CN116304780A - Method, device, equipment and storage medium for acquiring operation scene set of power distribution network - Google Patents
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Abstract
The application relates to a method, a device, equipment and a storage medium for acquiring a power distribution network operation scene set, wherein state quantity data of a target power distribution network are obtained through power flow calculation based on operation boundary conditions of the target power distribution network; secondly, classifying the state quantity data to obtain scene category labels; and finally, according to the scene category labels, obtaining a power distribution network operation scene set required by power distribution network operation analysis and decision training based on artificial intelligence. The method realizes the full training of the operation analysis and decision of the power distribution network based on artificial intelligence.
Description
Technical Field
The application relates to the technical field of power distribution networks, in particular to a method, a device, equipment and a storage medium for acquiring a power distribution network operation scene set.
Background
In a power system, a power distribution network is an important end link and directly supplies electric energy to various users. With the development of a novel power system, the duty ratio of new energy is continuously increased, which contains strong uncertainty, including dynamic and time-varying characteristics of weather, nonlinear energy conversion process and complex space-time correlation, and has great difficulty in realizing power balance and safe operation of a power distribution network and ensuring the power supply reliability and the power quality of users.
With the improvement of the automation operation level of the power distribution network and the development of artificial intelligence, the power distribution network analysis and decision-making technology based on the artificial intelligence is expected to solve the above challenges. However, when decision making and analysis are performed based on artificial intelligence technology at present, a large amount of operation scene data of the power distribution network is required, and the operation scene data of the power distribution network is difficult to obtain, so that the training of an artificial intelligence decision making method is insufficient, the actual power grid production and operation requirements are difficult to meet, and improvement is needed.
Disclosure of Invention
Based on the foregoing, it is necessary to provide a method, a device, equipment and a storage medium for acquiring a power distribution network operation scene set for power distribution network operation analysis and decision training based on artificial intelligence technology.
In a first aspect, the present application provides a method for acquiring an operation scene set of a power distribution network. The method comprises the following steps:
based on the operation boundary condition of the target power distribution network, obtaining state quantity data of the target power distribution network through load flow calculation;
classifying the state quantity data to obtain scene category labels;
and obtaining an operation scene set of the target power distribution network according to the scene category label.
In one embodiment, classifying the state quantity data to obtain a scene category label includes:
acquiring operation evaluation indexes of the target power distribution network according to the state quantity data;
and classifying the state quantity data based on the operation evaluation index of the target power distribution network to obtain scene category labels.
In one embodiment, classifying the state quantity data based on the operation evaluation index of the target power distribution network to obtain a scene category label includes:
and classifying the state quantity data based on the operation evaluation index of the target power distribution network and the preset classification number to obtain scene category labels.
In one embodiment, the method further comprises:
acquiring corresponding relations between different meteorological factors and different power distribution network operation boundary conditions;
and determining the operation boundary condition of the target power distribution network according to the corresponding relation.
In one embodiment, determining the operation boundary condition of the target power distribution network according to the correspondence relationship includes:
and determining the operation boundary condition of the target power distribution network according to the corresponding relation between the wind speed and the wind power output, the corresponding relation between the illumination intensity and the photovoltaic output and the corresponding relation between the temperature and the load output.
In one embodiment, the operation evaluation indexes of the target power distribution network include a line loss rate, a voltage quality, a voltage stability margin, a line average load rate, a line maximum load rate and a heavy load rate.
In a second aspect, the application further provides a device for acquiring the operation scene set of the power distribution network. The device comprises:
the first obtaining module is used for obtaining state quantity data of the target power distribution network through load flow calculation based on operation boundary conditions of the target power distribution network;
the second obtaining module is used for classifying the state quantity data to obtain scene category labels;
and the third obtaining module is used for obtaining the operation scene set of the target power distribution network according to the scene category label.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method provided by any of the embodiments of the first aspect described above when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. On which a computer program is stored which, when being executed by a processor, implements the steps of the method provided by any of the embodiments of the first aspect described above.
In a fifth aspect, the present application also provides a computer program product. Comprising a computer program which, when executed by a processor, implements the steps of the method provided by any of the embodiments of the first aspect described above.
According to the method, the device, the equipment and the storage medium for acquiring the operation scene set of the power distribution network, firstly, the state quantity data of the target power distribution network are obtained through power flow calculation based on the operation boundary conditions of the target power distribution network, secondly, the state quantity data are classified to obtain scene class labels, and finally, the operation scene set of the target power distribution network is obtained according to the scene class labels. According to the method, state quantity data of power distribution network operation are obtained through power flow calculation, the obtained state quantity data are classified, and a power distribution network operation scene set required by power distribution network operation analysis and decision training based on artificial intelligence is obtained, so that the power distribution network operation analysis and decision training based on the artificial intelligence is ensured to be fully trained, and actual power grid production and operation requirements are met.
Drawings
FIG. 1 is an application environment diagram of a method for acquiring a set of operating scenarios of a power distribution network in one embodiment;
fig. 2 is a flow chart of a method for acquiring a running scene set of a power distribution network in one embodiment;
FIG. 3 is a flow diagram of a method of obtaining a field Jing Leibie tag in one embodiment;
FIG. 4 is a block diagram illustrating a configuration of a power distribution network operation scenario set acquisition device according to an embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The method for acquiring the operation scene set of the power distribution network, provided by the embodiment of the application, can be applied to an application environment shown in fig. 1. Wherein the distribution network communicates with the computer devices via a network. The data storage system may store data that the computer device needs to process. The data storage system may be integrated on a computer device or may be located on a cloud or other network server. The computer device may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, etc.
In one embodiment, as shown in fig. 2, a method for obtaining a running scene set of a power distribution network is provided, and the embodiment relates to a specific process of obtaining the running scene set of the target power distribution network by obtaining state quantity data of the target power distribution network through load flow calculation based on running boundary conditions of the target power distribution network, classifying the state quantity data to obtain scene category labels, and finally obtaining the running scene set of the target power distribution network according to the scene category labels. This embodiment comprises the steps of:
s201, obtaining state quantity data of the target power distribution network through load flow calculation based on operation boundary conditions of the target power distribution network.
The operation boundary condition of the power distribution network refers to the condition that the power distribution network has a given power distribution network topological structure and network parameters and has no faults or other disturbance, and the power distribution network comprises renewable energy output and load output. Wherein the renewable energy source output comprises wind power output and photovoltaic output.
The state quantity data of the power distribution network comprises active, reactive, voltage and phase angle information of each node in the power grid system. It should be noted that, each node in the power distribution network system includes a balance node, where voltage and phase angle information of the balance node are given, active and reactive power of other nodes except the balance node may be directly determined by renewable energy output and load output, and active and reactive power of the balance node, and voltage and phase angle information of other nodes except the balance node may be determined by power flow calculation.
The flow equation expression related to flow calculation is as follows:
wherein P, Q, U, θ are node active, reactive, voltage and phase angle respectively, G and B represent admittances between nodes, and subscript n represents system node number. On the premise that the active power and the reactive power of the nodes are known, the voltage and the phase angle information of each node can be regarded as solutions of the tide equation, and the number of the solutions is uncertain for a nonlinear algebraic equation set. The flow equation is essentially a set of circular equations, and the case of multiple solutions can be divided into high-pressure solutions and low-pressure solutions. For systems in the normal operation interval, i.e. where the power flow calculation starts flat and converges, a unique solution can be considered. For a certain set of node active and reactive values, then a corresponding set of uniquely determined node voltages and phase angles, i.e. node voltages and phase angles are functions of node active and reactive values, specifically expressed as follows:
(V n ，θ n )＝f PF (P n ，Q n )
wherein f PF (. Cndot.) shows a mapping between known node active and reactive power and the node voltage and phase angle obtained by solving the power flow equation.
Based on the operation boundary conditions and the flow equation expression of the power distribution network, state quantity data of the target power distribution network can be obtained.
S202, classifying the state quantity data to obtain scene category labels.
The scene type tag refers to a tag corresponding to state quantity data, and can be obtained by classifying the state quantity data.
Specifically, after the state quantity data of the target power distribution network is obtained, the obtained state quantity data of the target power distribution network is classified, so that a plurality of types of state quantity data can be obtained, one type of state quantity data corresponds to one scene type label, and then the plurality of types of state quantity data correspond to a plurality of scene type labels; for example, after the state quantity data of the target power distribution network is obtained, the voltage quality in the obtained state quantity data of the target power distribution network is classified, so that a plurality of different types of voltage quality can be obtained, and one type of voltage quality corresponds to one scene type label.
And S203, obtaining an operation scene set of the target power distribution network according to the scene category labels.
Based on the obtained state quantity data and the scene type labels, an operation scene set of the power distribution network can be obtained, one type of state quantity data and the scene type label corresponding to the type of state quantity data form one operation scene of the power distribution network, a plurality of types of state quantity data and the scene type labels corresponding to the types of state quantity data respectively form a plurality of operation scenes of the power distribution network, and the plurality of operation scenes form the operation scene set of the power distribution network. For example, the voltage quality in the system state quantity data of the power distribution network is classified, so that types of multiple voltage quality can be obtained, and each type of voltage quality corresponds to one scene type label, one type of voltage quality and one scene type label corresponding to the type of voltage quality form one operation scene of the power distribution network, and multiple types of voltage quality and multiple scene type labels corresponding to the types of voltage quality form an operation scene set of the power distribution network.
In this embodiment, state quantity data of a target power distribution network is obtained through load flow calculation based on operation boundary conditions of the target power distribution network, scene category labels are obtained by classifying the state quantity data, and finally an operation scene set of the target power distribution network is obtained according to the scene category labels. According to the method, state quantity data of power distribution network operation are obtained through power flow calculation, the obtained state quantity data are classified, and a power distribution network operation scene set required by power distribution network operation analysis and decision training based on artificial intelligence is obtained, so that the power distribution network operation analysis and decision training based on the artificial intelligence is ensured to be fully trained, and actual power grid production and operation requirements are met.
Referring to fig. 3, fig. 3 is a flowchart of a scene category label obtaining method according to an embodiment of the present application. The embodiment relates to an optional implementation manner of classifying state quantity data to obtain a scene category label, and based on the embodiment, the step S202 includes the following steps:
s301, acquiring operation evaluation indexes of the target power distribution network according to the state quantity data.
The operation evaluation indexes of the power distribution network comprise a line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate and heavy load rate.
It can be understood that in actual operation and analysis of the power distribution network, all state quantity data in the power distribution network system are not considered at the same time, corresponding power distribution network operation evaluation indexes are established for analysis, and the single power distribution network operation evaluation indexes cannot well reflect the state of the power distribution network system, so that the power distribution network operation evaluation indexes of different layers such as safety, economy and stability are established for evaluating the state of the power distribution network system; the operation evaluation indexes of the target power distribution network comprise line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate and overload rate.
Further, the specific calculation for acquiring the operation evaluation index of the target power distribution network according to the state quantity data is specifically described.
Regarding the line Loss Rate, ensuring the economical efficiency of the operation of the Power distribution network system is also one of the basic requirements of the operation of the Power system, and the line Loss Rate (PLR) can reflect the economical efficiency of the operation of the system to a certain extent. The calculation formula is as follows:
wherein P is Gi Representing the injection power of node i, P Di Representing the outgoing power of node i. The formula represents the ratio of the difference between the total power generation amount and the total power consumption amount in the power distribution network system to the total power generation amount.
With respect to voltage quality, voltage is one of the main power quality indicators from the power quality point of view. When the operating voltage exceeds the allowable offset value, the operation of various devices in the power distribution network system is affected. The voltage quality can be calculated by adopting a bus voltage quality index (Bus Voltage Quality Index, BQI), and the calculation formula of the bus voltage quality index is as follows:
wherein V is i Representing the actual voltage at node i, V ri Indicating the nominal voltage of node i. The formula represents the average voltage offset rate of all nodes in the power distribution network system.
With respect to the voltage stability margin, from the perspective of operational safety of the power distribution network, the static voltage stability margin of the power distribution network system is an important indicator. When the voltage in the system exceeds the static voltage stability limit, voltage breakdown accidents can occur, and the system safety is endangered. The voltage stability margin can be the minimum singular value of the jacobian matrix of the power flow equation, and can be obtained by adopting a singular value decomposition method, a sensitivity method, a continuous power flow prolongation method, a collapse point method, a nonlinear programming method and the like in actual calculation.
In the actual operation of the power distribution network system, if a power transmission line is overloaded, the safe and stable operation of the power distribution network system can be influenced, so that the load index also needs to be concerned. The line Load mainly refers to the current of the line, and the following indexes can be correspondingly established, including the maximum, minimum and average Load Rate (Load Rate, LR) of the line, the Heavy Load Rate (Heavy-Load Rate, HR) and the like, and the calculation formulas of the indexes are as follows:
wherein I is i Representing the actual current of line I, I max Represents the maximum current, P, of line i i As an indication function of whether line i is overloaded. The expression of the indirection function is as follows:
and the overall load level of the power distribution network system during operation can be estimated by comprehensively considering the load related indexes.
S302, classifying the state quantity data based on the operation evaluation index of the target power distribution network to obtain scene category labels.
Further, after the operation evaluation index of the power distribution network is obtained, classifying the state quantity data to obtain scene category labels; specifically, each power distribution network operation evaluation index is presented in a numerical form, and for all obtained state quantity data, the numerical value of the corresponding power distribution network operation evaluation index necessarily corresponds to a numerical range, and the whole numerical range is classified, namely all state quantity data are classified according to the numerical range of any power distribution network operation evaluation index. It should be noted that, the scene category labels may be customized for the state quantity data in different numerical ranges, for example, the low, lower, middle, higher and high categories may be sequentially corresponding to each other. For example, 2500 sets of state quantity data are all calculated, the voltage quality of the 2500 sets of state quantity data corresponds to a value range of 0.9-0.95, when the 2500 sets of state quantity data are divided into five types, namely, the value ranges of the voltage quality corresponding to the 2500 sets of state quantity data are respectively 0.9-0.91, 0.91-0.92, 0.92-0.93, 0.93-0.94 and 0.94-0.95, and the value ranges of the voltage quality corresponding to the 500 sets of state quantity data are respectively 0.92-0.93, the 500 sets of state quantity data correspond to the same scene tag.
According to the method provided by the embodiment, the operation evaluation index of the target power distribution network is obtained according to the state quantity data, and the state quantity data is classified based on the operation evaluation index of the target power distribution network to obtain the scene type label, so that the scene type label is obtained by classifying the state quantity data based on the operation evaluation index of the target power distribution network, and then the operation scene set of the target power distribution network is obtained according to the scene type label, so that the full training of operation analysis and decision making of the power distribution network based on artificial intelligence is ensured, and the actual power grid production and operation requirements are met.
In one embodiment, S302 described above is further refined. Optionally, classifying the state quantity data based on the operation evaluation index of the target power distribution network to obtain a scene category label may be classifying the state quantity data based on the operation evaluation index of the target power distribution network and a preset classification number to obtain the scene category label;
specifically, each power distribution network operation evaluation index is presented in the form of a numerical value, and each group of operation boundary conditions corresponds to a group of state quantity data and corresponds to the numerical value of a group of power distribution network operation evaluation indexes. The method comprises the steps of obtaining a numerical value of an operation evaluation index of a group of target power distribution networks, wherein the numerical value of the operation evaluation index of the group of target power distribution networks necessarily corresponds to a numerical range, dividing the group of operation evaluation indexes of the target power distribution networks of the numerical range according to a preset classification number to obtain operation evaluation indexes of the target power distribution networks of different regional ranges, classifying the group of state quantity data according to the operation evaluation indexes of the target power distribution networks of the different regional ranges to obtain different types of state quantity data, and any one of the target power distribution network operation evaluation indexes of the different regional ranges corresponds to one type of state quantity data. Since each section range corresponds to a different scene category label, and each section range corresponds to different types of state quantity data, the different types of state quantity data correspond to different scene category labels. For example, a set of operation evaluation indexes of the target power distribution network are obtained according to a set of state quantity data of the target power distribution network, wherein the set of operation evaluation indexes refer to the operation evaluation indexes of the target power distribution network; the obtained voltage quality in a group of operation evaluation indexes has a numerical range of 0.9-0.95, then the voltage quality in the numerical range of 0.9-0.95 is divided into intervals, for example, the preset classification number is 5, the voltage quality in the numerical range of 0.9-0.95 can be divided into voltage quality of 0.9-0.91, voltage quality of 0.91-0.92, voltage quality of 0.92-0.93, voltage quality of 0.93-0.94 and voltage quality of 0.94-0.95, the group of state quantity data is further classified according to the voltage quality in the five different intervals, then the voltage quality in the five different intervals corresponds to one type of state quantity data respectively, and any one type of the five types of state quantity data can be obtained to correspond to one scene category label; for example, a voltage quality of 0.9-0.91, a voltage quality of 0.91-0.92, a voltage quality of 0.92-0.93, a voltage quality of 0.93-0.94, and a voltage quality of 0.94-0.95 are defined as a low voltage quality, a medium voltage quality, a high voltage quality, and a high voltage quality, respectively, wherein, for example, a scene type tag corresponding to the type of state quantity data corresponding to the voltage quality of 0.9-0.91 is a low voltage quality.
According to the method provided by the embodiment, the state quantity data is classified based on the operation evaluation index of the target power distribution network and the preset classification number to obtain the scene type label, so that the operation evaluation index based on the target power distribution network can be realized, the state quantity data is classified to obtain the scene type label, and then the operation scene set of the target power distribution network is obtained according to the scene type label, so that the full training of operation analysis and decision making of the power distribution network based on artificial intelligence is ensured, and the actual power grid production and operation requirements are met.
Next, a specific implementation procedure for obtaining the operation boundary condition of the target power distribution network will be described.
In one embodiment, the method further comprises obtaining correspondence between different meteorological factors and different power distribution network operating boundary conditions; and determining the operation boundary condition of the target power distribution network according to the corresponding relation.
Weather factors include, but are not limited to, air temperature, air pressure, precipitation, visibility, humidity, and illumination intensity;
further, the corresponding relation between different meteorological factors and different power distribution network operation boundary conditions, namely, the corresponding relation between air temperature, air pressure, precipitation, visibility, humidity and illumination intensity and renewable energy output or load output is determined. For example, the correspondence may include a correspondence between wind speed and wind power output, a correspondence between illumination intensity and photovoltaic output, and a correspondence between temperature and load output.
After the corresponding relations between different meteorological factors and different power distribution network operation boundary conditions are obtained, the operation boundary conditions of the target power distribution network can be directly determined according to the corresponding relations. For example, the current wind speed of the target power distribution network is obtained, and the operation boundary condition of the target power distribution network, namely wind power output, can be directly determined according to the corresponding relation between the wind speed and wind power output; correspondingly, the current illumination intensity of the target power distribution network is obtained, the operation boundary condition of the photovoltaic output power, namely the target power distribution network can be directly determined according to the corresponding relation between the illumination intensity and the photovoltaic output power, and the current temperature of the target power distribution network is obtained, namely the operation boundary condition of the load output power, namely the target power distribution network can be directly determined according to the corresponding relation between the temperature and the load output power.
In this embodiment, by acquiring the correspondence between different meteorological factors and different operation boundary conditions of the power distribution network, the operation boundary conditions of the target power distribution network can be determined, and the state quantity data of the power distribution network can be obtained through load flow calculation.
In one embodiment, determining the operational boundary condition of the target power distribution network according to the correspondence includes determining the operational boundary condition of the target power distribution network according to the correspondence between wind speed and wind power output, the correspondence between illumination intensity and photovoltaic output, and the correspondence between temperature and load output.
Further, the correspondence between wind speed and wind power output is as follows:
wherein P is W For the output of the fan, V t For the wind speed at time t, V ci For the fan to cut in the wind speed, V co Wind speed for fan cut-out, V r For rated wind speed of fan, P r For the rated power of the fan, A, B and C are fitting coefficients.
The correspondence between the illumination intensity and the photovoltaic output is as follows:
wherein P is b For photovoltaic output, P sn Rated for photovoltaic array, G bt For the light intensity coefficient at time t, G ste Is the unit light intensity, R c Is the intensity of a light of a particular intensity.
The correspondence between temperature and load output is as follows:
wherein E is the solar power, i.e. the load output is represented, n is the date, temp n，mean The average daily air temperature is represented, and a, b and c are fitting coefficients.
In the embodiment, the operation boundary condition of the target power distribution network can be determined through the corresponding relation between the wind speed and the wind power output, the corresponding relation between the illumination intensity and the photovoltaic output and the corresponding relation between the temperature and the load output, and data support is provided for obtaining state quantity data through calculation.
In addition, in one embodiment, the application further provides a complete example, the example is based on the IEEE33 node power distribution system, the meteorological data corresponding to the meteorological factors are derived from a real data set, the meteorological data of the Beijing area in the last seven years are selected, the meteorological data including wind speed, illumination intensity and temperature are respectively corresponding to wind power output, photovoltaic output and load output and are used as boundary conditions of operation of the power distribution network, and therefore power distribution network operation data are obtained.
According to the example, meteorological data of 2555 days in total are selected for seven years, and the renewable energy output and load of the system are calculated through the corresponding relation between wind speed and wind power output, the corresponding relation between illumination intensity and photovoltaic output and the corresponding relation between temperature and load output, wherein the data sample is obtained every day. For each sample, consider a time resolution of 5 minutes for 288 times a day, where each time corresponds to a system power flow profile. For each system power flow profile, the data includes active and reactive injection of the balancing nodes, voltage and phase angle information of all nodes and output of renewable energy sources, and active demand of the load nodes. The power factor of the load node is assumed to be fixed and therefore reactive load need not be considered. For a node containing 1 balance node, n con A plurality of connection nodes, n load Load nodes, n gen For a renewable energy output system, the data amount n contained in each tide section is as follows:
for an IEEE33 node distribution network, each power flow profile contains 108 data, each sample is a matrix of 108x288, and 2555 data points are all included.
The operation evaluation indexes of the power distribution network comprise six indexes including line loss rate, voltage quality, voltage stability margin, average line load rate, maximum line load rate and overload rate. And respectively taking the average value, the minimum value, the average value, the maximum value and the average value of each time index as the daily tide sequence index. For scene classification, the scene classification method is divided into five types of samples according to the voltage quality index, and the five types of samples can be used as labels in the process of training a related model.
In this way, firstly, according to the corresponding relation between wind speed and wind power output, the corresponding relation between illumination intensity and photovoltaic output and the corresponding relation between temperature and load output, the operation boundary condition of a target power distribution network is determined, secondly, state quantity data of power distribution network operation is obtained through calculation, and then, six operation evaluation indexes of line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate and load rate of the power distribution network are obtained through calculation based on the state quantity data, and finally, the state quantity data is classified, so that scene category labels are obtained. For example, the voltage quality index is equally divided into five types of samples, one type of voltage quality corresponds to one scene type label, one type of voltage quality and one scene type label corresponding to the voltage quality are one power distribution network operation scene, and all types of voltage quality and a plurality of scene type labels corresponding to the voltage quality are power distribution network operation scene sets.
In the embodiment, firstly, according to the corresponding relation between wind speed and wind power output, the corresponding relation between illumination intensity and photovoltaic output and the corresponding relation between temperature and load output, the operation boundary condition of a target power distribution network is determined, and based on the operation boundary condition of the target power distribution network, state quantity data of the target power distribution network are obtained through tide calculation, then the state quantity data are classified to obtain scene class labels, and finally, according to the scene class labels, a power distribution network operation scene set required by power distribution network operation analysis and decision training based on artificial intelligence is obtained, so that the obtained operation scene set can be used as data required by training, and full training of power distribution network operation analysis and decision based on artificial intelligence is realized.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a power distribution network operation scene set acquisition device for realizing the power distribution network operation scene set acquisition method. The implementation scheme of the solution to the problem provided by the device is similar to the implementation scheme recorded in the method, so the specific limitation in the embodiment of the device for obtaining the operation scene set of the power distribution network provided below can be referred to the limitation of the method for obtaining the operation scene set of the power distribution network hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 4, there is provided a power distribution network operation scenario set acquisition apparatus 400 including: a first obtaining module 401, a second obtaining module 402, and a third obtaining module 403, wherein:
the first obtaining module 401 is configured to obtain state quantity data of the target power distribution network through load flow calculation based on an operation boundary condition of the target power distribution network;
a second obtaining module 402, configured to classify the state quantity data to obtain a scene category label;
and a third obtaining module 403, configured to obtain an operation scene set of the target power distribution network according to the scene category label.
In one embodiment, the second obtaining module 402 includes:
the first acquisition unit is used for acquiring operation evaluation indexes of the target power distribution network according to the state quantity data;
the second acquisition unit is used for classifying the state quantity data based on the operation evaluation index of the target power distribution network to obtain scene category labels.
In one embodiment, the second obtaining unit is specifically configured to classify the state quantity data based on the operation evaluation index of the target power distribution network and a preset classification number to obtain a scene category.
In one embodiment, the apparatus 400 further comprises:
the acquisition module is used for acquiring the corresponding relation between different meteorological factors and different power distribution network operation boundary conditions;
and the determining module is used for determining the operation boundary condition of the target power distribution network according to the corresponding relation.
In one embodiment, the determining module is specifically configured to determine an operation boundary condition of the target power distribution network according to a corresponding relationship between wind speed and wind power output, a corresponding relationship between illumination intensity and photovoltaic output, and a corresponding relationship between temperature and load output.
In one embodiment, the operational evaluation indicators of the target power distribution network include line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate, and overload rate.
All or part of the modules in the power distribution network operation scene set acquisition device can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program, when executed by a processor, implements a method for acquiring a set of operating scenarios for a power distribution network. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
based on the operation boundary condition of the target power distribution network, obtaining state quantity data of the target power distribution network through load flow calculation;
classifying the state quantity data to obtain scene category labels;
and obtaining an operation scene set of the target power distribution network according to the scene category label.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring operation evaluation indexes of the target power distribution network according to the state quantity data;
and classifying the state quantity data based on the operation evaluation index of the target power distribution network to obtain scene category labels.
In one embodiment, the processor when executing the computer program further performs the steps of:
and classifying the state quantity data based on the operation evaluation index of the target power distribution network and the preset classification number to obtain scene category labels.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring corresponding relations between different meteorological factors and different power distribution network operation boundary conditions;
and determining the operation boundary condition of the target power distribution network according to the corresponding relation.
In one embodiment, the processor when executing the computer program further performs the steps of:
and determining the operation boundary condition of the target power distribution network according to the corresponding relation between the wind speed and the wind power output, the corresponding relation between the illumination intensity and the photovoltaic output and the corresponding relation between the temperature and the load output.
In one embodiment, the operational evaluation indicators of the target power distribution network include line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate, and overload rate.
The principles and specific processes of implementing the foregoing embodiments of the foregoing computer device may be referred to the description in the foregoing embodiments of the embodiment of the method for acquiring a running scene set of a power distribution network, which is not repeated herein.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
based on the operation boundary condition of the target power distribution network, obtaining state quantity data of the target power distribution network through load flow calculation;
classifying the state quantity data to obtain scene category labels;
and obtaining an operation scene set of the target power distribution network according to the scene category label.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring operation evaluation indexes of the target power distribution network according to the state quantity data;
and classifying the state quantity data based on the operation evaluation index of the target power distribution network to obtain scene category labels.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and classifying the state quantity data based on the operation evaluation index of the target power distribution network and the preset classification number to obtain scene category labels.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring corresponding relations between different meteorological factors and different power distribution network operation boundary conditions;
and determining the operation boundary condition of the target power distribution network according to the corresponding relation.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and determining the operation boundary condition of the target power distribution network according to the corresponding relation between the wind speed and the wind power output, the corresponding relation between the illumination intensity and the photovoltaic output and the corresponding relation between the temperature and the load output.
In one embodiment, the operational evaluation indicators of the target power distribution network include line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate, and overload rate.
The principles and specific processes of implementing the foregoing embodiments of the foregoing computer readable storage medium may be referred to the description of the embodiment of the method for acquiring the running scene set of the power distribution network in the foregoing embodiments, which is not repeated herein.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
based on the operation boundary condition of the target power distribution network, obtaining state quantity data of the target power distribution network through load flow calculation;
classifying the state quantity data to obtain scene category labels;
and obtaining an operation scene set of the target power distribution network according to the scene category label.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring operation evaluation indexes of the target power distribution network according to the state quantity data;
and classifying the state quantity data based on the operation evaluation index of the target power distribution network to obtain scene category labels.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and classifying the state quantity data based on the operation evaluation index of the target power distribution network and the preset classification number to obtain scene category labels.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring corresponding relations between different meteorological factors and different power distribution network operation boundary conditions;
and determining the operation boundary condition of the target power distribution network according to the corresponding relation.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and determining the operation boundary condition of the target power distribution network according to the corresponding relation between the wind speed and the wind power output, the corresponding relation between the illumination intensity and the photovoltaic output and the corresponding relation between the temperature and the load output.
In one embodiment, the operational evaluation indicators of the target power distribution network include line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate, and overload rate.
The principles and specific processes of implementing the foregoing embodiments of the foregoing computer program product may be referred to in the foregoing embodiments of the embodiment of the method for acquiring a running scene set of a power distribution network, which is not described herein again.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. The method for acquiring the operation scene set of the power distribution network is characterized by comprising the following steps:
based on the operation boundary condition of a target power distribution network, calculating to obtain state quantity data of the target power distribution network through tide;
classifying the state quantity data to obtain scene category labels;
and obtaining the operation scene set of the target power distribution network according to the scene category label.
2. The method of claim 1, wherein classifying the state quantity data to obtain a scene category label comprises:
acquiring operation evaluation indexes of the target power distribution network according to the state quantity data;
and classifying the state quantity data based on the operation evaluation index of the target power distribution network to obtain the scene category label.
3. The method according to claim 2, wherein classifying the state quantity data based on the operation evaluation index of the target power distribution network to obtain the scene category label includes:
and classifying the state quantity data based on the operation evaluation index of the target power distribution network and the preset classification number to obtain the scene category label.
4. A method according to claim 3, characterized in that the method further comprises:
acquiring corresponding relations between different meteorological factors and different power distribution network operation boundary conditions;
and determining the operation boundary condition of the target power distribution network according to the corresponding relation.
5. The method of claim 4, wherein determining the operational boundary condition of the target power distribution network from the correspondence relationship comprises:
and determining the operation boundary condition of the target power distribution network according to the corresponding relation between wind speed and wind power output, the corresponding relation between illumination intensity and photovoltaic output and the corresponding relation between temperature and load output.
6. A method according to claim 2 or 3, wherein the operational evaluation indicators of the target distribution network include line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate and overload rate.
7. An acquisition device of a power distribution network operation scene set, which is characterized by comprising:
the first obtaining module is used for obtaining state quantity data of the target power distribution network through load flow calculation based on operation boundary conditions of the target power distribution network;
the second obtaining module is used for classifying the state quantity data to obtain scene category labels;
and the third obtaining module is used for obtaining the operation scene set of the target power distribution network according to the scene category label.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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