CN113191687A - Elastic power distribution network panoramic information visualization method and system - Google Patents
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Abstract
The invention discloses a method and a system for visualizing panoramic information of an elastic power distribution network, which are used for monitoring system data information in real time, calculating and displaying dynamic elastic indexes of the power distribution network, carrying out fine early warning on the system according to index thresholds and marking risk elements; and (3) simulating extreme disaster weather in an off-line manner, calculating and displaying the comprehensive elasticity score of the power distribution network, sorting the importance of the power distribution network elements, and marking the power distribution network element with the highest importance. The invention reflects the elastic change of the power distribution network through two levels of off-line evaluation, on-line monitoring and the like, and further performs visual display through a panoramic information visualization system, thereby not only guiding an element strengthening scheme and a resource allocation strategy from a planning level, but also assisting workers to perform emergency scheduling and quick recovery from an operation level.
Description
Technical Field
The invention belongs to the technical field of elastic power distribution networks, and particularly relates to a panoramic information visualization method and system for an elastic power distribution network.
Background
The safety and reliability of power systems are essential requirements for maintaining normal operation in modern society. However, extreme events have become more frequent in recent years, causing a large loss in power supply. Therefore, there is a need to actively construct a resilient distribution network to improve the system's ability to handle extreme events.
In order to visually reflect the comprehensive elasticity level of a system and improve the analysis and processing efficiency of the system on extreme events, an elastic power distribution network panoramic information visualization system needs to be developed urgently, real-time monitoring and fine early warning of the system state under the extreme events are achieved, meanwhile, the comprehensive elasticity of the system is evaluated offline, and a foundation is laid for researching elasticity improvement strategies.
Disclosure of Invention
The invention aims to solve the technical problem that in order to overcome the defects in the prior art, the invention provides the elastic power distribution network panoramic information visualization method and system, and various functions such as online monitoring and early warning, offline elastic evaluation and the like are realized through program modular design.
The invention adopts the following technical scheme:
a panoramic information visualization method for an elastic power distribution network comprises the following steps:
s1, monitoring system data information in real time, calculating and displaying dynamic elasticity indexes of the power distribution network, carrying out fine early warning on the system according to index thresholds, and marking risk elements of the power distribution network;
s2, simulating extreme disaster weather in an off-line mode, calculating and displaying comprehensive elastic scores of the power distribution network, sorting the importance of the power distribution network elements, marking the power distribution network elements with the highest importance, and realizing elastic power distribution network panoramic information visualization.
Specifically, the real-time monitoring of system data information and the fine early warning of the system according to the index threshold specifically include:
acquiring system state information through a space-time cloud big data platform, displaying by adopting a comprehensive live monitoring index, sequencing the live monitoring index according to a background set threshold value, and preferentially displaying risky equipment; and calculating the dynamic elastic index of the system by combining the meteorological monitoring and prediction data and the power distribution network monitoring data, coloring, sequencing and displaying the index, predicting the overall elastic change trend of the time sequence fault scene in a period of time in the future according to the dynamic elastic index of the power distribution network, and forming a prediction function curve.
Further, the weight of the live monitoring index is calculated by adopting an entropy weight method, meanwhile, the threshold values of different early warning levels are judged according to a grey correlation analysis method, the system visualization platform colors the live monitoring index according to the set threshold value, sets and sorts safe, low-risk, high-risk and out-of-limit colors, and preferentially displays the equipment with risks.
Further, the live monitoring indicators include: load rates of lines and transformers, medium-low voltage bus voltages and load data of each transformer area; the dynamic elasticity index includes: the system maximum load loss rate index is the ratio of the maximum loss load of the power distribution network caused during a disaster period to the load of the power distribution network before the disaster occurs; the system loss electric quantity index is the sum of the electric quantity lost by the power distribution network in a disaster; the system has comprehensive power restoration time indexes, and the time required by the power distribution network from the beginning of load loss to the restoration of power supply of all loads during a disaster is shortened.
Further, the forming of the pre-estimated function curve specifically includes:
determining the failure rate of the element according to a disaster scene, coloring the failure rate of the element according to a given threshold value, and displaying the failure rate on a platform from high to low; and calculating the load loss probability indexes of the loads of all the distribution areas by adopting a Monte Carlo simulation method according to the reliability evaluation thought of the power distribution system after the fault rate is obtained, coloring, sequencing and displaying the indexes, generating an element outage scene according to weather forecast data and power grid live data sampling, simulating the load special supply and recovery process and updating, calculating the integral elastic change trend of the time sequence fault scene with the maximum probability in a period of time in the future, and forming an estimation function curve.
Specifically, the extreme disaster weather of off-line simulation carries out the importance sequencing to the distribution network component and specifically does:
simulating extreme disasters by using a disaster simulation generator, and simultaneously simulating and counting the loss condition of the power distribution network by adopting Monte Carlo to perform off-line disaster simulation evaluation; obtaining an element repair sequence which enables the accumulated load shedding amount of the system to be minimum through simulating the recovery process of the system after an extreme event, and analyzing the importance degree of elements in the power distribution network; and on the basis of disaster simulation influence evaluation, calculating the comprehensive elastic score condition of the system by combining the elastic resource reserves of the system through an analytic hierarchy process.
Further, the off-line disaster simulation and evaluation specifically comprises:
the disaster simulation generator simulates typhoon, rainstorm and thunderstorm, parameters of extreme weather needing to be evaluated are given, convergence criteria of Monte Carlo simulation are set, off-line disaster simulation evaluation is carried out, according to the parameters of the extreme disasters, the disaster simulation generator is used for generating disasters, then fault conditions of the power distribution network in the disaster occurrence process are obtained by using vulnerability curves of all elements in the power distribution network, influences on evaluation results caused by randomness of the disasters and the power distribution network faults are eliminated through repeated simulation, and the evaluation results are output after the convergence criteria are met.
Further, the element repair sequence for minimizing the cumulative load shedding amount of the system specifically includes:
analyzing the distribution lines through the recovery process of the simulation system after the extreme event, and simulating the recovery process of the system after the disaster under the set load level and meteorological environment conditions when the importance degree is sequenced to obtain an element recovery sequence which enables the accumulated load shedding amount of the system to be minimum; sampling the fault scenes by a non-time sequence Monte Carlo sampling method to obtain a plurality of groups of fault scenes; solving an optimization model which minimizes the load shedding amount for the fault scene to obtain the repair time of each element in each scene, forming a distribution function of the repair time of each element, comparing and sequencing the distribution functions by a Copland sequencing method, and finally sequencing the Copland score results of each element to obtain an evaluation result of the element importance; and implementing pre-disaster strengthening and post-disaster first-aid repair strategies for important elements according to the importance of the elements.
Further, the calculation of the comprehensive elasticity score of the system by the analytic hierarchy process specifically comprises: calculating an elastic effect index generated by the elastic resource according to the basic elastic resource data of the power distribution network input by a user; and processing the index importance set by the user through an analytic hierarchy process, calculating the index weight, and finally calculating the comprehensive elasticity score of the system.
Another technical solution of the present invention is an elastic power distribution network panoramic information visualization system, including:
the online monitoring and early warning module is used for monitoring system data information in real time, calculating and displaying dynamic elastic indexes of the power distribution network, carrying out fine early warning on the system according to index thresholds and marking risk elements;
and the offline elastic evaluation module is used for simulating extreme disaster weather offline, calculating and displaying the comprehensive elastic scores of the power distribution network, sorting the importance of the power distribution network elements, marking the power distribution network element with the highest importance and realizing the visualization of the panoramic information of the elastic power distribution network.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention discloses a panoramic information visualization method for an elastic power distribution network, which can realize the following functions through modular design: firstly, before an extreme disaster occurs, an offline elasticity evaluation module is arranged to simulate the extreme disaster offline and evaluate the comprehensive elasticity level of the power distribution network, so as to rank the importance of elements, further strengthen weak links of the system and optimize and configure elastic resources; after the extreme disasters occur, the online monitoring and early warning module is arranged to predict the system fault scene through online monitoring of the extreme disasters and live information of the power distribution network, and mark out equipment with large risks, so that reasonable emergency measures are taken, and the safe operation of the power grid is guided in all directions.
Further, the running state of the system is monitored in real time through real-time monitoring and evaluation, live monitoring indexes are calculated and displayed, and risky equipment is judged according to the indexes; and a risk evaluation and early warning module is arranged for performing rolling update and prediction on the running state of the system and predicting the state change condition of the system in a period of time in the future according to the dynamic elasticity index.
Further, the weight of the live monitoring index is calculated by adopting an entropy weight method, the larger the entropy weight is, the larger the effect of the index on the comprehensive early warning is, the difference degree between the indexes can be intuitively and effectively reflected, the relationship between the index and the overall development situation of the system is analyzed by adopting a gray correlation analysis method, the higher the gray correlation degree is, the more serious the comprehensive early warning level is, and index thresholds in different risk ranges can be designed according to the relationship.
Furthermore, live monitoring indexes are set for determining real-time state information of lines, transformers, buses and loads, the risk level of the equipment can be judged according to the live monitoring indexes, and dynamic elastic indexes are set for dynamically evaluating the risk level of the elastic power distribution network under the current disaster condition according to the live data of the system.
Furthermore, the purpose of calculating the overall elastic change trend in a period of time in the future according to the dynamic elastic indexes is to sample and generate element fault moments and generate random outage scenes according to power grid live data and weather forecast data, simulate the load supply and power supply recovery process through a Monte Carlo method, and continuously update the load supply and power supply recovery process in a rolling manner, so that an estimated function curve is obtained.
Further, a representative disaster characteristic is generated by using a disaster simulation generator, and the fault condition of the power distribution network is analyzed, so that the vulnerability of the elastic power distribution network under a typical disaster condition is evaluated; the device is characterized in that an element importance sorting module is arranged for analyzing the importance of each element in the power distribution network, and the influence of disasters on the power distribution network is effectively reduced by reinforcing or preemptively repairing the important elements; and the comprehensive elasticity evaluation module is arranged for evaluating the comprehensive elasticity level of the power distribution network, and the comprehensive elasticity score of the system is obtained according to the disaster simulation condition and the elastic resource configuration condition, so that the comprehensive elasticity evaluation module can be used for comparing the elasticity levels of a plurality of power distribution networks.
Furthermore, the purpose of evaluating the disaster simulation influence through the disaster simulation generator is to eliminate the influence of the randomness of the disasters and the faults of the power distribution network on the evaluation result by repeatedly simulating the operation state and the fault conditions of the elastic power distribution network under typical common disasters.
Furthermore, the purpose of ranking the importance by using the copperand method is to solve an optimization model which minimizes the load shedding amount under various fault scenes to form a distribution function of the repair time of each element, so that the comprehensive influence of each element on the system under different fault scenes can be determined.
Furthermore, by combining disaster characteristic conditions and resource allocation conditions, an analytic hierarchy process is adopted to obtain the comprehensive elasticity level of the system, and the influence degree of disaster characteristics and resource adequacy on the elasticity of the system is further determined.
In summary, the invention reflects the elastic change of the power distribution network through two levels of off-line evaluation, on-line monitoring and the like, and further performs visual display through the panoramic information visualization system, so that the element strengthening scheme and the resource allocation strategy can be guided from a planning level, and the emergency scheduling and quick recovery of workers can be assisted from an operation level.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a flow chart of an on-line monitoring and early warning module according to the present invention;
FIG. 2 is a flow chart of an offline elasticity evaluation module according to the present invention;
FIG. 3 is a diagram showing an interface of an on-line monitoring and early warning module according to the present invention;
FIG. 4 is a diagram illustrating an interface of an off-line elasticity evaluation module according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
The invention discloses a panoramic information visualization method for an elastic power distribution network, which comprises the following steps of:
s1, monitoring system data information in real time, calculating and displaying dynamic elasticity indexes of the power distribution network, and performing fine early warning on the system according to index thresholds;
s101, obtaining system state information through a space-time cloud big data platform, displaying by adopting comprehensive live monitoring indexes, sequencing the live monitoring indexes according to a background set threshold value, and preferentially displaying risky equipment;
the live monitoring indexes mainly comprise: load rate information (and average load rate information) of the line and the transformer, medium and low voltage bus voltage information, and load information of each transformer area.
S102, combining meteorological monitoring and prediction data and power distribution network monitoring data, calculating dynamic elastic indexes of the system, coloring, sequencing and displaying the indexes, predicting the overall elastic change trend of a time sequence fault scene with the highest probability in a period of time in the future, and forming an estimated function curve of the system.
S2, simulating extreme disaster weather in an off-line mode, evaluating long-term comprehensive elasticity of the power distribution network, sorting importance of elements of the power distribution network, and realizing panoramic information visualization of the elastic power distribution network.
S201, simulating three extreme disasters, namely typhoon, rainstorm, thunderstorm and the like, by using the disaster simulation generator, and simultaneously simulating and counting the loss condition of the power distribution network by adopting Monte Carlo to perform off-line disaster simulation evaluation.
S202, obtaining an element repairing sequence with the minimum accumulated load shedding amount of the system through simulating the recovery process of the system after the extreme event, and analyzing the importance degree of elements in the power distribution network.
And S203, on the basis of disaster simulation influence evaluation, calculating the comprehensive elasticity score condition of the system by combining the elastic resource reserves of the system through an analytic hierarchy process.
In another embodiment of the present invention, a system for visualizing panoramic information of an elastic power distribution network is provided, where the system can be used to implement the method for visualizing panoramic information of an elastic power distribution network, and specifically, the system for visualizing panoramic information of an elastic power distribution network includes an online monitoring and early warning module and an offline elastic evaluation module.
The online monitoring and early warning module is used for monitoring system data information in real time, calculating and displaying dynamic elastic indexes of the power distribution network, carrying out fine early warning on the system according to index thresholds and marking risk elements;
and the offline elastic evaluation module is used for simulating extreme disaster weather offline, calculating and displaying the comprehensive elastic scores of the power distribution network, sorting the importance of the power distribution network elements, marking the power distribution network element with the highest importance and realizing the visualization of the panoramic information of the elastic power distribution network.
In yet another embodiment of the present invention, a terminal device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for the operation of the elastic power distribution network panoramic information visualization method, and comprises the following steps:
monitoring system data information in real time, calculating and displaying dynamic elastic indexes of the power distribution network, carrying out fine early warning on the system according to index thresholds, and labeling risk elements; the method comprises the steps of simulating extreme disaster weather in an off-line mode, calculating and displaying comprehensive elastic scores of the power distribution network, sorting the importance of power distribution network elements, marking the power distribution network element with the highest importance, and achieving visualization of panoramic information of the elastic power distribution network.
In still another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a terminal device and is used for storing programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor can load and execute one or more instructions stored in the computer readable storage medium to realize the corresponding steps of the method for visualizing the panoramic information of the elastic distribution network in the embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and perform the steps of:
monitoring system data information in real time, calculating and displaying dynamic elastic indexes of the power distribution network, carrying out fine early warning on the system according to index thresholds, and labeling risk elements; the method comprises the steps of simulating extreme disaster weather in an off-line mode, calculating and displaying comprehensive elastic scores of the power distribution network, sorting the importance of power distribution network elements, marking the power distribution network element with the highest importance, and achieving visualization of panoramic information of the elastic power distribution network.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, data information related to the elastic distribution network is obtained in real time from a database of a space-time cloud big data platform of the power supply bureau of guangzhou, and the data information includes space-time information, state information and special information. The system comprises time-space information, state information and data fusion technology, wherein the time-space information is the geographic position and the time point of various information, the state information is the attribute value of the information, and the special information is information related to a disaster, and the various information is associated together through the data fusion technology to clarify the disaster and the real-time state of various devices; and then, directly transmitting and displaying the data information in a visual platform, or forming a more comprehensive live monitoring index for displaying after brief algebraic operation. The designed live monitoring indexes to be displayed mainly comprise: load rate information (and average load rate information) of lines and transformers, medium and low voltage bus voltage information, load information of each transformer area, and the like.
After the indexes are obtained through calculation, the system visualization platform colors the live monitoring indexes (green-safe, yellow-low risk, red-high risk, purple-out-of-limit and other abnormal states) according to the threshold set by the background, and displays the equipment with risks preferentially in sequence. When the threshold is set, the weight of each index is calculated by adopting an entropy weight method, and meanwhile, the thresholds of different early warning levels are judged according to a gray correlation analysis method, wherein the higher the gray correlation degree is, the more serious the comprehensive early warning level is.
And (4) performing online resilience evaluation on the elastic power distribution network by combining risk evaluation and early warning with real-time meteorological data, meteorological prediction data (a prediction interval of 15min/30min/1 h) and power distribution network real-time monitoring data, and calculating dynamic elastic indexes of the elastic power distribution network. The vulnerability model of the elastic distribution network element set in the system can be used for depicting the condition dependence relationship between the disaster-causing factor and the disaster-bearing body, so that the failure rate of the distribution network element affected by various extreme natural disasters can be calculated, and related data characteristics are extracted by adopting a data-driven method to establish the relation between the two. After the element failure rate is determined, coloring the element failure rate according to a given threshold value, and displaying the element failure rate on the platform from high to low according to the failure rate; and calculating the load loss probability index LOLP of each distribution area load by adopting a Monte Carlo simulation method according to the idea of reliability evaluation of the power distribution system after the fault rate is obtained, and coloring, sequencing and displaying the index in the same way. And finally, calculating the overall elastic change trend of the time sequence fault scene with the maximum probability in a future period of time according to the dynamic elastic indexes of the power distribution network, thereby forming an estimated function curve of the system.
Referring to fig. 2, an offline calculation module simulates the common extreme weather, evaluates the long-term comprehensive elasticity of the distribution network, and ranks the importance of the elements in the distribution network through elasticity capability evaluation.
Three common extreme disasters to the power distribution network are evaluated through disaster influence simulation: and carrying out Monte Carlo simulation on typhoon, rainstorm and thunderstorm, and counting the loss condition of the power distribution network to obtain modules of the influence condition of various extreme events on the power distribution network. And giving parameters (such as a regression period, a cyclone grade and other typhoon intensity parameters) of extreme weather needing to be evaluated, and setting a convergence criterion of Monte Carlo simulation, so that the off-line disaster simulation evaluation can be carried out. And the background utilizes the disaster simulation generator to generate the disaster according to the parameters of the extreme disaster, and then utilizes the vulnerability curves of each element in the Guangzhou power distribution network to obtain the fault condition of the power distribution network in the disaster generating process, and the influence of the randomness of the disaster and the power distribution network fault on the evaluation result is eliminated by repeated simulation. And outputting an evaluation result after meeting the convergence criterion.
The module analyzes the importance degree of elements (mainly distribution lines) in the Guangzhou power distribution network through the recovery process of the element importance degree evaluation simulation system after an extreme event, and simulates the post-disaster repair process of the system under a certain load level and a certain meteorological environment condition when the importance degree is sequenced to obtain an element repair sequence which enables the accumulated load shedding amount of the system to be minimum. And sampling the fault scenes by a non-time sequence Monte Carlo sampling method to obtain a plurality of groups of fault scenes. And solving the optimization model which minimizes the load shedding amount for the fault scenes to obtain the repair time of each element in each scene, forming a distribution function of the repair time of each element, comparing and sequencing the distribution functions by a Copland sequencing method, and sequencing the Copland score results of each element to obtain the evaluation result of the element importance. After the importance of the elements is sequenced, scheduling personnel can carry out pre-disaster prevention strategies such as reinforcement or maintenance personnel/material storage on the important elements in advance, so that the influence of extreme disasters on the power distribution network is effectively reduced.
And further summarizing indexes of the power grid, the load, the weather, the elastic resource and the emergency management on the basis of the disaster simulation influence evaluation through the system comprehensive elasticity evaluation, and calculating the indexes to obtain a module of the system comprehensive elasticity score. According to the large category of indexes, the method is divided into two independent parts: disaster simulation evaluation and elastic resource evaluation. Disaster simulation evaluation, as described in detail above, the elastic resource evaluation requires a user to input some basic elastic resource indicators of the distribution network, such as the capacity of the flexible controllable load, the number of emergency power generation cars, the proportion of the distributed power sources, and the like, and then the system calculates the elastic effect indicators generated by these elastic resources. And finally, processing the index importance set by the user through an analytic hierarchy process to calculate the index weight, and finally calculating the comprehensive elasticity score of the system.
Referring to fig. 3, submodules of an equipment load rate monitoring function, a user load monitoring function, an equipment failure risk sorting, a user load loss risk sorting, a system pre-estimation function level curve, and the like are shown.
The equipment load rate monitoring function displays the load rate monitoring value information of each numbered equipment, the load rate degree evaluation of the equipment is marked by different colors, four evaluation results of 'normal', 'light', 'moderate', 'heavy' are contained, and the evaluation result is 'normal' when the evaluation result is less than 50; 50 to 80 are "light"; 80 to 100 is "medium"; more than 100 is "severe". And specific information can be viewed by placing the mouse on the equipment with the corresponding number. The load rate of the plant 6 shown in the legend is 169, the current rating is: and (4) heavy.
The user load monitoring function displays the load monitoring value information of each numbered load point, and the load degree evaluation of each load point is marked by different colors, so that the user load monitoring function totally comprises four evaluation results of 'normal', 'light', 'moderate', 'heavy', and 'normal'; 50 to 80 are "light"; 80 to 120 are "medium"; above 120 is "severe". And specific information can be viewed by placing the mouse on the load point with the corresponding number. Load point 9 shown in the legend is load 63, and the current rating is: mild in nature.
The equipment failure risk sorting function displays the failure risk grades of the numbered equipment, and sorts the equipment according to the risk from high to low. And specific information can be viewed by placing the mouse on the equipment with the corresponding number. The device 9 shown in the legend has a risk value of 54 and a risk rating of two.
The user load loss risk sorting function displays the load loss risk grades of all the load points, and sorts the load loss risk grades according to the sequence of risks from high to low. And specific information can be viewed by placing the mouse on the load point with the corresponding number. The load 27 loss risk value shown in the legend is 7 with a risk rating of one.
The system pre-estimation function level curve shows the pre-estimated loss electric quantity and the pre-estimated maximum load loss rate of each time point all day of the system. Specific information can be viewed by placing a mouse on the curve at the corresponding time point. In the legend, the estimated loss electric quantity value of 18:00 at 3, 15 and 2020 is 55, and the estimated maximum loss load rate is 16.
Referring to fig. 4, disaster simulation evaluation, importance ranking, and comprehensive elasticity evaluation are shown.
The disaster simulation evaluation is mainly used for evaluating the influence degree of different disasters on the power distribution network to simulate, and the evaluation result is displayed by indexes and graphs. The types of disasters that can be evaluated currently are: tropical cyclone, heavy precipitation, thunderstorm. The disaster influence simulation evaluation result is reflected by three indexes: the method comprises the following steps of system load loss proportion expectation, system comprehensive power restoration time expectation and system power loss electric quantity expectation, wherein each index has three attributes of 'expectation value', 'at-risk value' and 'tail-risk value', and finally, a probability distribution curve of each index in each time period is given.
And the component importance evaluation is to sort the devices according to the importance degree evaluation method, and to place a mouse on the device with the corresponding number to view specific information. The device 3 shown in the figure has an importance value of 17 and an importance level of one level.
The comprehensive elasticity evaluation of the system shows comprehensive elasticity indexes comprising probability indexes and elasticity resource indexes. The probability indexes comprise loss load ratio expectation, comprehensive power restoration duration expectation and annual power loss expectation; the elastic resource indexes comprise a flexible controllable load proportion, the number and the capacity of power generation or energy storage vehicles, a distributed power supply proportion and a network reconfiguration switch proportion. And finally obtaining the comprehensive elasticity score of the system according to the index setting.
In summary, the elastic power distribution network panoramic information visualization method and system provided by the invention realize various functions such as online monitoring and early warning and elastic capability assessment through program modular design. Before extreme disasters occur, the extreme disasters are simulated off line and the comprehensive elasticity level of the power distribution network is evaluated, the importance of elements is sorted, weak links of the system are further strengthened, and elastic resources are optimally configured; after the extreme disasters occur, the system fault scene is predicted by monitoring the extreme disasters and the live information of the power distribution network on line, and equipment with large risks is marked, so that reasonable emergency measures are taken, and the safe operation of the power grid is guided in all directions.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (10)
1. An elastic power distribution network panoramic information visualization method is characterized by comprising the following steps:
s1, monitoring system data information in real time, calculating and displaying dynamic elasticity indexes of the power distribution network, carrying out fine early warning on the system according to index thresholds, and marking risk elements of the power distribution network;
s2, simulating extreme disaster weather in an off-line mode, calculating and displaying comprehensive elastic scores of the power distribution network, sorting the importance of the power distribution network elements, marking the power distribution network elements with the highest importance, and realizing elastic power distribution network panoramic information visualization.
2. The method according to claim 1, wherein the monitoring of system data information in real time and the fine early warning of the system according to the index threshold specifically comprise:
acquiring system state information through a space-time cloud big data platform, displaying by adopting a comprehensive live monitoring index, sequencing the live monitoring index according to a background set threshold value, and preferentially displaying risky equipment; and calculating the dynamic elastic index of the system by combining the meteorological monitoring and prediction data and the power distribution network monitoring data, coloring, sequencing and displaying the index, predicting the overall elastic change trend of the time sequence fault scene in a period of time in the future according to the dynamic elastic index of the power distribution network, and forming a prediction function curve.
3. The method as claimed in claim 2, wherein the weight of the live monitoring index is calculated by using an entropy weight method, the threshold values of different early warning levels are judged according to a grey correlation analysis method, the system visualization platform colors the live monitoring index according to the set threshold values, sets and sorts safe, low-risk, high-risk and out-of-limit colors, and preferentially displays the equipment with risks.
4. The method of claim 2, wherein the live monitoring metrics comprise: load rates of lines and transformers, medium-low voltage bus voltages and load data of each transformer area; the dynamic elasticity index includes: the system maximum load loss rate index is the ratio of the maximum loss load of the power distribution network caused during a disaster period to the load of the power distribution network before the disaster occurs; the system loss electric quantity index is the sum of the electric quantity lost by the power distribution network in a disaster; the system has comprehensive power restoration time indexes, and the time required by the power distribution network from the beginning of load loss to the restoration of power supply of all loads during a disaster is shortened.
5. The method according to claim 2, wherein forming the predicted functional curve is specifically:
determining the failure rate of the element according to a disaster scene, coloring the failure rate of the element according to a given threshold value, and displaying the failure rate on a platform from high to low; and calculating the load loss probability indexes of the loads of all the distribution areas by adopting a Monte Carlo simulation method according to the reliability evaluation thought of the power distribution system after the fault rate is obtained, coloring, sequencing and displaying the indexes, generating an element outage scene according to weather forecast data and power grid live data sampling, simulating the load special supply and recovery process and updating, calculating the integral elastic change trend of the time sequence fault scene with the maximum probability in a period of time in the future, and forming an estimation function curve.
6. The method according to claim 1, wherein extreme disaster weather is simulated offline, and the ranking of importance of the distribution network elements is specifically:
simulating extreme disasters by using a disaster simulation generator, and simultaneously simulating and counting the loss condition of the power distribution network by adopting Monte Carlo to perform off-line disaster simulation evaluation; obtaining an element repair sequence which enables the accumulated load shedding amount of the system to be minimum through simulating the recovery process of the system after an extreme event, and analyzing the importance degree of elements in the power distribution network; and on the basis of disaster simulation influence evaluation, calculating the comprehensive elastic score condition of the system by combining the elastic resource reserves of the system through an analytic hierarchy process.
7. The method according to claim 6, wherein performing offline disaster simulation evaluation specifically comprises:
the disaster simulation generator simulates typhoon, rainstorm and thunderstorm, parameters of extreme weather needing to be evaluated are given, convergence criteria of Monte Carlo simulation are set, off-line disaster simulation evaluation is carried out, according to the parameters of the extreme disasters, the disaster simulation generator is used for generating disasters, then fault conditions of the power distribution network in the disaster occurrence process are obtained by using vulnerability curves of all elements in the power distribution network, influences on evaluation results caused by randomness of the disasters and the power distribution network faults are eliminated through repeated simulation, and the evaluation results are output after the convergence criteria are met.
8. The method according to claim 6, wherein the element repairing sequence for minimizing the accumulated system load cut is embodied as:
analyzing the distribution lines through the recovery process of the simulation system after the extreme event, and simulating the recovery process of the system after the disaster under the set load level and meteorological environment conditions when the importance degree is sequenced to obtain an element recovery sequence which enables the accumulated load shedding amount of the system to be minimum; sampling the fault scenes by a non-time sequence Monte Carlo sampling method to obtain a plurality of groups of fault scenes; solving an optimization model which minimizes the load shedding amount for the fault scene to obtain the repair time of each element in each scene, forming a distribution function of the repair time of each element, comparing and sequencing the distribution functions by a Copland sequencing method, and finally sequencing the Copland score results of each element to obtain an evaluation result of the element importance; and implementing pre-disaster strengthening and post-disaster first-aid repair strategies for important elements according to the importance of the elements.
9. The method of claim 6, wherein the computing of the composite elasticity score for the system by analytic hierarchy process is specified as: calculating an elastic effect index generated by the elastic resource according to the basic elastic resource data of the power distribution network input by a user; and processing the index importance set by the user through an analytic hierarchy process, calculating the index weight, and finally calculating the comprehensive elasticity score of the system.
10. The utility model provides an elasticity distribution network panorama information visualization system which characterized in that includes:
the online monitoring and early warning module is used for monitoring system data information in real time, calculating and displaying dynamic elastic indexes of the power distribution network, carrying out fine early warning on the system according to index thresholds and marking risk elements;
and the offline elastic evaluation module is used for simulating extreme disaster weather offline, calculating and displaying the comprehensive elastic scores of the power distribution network, sorting the importance of the power distribution network elements, marking the power distribution network element with the highest importance and realizing the visualization of the panoramic information of the elastic power distribution network.
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