stations are deployed in a monitoring room of a provincial power grid production command center, and a Thinkstation P920 series double-channel workstation is selected.
The configuration principle and the technical requirements of the engineer station are in accordance with the requirements of Q/CSG 1203005 technical guide of electric power secondary equipment on a computer monitoring system, and the system is used for providing a service for maintaining a flood-submerging power supply station area range prediction system for a system administrator.
The number of the operator stations is 1, the operator stations are deployed in a monitoring room of a provincial power grid production command center, and the Thinkstation K-series workstations are selected.
The configuration principle and technical requirements of the operator station are in accordance with the requirements of Q/CSG 1203005 technical guide of electric power secondary equipment on a computer monitoring system, and the operator station is used for providing a service for developing flood emergency and early warning of the disaster damage degree of a power supply station area for system administrators and safety supervisors.
The physical interfaces, protocols, interconnection and intercommunication and compatibility requirements of the intranet switch and the flood inundation power supply station area range prediction system database server, the preposed acquisition server, the application server, the website server, the engineer station, the operator station and the extranet switch meet Q/CSG1204016.3 part 3: the requirements of configuration, setting and partition of a database server, a preposed acquisition server, an application server, a website server, an engineer station, an operator station, an internal network switch and an external network switch meet the requirements of Q/CSG 212001 safety protection management method of an electric power monitoring system and Q/CSG 1204009 safety protection technical specification of the electric power monitoring system. The main performance indexes of the flood-inundation power supply platform area range prediction system are in accordance with the 2 nd part of the software engineering production quality of GB/T16260.2: internal quality, GB/T16260.3 software engineering product quality part 3: external quality, Q/CSG1204016.3 data network specification part 3 data network device technical requirements. The safety function requirement of the flood-inundated power supply area range prediction system is in accordance with the regulation of GB/T20271 general safety technical requirement of information safety technical information system.
In the specific installation and deployment process of the flood inundation power supply area range prediction system, a prepositive acquisition server, a database server (relational library), a database server (real-time library), an application server and a website server are deployed in a screen cabinet in an information machine room of a provincial power grid production command center, and the number of various devices is one and only one. Secondly, an internal network switch and an external network switch are deployed in a communication machine room screen cabinet of a provincial power grid production command center, the number of various devices is one and only one, and after identity authentication and data encryption, short-term weather forecast information (including provincial regional weather forecast images and rainfall actual conditions images) of a provincial weather station short-term and medium-term early warning center at the location and an electric power geographic information image of an electric power geographic information system are remotely acquired through the external network switch. And thirdly, the engineer station and the operator station are deployed in a monitoring room of a provincial power grid production command center, and only one set of equipment is provided for remote monitoring and maintenance of the flood flooding power supply station area range prediction system.
In the specific monitoring and estimating process of the flood inundation power supply station area range prediction system, firstly, a provincial weather station short-term and middle-term early warning center starts an emergency response grade and a plan flow thereof according to the regulation of QX/T116' major weather disaster emergency response starting grade; reference to QX/T52 ground Meteorological Observation Specification NoPart 8: and (4) observing and obtaining short-term weather forecast information of the weather station short-term and medium-term early warning center according to the regulation of precipitation observation. Secondly, a provincial power grid production command center technician starts an emergency response grade and a plan thereof according to the 'major meteorological disaster emergency response starting grade' of QX/T116, and starts a flood flooding power supply station area range measuring process. Thirdly, measuring the flood submerging power supply area range by a flood submerging power supply area range prediction system, outputting an area map of each submerged area, power failure risk level E of the corresponding power supply area and a general map of the submerged areas, and obtaining the power failure risk level E of the corresponding power supply area according to the flood level X 1 And monitoring and studying and judging the power failure risk level E of each power supply area in real time, wherein the power failure range of each power supply area is the power failure range of the flood power supply area. Finally, technical decision suggestions for disposing flood risks of various power supply transformer areas are provided by technicians of each relevant power supply bureau by technicians of two-stage production command centers in province and region according to operation control principles and targets specified by DL/T1883 technical guidance for operation control of a power distribution network, Q/CSG 1205003 management standards for operation of medium and low voltage power distribution and Q/CSG 430043 post-emergency assessment service instruction, and operation modes can be adjusted, new flood prevention and flood prevention reinforcement measures can be adopted when necessary.
The main implementation in the specific treatment process is as follows:
technicians of enterprise production command centers of provincial power grids and ground power grids aim at power supply areas affected by heavy rainfall induced flooding, and power supply areas based on actual power failure in historical data and power failure data forecasting sensitivity R e And accuracy P r Predicting the power failure risk level R of the power supply area caused by heavy rainfall, solving the flood disaster-causing power failure area, proposing an emergency disposal measure suggestion, and providing a management and control list for technical personnel of a power supply bureau. The unit to which the power supply area belongs combines the waterlogging risk distribution map and the operation experience, and comprehensively organizes to carry out investigation and treatment on the power distribution facilities affected by waterlogging and water logging. The method mainly provides decision suggestions related to the repair and restoration sequence of each power supply area aiming at different power failure risk levels of the power supply area. For the power supply station area belonging to the power user assets, the technical personnel of each related power supply bureau give an early warning and give guidance or cooperate according to the conditionThe relevant regulations of GB/T37136 "operating and maintaining Specifications of Power consumer Power supply and distribution facilities" develop emergency treatment measures. In addition, the flood inundation power supply area range prediction system can provide useful information of planning, management and decision for distribution network flood early warning and prevention, linear regression is good in performance, and the basic functions can be summarized as follows:
(1) Collecting power supply area spatial data;
(2) Managing, converting and sharing spatial data of the power supply area;
(3) Processing and editing the graphs of the power failure areas caused by flooding of the power distribution network;
(4) Carrying out space analysis, derivation and query on a flood-inundated power supply area;
(5) And displaying and outputting the risk level within the range of the flood submerging power supply area.
Embodiment 4, according to another aspect of the embodiments of the present invention, a computer-readable storage medium is further provided, where the computer-readable storage medium includes a stored program, and when the program runs, a device in which the computer-readable storage medium is located is controlled to execute any one of the above methods for predicting a power outage range of a flood disaster induced power supply platform area.
Optionally, in this embodiment, the computer-readable storage medium may be located in any one of a group of computer terminals in a computer network or in any one of a group of mobile terminals, and the computer-readable storage medium includes a stored program.
Optionally, the program when executed controls an apparatus in which the computer-readable storage medium is located to perform the following functions: acquiring short-term weather forecast information, and judging whether to start a process for measuring power failure of a power supply area due to disaster or not; when the process needs to be started, acquiring an electric power geographic information map, and judging whether a power supply area in the electric power geographic information map is in a boundary range of a heavy rainfall influence area or not; when the power supply areas are located in the boundary range of the strong rainfall influence area, calculating the individual area of the power distribution facility where the power supply areas affected by the flood disaster are located, the total area of each power supply area and the power failure pre-evaluation accurate probability of each power supply area respectively; according to the individual area of the power distribution facility where the power supply areas affected by the flood disasters are located, the total area of each power supply area, the power outage pre-evaluation accuracy probability of each power supply area, the flood level, the station variable and the number of users, a neural network for measuring the power outage range of the power supply areas due to the flood is constructed, and the neural network solves and outputs the prediction result of the power outage range of the power supply areas due to the flood disasters.
Example 5
According to another aspect of the embodiments of the present invention, there is also provided a processor, configured to execute a program, where the program executes any one of the above methods for predicting a power outage range of a flood disaster-induced power supply area when the program runs.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program which is stored on the memory and can run on the processor, wherein the step of the prediction method of the power failure range of the flood disaster-causing power supply area is realized when the processor executes the program.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, a division of a unit may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, and the indirect coupling or communication connection of the units or modules may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be considered as the protection scope of the present invention.
Claims (10)
1. A method for predicting the power failure range of a flood disaster-causing power supply area is characterized by comprising the following steps:
acquiring short-term weather forecast information, and judging whether to start a process for measuring power failure of a power supply area due to disaster or not;
when the process needs to be started, acquiring an electric power geographic information map, and judging whether a power supply area in the electric power geographic information map is in a boundary range of a heavy rainfall influence area or not;
when the power supply areas are located in the boundary range of the strong rainfall influence area, calculating the individual area of the power distribution facility where the power supply areas affected by the flood disaster are located, the total area of each power supply area and the power failure pre-evaluation accurate probability of each power supply area respectively;
according to the individual area of a power distribution facility where power supply areas affected by flood disasters are located, the total area of each power supply area, the power outage pre-evaluation accuracy probability of each power supply area, the flood grade, the station variable and the number of users, a neural network for measuring the power outage range of the power supply areas due to the flood is constructed, and the neural network is used for solving and outputting the prediction result of the power outage range of the power supply areas due to the flood disasters.
2. The method for predicting the outage range of a flood disaster-causing power supply area according to claim 1, wherein the prediction result comprises: the individual area of the power distribution facility where the power supply platform area influenced by the flood disaster is located, the total area of each power supply platform area, the power failure risk level of the power supply platform area caused by heavy rainfall and a real-time display map.
3. The method for predicting the power outage range of the flood disaster-causing power supply station area according to claim 1, wherein the process of judging whether to start measuring the power outage of the power supply station area due to disaster or not comprises the following steps: and judging whether the rainfall in the set time exceeds a threshold value.
4. The method for predicting the power outage range of a flood disaster-causing power supply area according to claim 1, wherein a spatial association rule is used to determine whether the power supply area is in a boundary range of a heavy rainfall influence area.
5. The method for predicting the power outage range of the flood disaster-causing power supply area according to claim 4, wherein the step of judging whether the power supply area is in the boundary range of the heavy rainfall influence area by using the spatial association rule specifically comprises the following steps:
condition section A (M) u ，M v ) Is a provincial regionGenerating point coordinate clustering of strong rainfall influence area by using domain weather forecast graph and rainfall condition graph, and determining confidence interval B (M) x ，M y ) Generating point coordinate clusters of a power supply area for the electric power geographic information map;
for rule A → B, the conditional and confidence interval calculation formulas are:
Support(A→B)＝Support(A∪B)＝P(A∪B)
Feasible(A→B)＝P(B|A)
the expression for judging whether the power supply area is in the boundary range of the heavy rainfall influence area is as follows:
6. the method for predicting the power outage range of the flood disaster-causing power supply area according to claim 1, wherein the step of calculating the individual area of the power distribution facility where the power supply area affected by the flood disaster is located specifically comprises:
the individual area M of the distribution facility at power supply district place that flood disaster influenced is for using district distribution transformer of place to be the area of geometric center to distribution transformer's power supply boundary within range, and the individual area M of the distribution facility at power supply district place that flood disaster influenced is irregular shape, and has n boundary points, and the expression is:
in the above formula, x i 、y i Is the plane coordinates of each boundary point.
7. The method for predicting the blackout range of a flood disaster power supply area according to claim 1, wherein the total area of the power supply area affected by the flood disaster is the sum of the individual areas of the power distribution facilities where the power supply area affected by the flood disaster is located.
8. The method for predicting the outage range of a flood disaster-causing power supply area according to claim 1, wherein the expression of the outage pre-evaluation accuracy probability F of the power supply area is as follows:
in the above formula, β represents a weighting coefficient for the grid of the power outage history, R e Is the proportion, i.e. sensitivity, P, of all the actual blackout grid samples that is correctly identified as a blackout grid r The accuracy is the proportion of the actual blackout grid in the samples identified as the blackout grid.
9. The method for predicting power outage coverage of a flood disaster power supply area according to claim 1, wherein the neural network input comprises an input layer, a hidden layer and an output layer,
the input layer nodes comprise the individual area of the power distribution facility where each power supply area affected by the flood disasters exists, the total area of each power supply area and the power failure pre-evaluation accurate probability of the corresponding power supply area within the possible duration of the flood;
the hidden layer is used for solving the power failure risk level of each power supply area caused by heavy rainfall;
the output layer is used for outputting display diagrams for identifying power supply areas in different power failure risk levels.
10. A prediction system for power failure range of flood disaster-causing power supply transformer area is characterized by comprising:
the acquisition layer is used for acquiring short-term weather forecast information through the front-end acquisition server and judging whether to start a process for measuring power failure of the power supply area due to disasters; when the process needs to be started, acquiring an electric power geographic information map, and judging whether a power supply area in the electric power geographic information map is in a boundary range of a heavy rainfall influence area or not; meanwhile, the preposed acquisition server is positioned in a safe access area, and the safe access area meets the network safety requirement of accessing data when a communication mode of a public communication network and a wireless communication network is used, wherein the public communication network does not comprise the Internet;
the data layer comprises data related to power failure prediction of the flood disaster-causing power supply area;
the processing layer constructs a neural network for measuring the power supply area power failure range due to the flood disaster through the application server according to the individual area of the power distribution facility where the power supply area is located, the total area of each power supply area, the power failure pre-evaluation accurate probability of each power supply area, the flood level, the station variable number and the number of users, wherein the power supply area is influenced by the flood disaster; and
the application layer is used for outputting and displaying a prediction result of the power failure area range caused by the flood disaster; and the prediction information of the power failure range of the power supply area caused by the flood is issued to related technicians in related enterprises through the website server.
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