CN110796567B - Production early report application framework based on regulation and control cloud - Google Patents
Production early report application framework based on regulation and control cloud Download PDFInfo
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Abstract
The invention discloses a control cloud-based production early report application framework which comprises a source data layer, an application analysis layer and a data objectification layer. The application analysis layer comprises a whole-network electric power unit, a regional electric power unit, a main power unit start-stop unit, a power grid accident abnormal unit, a current power grid stop-re-service equipment unit, a week weather forecast unit, a whole-network electric power balance condition unit, a direct current power planning unit, an equipment maintenance risk early warning unit and a holiday air temperature, load and power consumption condition unit. The data objectification layer comprises an accident abnormal data objectification module and a maintenance work data objectification module. According to the production early report application framework based on the regulation cloud, the production early report is fused with the analysis advanced application of the regulation cloud power system, key conclusions related to the simulation analysis of the operation depth of the power grid are displayed, and the multi-subject templatized storage and the configurable automatic generation technology of the research early report are used for supporting and scheduling daily application.
Description
Technical Field
The invention relates to a production early report application framework based on a regulation cloud.
Background
The production early report is an important grip for the production operation management of Shanghai companies. The regulatory centers have accumulated a lot of experience in the programming application of production breakfast. The production early report requires timely and accurate collection of the power grid dispatching operation professional information and further carries out association analysis.
At present, the generation of early newspaper is mainly completed by a mode of manufacturing PPT by professionals. Along with the continuous promotion of 'regulation and control cloud' construction of a national power grid company, the method has the conditions of developing the research on the targeting and analysis technology of the production early newspaper based on the regulation and control cloud so as to fully exert the intelligent data convergence advantage of the large data platform of the regulation and control cloud, further improve the compiling efficiency and the data association analysis capability of the production early newspaper and provide powerful support for the safe production of the power grid.
The production early report is a typical power grid dispatching big data application scene, closely meets the production application requirements, and has high practical value and application popularization value.
The national grid company highly pays attention to research and application of big data technology, and builds a national grid cloud platform with more flexible and flexible resource allocation, more centralized and intelligent data utilization, more unified and efficient service integration and quicker and more convenient application development. The regulation cloud takes inheritance innovation as a basic principle, practical and effective falling to the ground, combines the operation state of a regulation system, introduces new generation information technologies such as cloud computing, big data and the like, and builds a regulation cloud platform with physical distribution and logic concentration.
In the aspect of scheduling data objectification, the regulation and control cloud platform fully absorbs model data standard thought and experience related to the domestic and foreign intelligent power grid scheduling control technology, and the problem of model data objectification information sharing among scheduling data centers and among professions in the scheduling centers is solved, wherein the model data standard thought and experience comprises localized expansion of IEC61970 and CIM/E technical standards. In the concrete technical realization, the regulation cloud deploys the unified maintenance and distribution functions of the metadata and the data dictionary, and each application end and the source data end can realize the subscription receiving of the metadata and the dictionary data in a subscription mode.
The data cloud platform is used as an important component of the regulation cloud and provides data management and data service, and comprises a physical power grid, entity equipment and an organization structure, and performs abstract modeling and standard division design on the relation between electric power regulation business objects and objects to establish a standard and unified scheduling model database. On the basis, a model data cloud platform, an operation data cloud platform, a real-time data cloud platform and a big data platform are constructed, and management and maintenance of data such as model, operation and real-time are realized.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a production early report application (application) framework based on regulation and control cloud, integrates production early report with analysis advanced application of a power system of the regulation and control cloud, displays key conclusions related to simulation analysis of the operation depth of a power grid, and supports and schedules daily application by multi-subject templatized storage and configurable automatic generation technology of research early report.
The technical scheme for achieving the purpose is as follows: a regulatory cloud-based production early report application framework, comprising a source data layer, an application analysis layer and a data objectification layer, wherein:
the source data layer comprises an accident and abnormality module, an overhaul work sheet, a meteorological database, an electric power and energy compiling system and a scheduling database;
the application analysis layer comprises the following units:
full network electric power and electricity unit: the data for analyzing the electric quantity of the whole network comprises data of air temperature, electric load, peak-valley difference of electric power, electric power receiving, power generation output, electric quantity, generated energy, electric power receiving, electric load rate and power generation load rate, wherein the generated energy data comprises a historical highest value, a month accumulated value, a month increasing rate, an year accumulated value and an year increasing rate;
regional power and electricity unit: the method comprises the steps of analyzing yesterday highest load, historical highest load, month highest homonymy, daily electric quantity, month accumulation, month increase, year accumulation and year increase data of each region;
and a main power unit start-stop unit: checking the real-time running state of the unit, wherein the data to be extracted in the processing process comprises the following steps: matching a unit object from the regulation cloud, the power plant object, further providing unit capacity and whether the unit is a coal-fired unit or peak shaver unit information, and finally storing the processed data into a unit equipment outage time table;
an electric network accident abnormal unit: the source data come from an accident and abnormality module, the accident and abnormality module is divided into 7 modules, namely an accident scheduling module, an abnormality scheduling module, a direct current accident and abnormality module, an operation abnormality module, an accident monitoring module, an abnormality monitoring module and a ground regulation accident and abnormality module, and the objects to be extracted are plant station objects and equipment objects; the content to be extracted comprises fault description, fault influence, fault reason, influence recovery and equipment recovery data;
current power grid outage and restoration equipment unit: the source data come from an overhaul work order, and the equipment running state check comes from the real-time running state of the equipment; the source data to be extracted in the processing process comprises equipment name, station name, planning time, start time, finishing time, flow state and equipment type data; matching equipment objects and plant station objects from the regulation cloud; further providing equipment voltage class information; finally, storing the processed data into a unit equipment retirement time table;
a week weather forecast unit: the source data is from a meteorological database, and the source data required to be extracted in the processing process comprises early weather, late weather, minimum temperature and maximum temperature;
full network power balance condition unit: the source data is from a power and energy compiling system and a dispatching database, and the source data to be extracted comprises the estimated highest power load, the highest power receiving power of the present day, the highest power generation output of the present day, the available load of the present day, the rotation standby minimum value of the present day, the present day low load, the present day ordered power demand, the yesterday gate fuel stock available days, the yesterday power plant coal stock and the yesterday power plant coal stock average available days; extracting the estimated highest temperature and the estimated lowest temperature from a meteorological database;
a direct current power planning unit: checking the real-time power according to the direct current state and rated power of the equipment;
equipment overhaul risk early warning unit: analyzing a plurality of pre-warning control measures for risk assessment of a main network of 220 kilovolts or more in a one-week overhaul mode, and analyzing the risk assessment and pre-warning control measures for the current overhaul mode of a distribution network of 35 kilovolts or more;
holiday air temperature, load and electricity consumption condition unit: analyzing the corresponding values, loads and electricity consumption conditions of the highest air temperature/lowest air temperature, the average highest air temperature/average lowest air temperature and the same date in the holiday of the current year;
the data objectification layer comprises an accident abnormal data objectification module and a maintenance work data objectification module, wherein the accident abnormal data objectification module adopts unified station equipment information to objectify, normalize and structure the original accident abnormality description in the power grid accident abnormal unit; and the maintenance work sheet data objectification module obtains the out-of-service time sequence data of each type of equipment by analyzing the maintenance work sheet.
The production early report application framework based on the regulation cloud, wherein the accident scheduling module and the abnormal scheduling module correspondingly store scheduling accidents and scheduling abnormalities of equipment with voltage levels of 220kV and above, and the fields of original accident abnormal source data extracted from the accident scheduling module and the abnormal scheduling module comprise a station name, an equipment name, a protection action, a fault phase, mode adjustment, line inspection conditions, zoning and ring disassembly, fault ranging information/unit emergency start-stop conditions, unit processing regulation conditions, load transfer conditions, lost load, lower-level self-cutting action conditions, single power supply risks of 35kV and above, protection user conditions, important user conditions, power failure user conditions and reclosing conditions;
the direct current accident and abnormality module stores direct current accidents and abnormalities of equipment with voltage levels of 220kV and above, and the fields of original accident abnormality source data extracted from the direct current accident and abnormality module comprise emergency start-stop conditions of a unit, mode adjustment, line inspection conditions, unit processing adjustment conditions, load adjustment, network adjustment and reduction power receiving conditions, power transfer conditions, frequency change conditions, countermeasures, accident influence, power transmission size before locking and direct current accident abnormality conditions;
the operation abnormality module stores operation abnormalities of equipment with voltage class of 220kV and above, and the field of original accident abnormality source data extracted from the operation abnormalities comprises equipment names, station names, operation content and results triggering accident abnormalities and subsequent operations;
the accident monitoring module and the abnormality monitoring module correspondingly store monitoring accidents and monitoring abnormalities of equipment with voltage levels of 220kV and above, and the fields of original accident abnormality source data extracted from the monitoring accidents and the monitoring abnormalities comprise recovery conditions, accident handling conditions, fault/abnormality conditions, equipment names and station names;
the ground-control accident and abnormality module stores dispatching accidents and abnormalities with the voltage level of 220kV and above of the equipment, and the fields of original accident abnormality source data extracted from the dispatching accidents and abnormalities comprise equipment names, station names, subordinate self-switching action conditions, single power supply risks of 35kV and above, protection user conditions, important user conditions, power-loss user conditions, reclosing conditions and loss loads.
The production early report application framework based on the regulation cloud, wherein the power grid accident abnormal unit extracts original accident abnormal source data from an accident scheduling module, an abnormal scheduling module, a direct current accident and abnormal module, an operation abnormal module, an accident monitoring module, an abnormal monitoring module and a ground regulation accident and abnormal module; the accident abnormal data objectification module firstly extracts the station name, the equipment name, the description and the processing condition information in the original data of the power grid accident abnormal unit in the process of analyzing the accident abnormal source data, and then finds out the corresponding object from the regulation cloud equipment library according to the station name and the equipment name; and then, according to the corresponding keywords, cutting words from the original description text, extracting needed information section by section to form corresponding standardized recorded data, and dividing the standardized and structured recorded data into 6 major categories including direct current categories, power plants, abnormal operation categories, ground adjustment categories, main network line equipment categories and main network non-line equipment categories.
The production early report application framework based on the regulation cloud, wherein the maintenance work data objectification module extracts unaddressed and archived but unprocessed data from a maintenance work list every hour, and extracts the equipment name, the station name, the equipment type, the flow state, the planning time, the start time, the finishing time and the equipment state respectively;
then matching the extracted equipment names, the station names and the equipment types with data stored in the regulation cloud to obtain a unit object and a power plant object; analyzing the equipment state of each equipment, corresponding the equipment state to the standard description in the regulation cloud, and constructing the unit equipment out-of-service time according to the flow state, the planning time, the starting time and the finishing time of the overhaul work order;
then, according to different equipment types, dividing the equipment into power grid equipment and unit equipment;
judging whether the unit equipment is a peak shaver unit or a coal-fired unit or whether the capacity is greater than or equal to 300MW, if so, setting the data to be displayed in a report, and storing the processed data into a unit equipment outage time table;
judging whether the power grid equipment is direct current or not, judging whether the direct current to which the equipment belongs is direct current of a reed , and if the direct current is direct current of a reed , setting the direct current equipment not to be displayed on the PPT; if the voltage class of the equipment is 1000/500/220kV, if the voltage class is 1000/500kV, the equipment type is not a main transformer/bus/line, and the equipment type is not displayed in a report; if the voltage class is 220kV, the type of the power grid equipment is not bus/line, and the power grid equipment is not displayed in a report; and then storing the processed data into a power grid equipment outage schedule.
The production early report application framework based on the regulation cloud fuses the production early report with the analysis advanced application of the regulation cloud power system, displays key conclusions related to the simulation analysis of the operation depth of the power grid, and also supports daily application scheduling by multi-topic templatized storage and configurable automatic generation technology of research early report.
Drawings
Fig. 1 is a schematic structural diagram of a regulatory cloud-based production early report application framework of the present invention.
Detailed Description
In order to enable those skilled in the art to better understand the technical scheme of the present invention, the following detailed description is provided with reference to the accompanying drawings:
referring to fig. 1, a regulatory cloud-based production early report application framework according to the preferred embodiment of the present invention includes a source data layer 1, an application analysis layer 2, and a data objectification layer 3.
The source data layer 1 comprises an accident and abnormality module, an overhaul work sheet, a meteorological database, an electric power and energy compiling system and a scheduling database; the accident and abnormality module and the overhaul work order are stored in an OMS system, and the scheduling database adopts a D5000 database.
The application analysis layer 2 comprises a whole-network electric power unit 20, a regional electric power unit 21, a main power unit start-stop unit 22, a power grid accident abnormal unit 23, a current power grid shutdown device unit 24, a week weather forecast unit 25, a whole-network electric power balance condition unit 26, a direct current power planning unit 27, an equipment maintenance risk early warning unit 28 and a holiday air temperature, load and electricity consumption condition unit 29.
The data objectification layer 3 includes an accident abnormal data objectification module 31 and a maintenance work data objectification module 32.
1. The whole-network electric power unit 20 is configured to analyze data of whole-network electric power, including data of air temperature, electric load, peak-valley difference, electric power, power output, electric power consumption, electric power generation, electric power consumption, electric load rate and power load rate, and the electric power generation data includes historical maximum value, month accumulation rate, year accumulation value and year accumulation rate;
the average power generation amount in the D5000 database can be calculated according to 24 hours, and the actual value of the total power generation amount in the EMS daily reporting module can be obtained.
The data to be analyzed of the generated energy comprises a historical highest value, a month accumulation value, a month increase rate, a year accumulation value and a year increase rate.
The historical highest value takes the highest value of the historical power generation amount data in the OMS.
Month accumulation takes the month accumulation value of yesterday in the OMS plus the power generation amount of the current day.
The month increase takes historical accumulated data in OMS, and the calculation formula is as follows: month increase= (current month accumulation-last month accumulation)/last month accumulation 100%.
The annual cumulative value of yesterday plus the current day's power production in the OMS is taken.
The annual growth takes historical accumulated data in OMS, and the calculation formula is as follows: annual growth= (current year accumulation-last year accumulation)/last year accumulation by 100%.
And the electricity load rate is a calculated value. Firstly taking the daily electricity consumption, and then taking the highest electricity consumption load, wherein the specific calculation formula is as follows: electricity usage rate = electricity usage/(maximum electricity usage load 24) ×100%.
The power generation load factor is a calculated value. Firstly taking the daily power generation amount, and then taking the highest power generation amount, wherein a specific calculation formula is as follows: power generation load ratio=power generation amount/(maximum power generation capacity 24) ×100%.
The power generation load factor (new) is a calculated value. The power generation amount of the middle day is firstly taken, and the new highest power generation output is calculated. The calculation formula of the new power generation output is as follows: new generated output = total highest generated output-generated output of 16 power plants + rated generated output of a set after the power plant is started up in 16 power plants (note: 16 home power plants comprise an outer high bridge, an outer high bridge second power plant, an outer high bridge third power plant, a stone hole abnormality, a stone hole second power plant, a Dan Er new power plant, an upper electric channel jing, wu Jing, wu Jing six-period, wu Jing second power plant, a channel jing thermoelectric, a voxian combustion engine, a stone hole combustion engine, an upper temporary combustion engine, a Luo Jing combustion engine and a voxian second power plant). And then removing the maximum value of the new generated output to obtain the new highest generated output. The new calculation formula of the power generation load rate is as follows: power generation load ratio=power generation amount/(new highest power generation capacity 24) ×100%.
2. The regional power and electricity unit 21 is used for analyzing data of yesterday highest load, historical highest load, current month highest homonymy, daily electricity, month accumulation, month increase, year accumulation and year increase in 11 regions such as Shanghai Pudong, city north, city south, city district, jiang, pingjiang, feng xian, jinshan, qing pu, chong Ming, changxing and the like;
3. the main power unit start-stop unit 22 is used for checking the real-time running state of the unit, and the data to be extracted in the processing process are as follows: the method comprises the steps of matching a unit object from a regulation cloud, a power plant object, further providing unit capacity and unit information whether the unit is a coal-fired unit or a peak-shaving unit, and finally storing processed data into a unit equipment decommissioning time table;
4. the source data of the power grid accident abnormal unit 23 come from accident and abnormal modules, wherein the accident and abnormal modules are divided into 7 modules, namely an accident dispatching module, an abnormal dispatching module, a direct current accident and abnormal module, an operation abnormal module, an accident monitoring module, an abnormal monitoring module and a ground dispatching accident and abnormal module, and the objects to be extracted are plant station objects and equipment objects; the content to be extracted includes fault description, fault impact, fault cause, impact recovery and device recovery data.
The accident scheduling module and the abnormal scheduling module correspondingly store the scheduling accidents and scheduling anomalies of the equipment with the voltage level of 220kV and above, and the fields of the original accident anomaly source data extracted from the equipment comprise a station name, an equipment name, a protection action, a fault phase, mode adjustment, line inspection conditions, a partition and ring release, fault ranging information/unit emergency start-stop conditions, unit processing adjustment conditions, load transfer conditions, loss loads, subordinate self-cutting action conditions, single power supply risks of 35kV and above, protection user conditions, important user conditions, power failure user conditions and reclosing conditions;
the direct current accident and abnormality module stores direct current accidents and abnormalities of equipment with voltage levels of 220kV and above, and the field of original accident abnormality source data extracted from the direct current accident and abnormality module comprises emergency starting and stopping conditions of a unit, mode adjustment, line inspection conditions, unit processing adjustment conditions, load adjustment, network adjustment and reduction power receiving conditions, power transfer conditions, frequency change conditions, countermeasures, accident influence, power transmission size before locking and direct current accident abnormality conditions;
the operation abnormality module stores operation abnormality of equipment with voltage class of 220kV and above, and the field of original accident abnormality source data extracted from the operation abnormality module comprises equipment name, station name, operation content and result for triggering accident abnormality and subsequent operation;
the accident monitoring module and the abnormality monitoring module correspondingly store monitoring accidents and monitoring abnormalities of equipment with voltage levels of 220kV and above, and the fields of original accident abnormality source data extracted from the monitoring accidents and the monitoring abnormalities comprise recovery conditions, accident handling conditions, fault/abnormality conditions, equipment names and station names;
the ground-control accident and anomaly module stores the dispatching accidents and anomalies of 220kV and above of the voltage level of the equipment, and the fields of the original accident anomaly source data extracted from the dispatching accidents and anomalies comprise equipment names, station names, subordinate self-switching action conditions, single power supply risks of 35kV and above, protection user conditions, important user conditions, power-off user conditions, reclosing conditions and loss loads.
5. The source data of the current power grid shutdown and re-service equipment unit 24 come from an overhaul work sheet, and the equipment running state check comes from the real-time running state of equipment in the D5000 database; the source data to be extracted in the processing process comprises equipment name, station name, planning time, start time, finishing time, flow state and equipment type data; matching equipment objects and plant station objects from the regulation cloud; further providing equipment voltage class information; and finally, storing the processed data into a unit equipment shutdown time table.
6. The source data of the weather forecast unit 25 of the week comes from a weather database, and the source data to be extracted in the processing process comprises early weather, late weather, minimum temperature and maximum temperature; the data analysis is obtained directly from the weather database.
7. The source data of the whole-network power balance condition unit 26 is from a power and energy compiling system and a dispatching database, and the source data required to be extracted comprises the estimated highest power load, the highest power receiving power of the present day, the highest power generation output of the present day, the available load of the present day, the peak rotation standby minimum value of the present day, the valley load of the present day, the ordered power demand of the present day, the yesterday gate fuel oil stock available days, the electric power plant coal stock, the yesterday power plant coal stock and the yesterday power plant coal stock; extracting the estimated highest temperature and the estimated lowest temperature from a meteorological database;
8. the source data of the dc power planning unit 27 is derived from the dc planned power in the D5000 database, and the real-time power is checked based on the dc status and rated power of the device.
9. The source data of the equipment maintenance risk early warning unit 28 come from main network risk early warning and distribution network risk early warning in the OMS system, the state is published, and the date is the same day. The equipment overhaul risk early-warning unit 28 is used for analyzing a plurality of early-warning control measures for risk assessment of a main network overhaul mode of 220 kilovolts and more, and analyzing the risk assessment and early-warning control measures for a current overhaul mode of a distribution network of 35 kilovolts and more;
10. the holiday air temperature, load and electricity consumption condition unit 29 is used for analyzing the highest air temperature/lowest air temperature, the holiday average highest air temperature/average lowest air temperature and the corresponding values, load and electricity consumption conditions of the same date in the last year;
the dates are arranged downwards in sequence. The last line shows the average value during this period. Each data value holds a fraction of one bit. In addition, the maximum "highest air temperature" value in the same period of the last year and the present year is marked red, and the minimum "lowest air temperature value is marked red. Holidays require data such as actual highest load electricity load in the current year, daily highest/lowest electricity load and daily electricity quantity on the same date in the last year, and corresponding growth rate. And calculating an average value of each attribute over the holiday date. The standard redness of each table is as follows, the actual highest load and lowest load in the current year/last year, the daily electric quantity takes the minimum value and the standard redness; the average growth rate is marked red at the maximum value. If there is no value on a certain day, no value is added to the average calculation. All data growth rates in the table hold two-bit decimal and the remaining values hold integers. Average peak voltage and average valley voltage of 16 stations or 14 partitions, maximum value of the highest voltage, minimum value of the lowest voltage, total value of the upper limit times and the lower line times, and average value of the qualification rate. All voltage values retain one decimal, and the qualification rate retains two decimal
The accident abnormal data objectifying module 31 adopts unified plant station equipment information to objectify, normalize and structure the original accident abnormal description in the power grid accident abnormal unit. The power grid accident abnormal unit 23 extracts original accident abnormal source data from the accident dispatching module, the abnormal dispatching module, the direct current accident and abnormal module, the operation abnormal module, the accident monitoring module, the abnormal monitoring module, the ground dispatching accident and abnormal module; in the process of analyzing accident abnormal source data, the accident abnormal data objectifying module 31 firstly extracts the station name, the equipment name, the description and the processing condition information in the original data of the power grid accident abnormal unit 23, and then finds out the corresponding object from the regulation cloud equipment library according to the station name and the equipment name; and then, according to the corresponding keywords, cutting words from the original description text, extracting needed information section by section to form corresponding standardized recorded data, and dividing the standardized and structured recorded data into 6 major categories including direct current categories, power plants, abnormal operation categories, ground adjustment categories, main network line equipment categories and main network non-line equipment categories.
The maintenance work sheet data objectification module 32 obtains the downtime sequence of each type of equipment by analyzing the maintenance work sheet. The maintenance work sheet data objectifying module 32 extracts unaddressed and archived but unprocessed data from the maintenance work sheet every hour, and extracts the equipment name, the station name, the equipment type, the flow state, the planning time, the start time, the finishing time and the equipment state respectively;
then matching the extracted equipment names, the station names and the equipment types with data stored in the regulation cloud to obtain a unit object and a power plant object; analyzing the equipment state of each equipment, corresponding the equipment state to the standard description in the regulation cloud, and constructing the unit equipment out-of-service time according to the flow state, the planning time, the starting time and the finishing time of the overhaul work order;
then, according to different equipment types, dividing the equipment into power grid equipment and unit equipment;
judging whether the unit equipment is a peak shaver unit or a coal-fired unit or whether the capacity is greater than or equal to 300MW, if so, setting the data to be displayed in a report, and storing the processed data into a unit equipment outage time table;
judging whether the power grid equipment is direct current or not, judging whether the direct current to which the equipment belongs is direct current of a reed , and if the direct current is direct current of a reed , setting the direct current equipment not to be displayed on the PPT; if the voltage class of the equipment is 1000/500/220kV, if the voltage class is 1000/500kV, the equipment type is not a main transformer/bus/line, and the equipment type is not displayed in a report; if the voltage class is 220kV, the type of the power grid equipment is not bus/line, and the power grid equipment is not displayed in a report; and storing the processed data into the power grid equipment in-service time.
The invention discloses a production early report application framework based on a regulation cloud, which is used for summarizing, refining and intelligently mining and analyzing a large amount of data related to power grid dispatching operation conditions. Relating to various professions such as scheduling, planning, relay protection, communication, automation, synthesis and the like and various relevant business departments of a company. The information for scheduling production daily report to be summarized and analyzed comprises the following steps: the system comprises power grid model information, scheduling log information, power generation plan information, load information, electric power and electricity balance, power grid equipment operation information, stability control information, overhaul information, power grid fault information, important section tide information, frequency modulation peak regulation operation information, new equipment production, tie line planning and transaction information, external activity electricity protection event information, weather pre-report, disaster event information and the like. The scheduling production daily report not only needs to analyze the production operation condition of the power grid in the previous day, but also needs to analyze and predict the operation plan of the next day and the subsequent period of time.
The production early report research is based on a construction method of a regulation and control cloud data platform. And collecting the related structural information, unstructured information and geographic information of the power grid production operation based on the CIM/E power system object model.
The production early report adopts artificial intelligence technology, data mining technology and other technologies to analyze and process data. The data mining technology searches for the rule from a large amount of data, and mainly comprises 3 steps of data preparation, rule searching and rule representation. Data preparation is to select the required data from the relevant data sources and integrate the data into a data set for data mining; the rule searching is to find out the rule contained in the data set by a certain method; the rule representation is a representation of the found rule as well as possible in a way that is user-understandable (e.g. visual). Specific data analysis methods for data mining include association analysis, cluster analysis, classification analysis, anomaly analysis, specific group analysis, evolution analysis, and the like.
The production early report research visualization technology realizes automatic generation of the content of the scheduling daily report. The visualization technology is mainly used for clearly and effectively conveying and communicating information by means of a graphical means, so that relatives can understand and know data conveniently, and the visualization display method comprises two-dimensional, three-dimensional, geographic diagrams and animation.
In summary, the production early report application framework based on the regulation cloud integrates production early report with the analysis advanced application of the regulation cloud power system, shows key conclusions related to the simulation analysis of the operation depth of the power grid, and also supports and schedules daily application by researching multi-subject templatized storage and configurable automatic generation technology of early report.
It will be appreciated by persons skilled in the art that the above embodiments are provided for illustration only and not for limitation of the invention, and that variations and modifications of the above described embodiments are intended to fall within the scope of the claims of the invention as long as they fall within the true spirit of the invention.
Claims (4)
1. The production early report application framework based on the regulation cloud is characterized by comprising a source data layer, an application analysis layer and a data objectification layer, wherein:
the source data layer comprises an accident and abnormality module, an overhaul work sheet, a meteorological database, an electric power and energy compiling system and a scheduling database;
the application analysis layer comprises the following units:
full network electric power and electricity unit: the data for analyzing the electric quantity of the whole network comprises data of air temperature, electric load, peak-valley difference of electric power, electric power receiving, power generation output, electric quantity, generated energy, electric power receiving, electric load rate and power generation load rate, wherein the generated energy data comprises a historical highest value, a month accumulated value, a month increasing rate, an year accumulated value and an year increasing rate;
regional power and electricity unit: the method comprises the steps of analyzing yesterday highest load, historical highest load, month highest homonymy, daily electric quantity, month accumulation, month increase, year accumulation and year increase data of each region;
and a main power unit start-stop unit: checking the real-time running state of the unit, wherein the data to be extracted in the processing process comprises the following steps: matching a unit object from the regulation cloud, the power plant object, further providing unit capacity and whether the unit is a coal-fired unit or peak shaver unit information, and finally storing the processed data into a unit equipment outage time table;
an electric network accident abnormal unit: the source data come from an accident and abnormality module, the accident and abnormality module is divided into 7 modules, namely an accident scheduling module, an abnormality scheduling module, a direct current accident and abnormality module, an operation abnormality module, an accident monitoring module, an abnormality monitoring module and a ground regulation accident and abnormality module, and the objects to be extracted are plant station objects and equipment objects; the content to be extracted comprises fault description, fault influence, fault reason, influence recovery and equipment recovery data;
current power grid outage and restoration equipment unit: the source data come from an overhaul work order, and the equipment running state check comes from the real-time running state of the equipment; the source data to be extracted in the processing process comprises equipment name, station name, planning time, start time, finishing time, flow state and equipment type data; matching equipment objects and plant station objects from the regulation cloud; further providing equipment voltage class information; finally, storing the processed data into a unit equipment retirement time table;
a week weather forecast unit: the source data is from a meteorological database, and the source data required to be extracted in the processing process comprises early weather, late weather, minimum temperature and maximum temperature;
full network power balance condition unit: the source data is from a power and energy compiling system and a dispatching database, and the source data to be extracted comprises the estimated highest power consumption load, the highest power receiving power of the present day, the highest power generation output of the present day, the available load of the present day, the peak rotation standby of the present day, the minimum value of the peak rotation standby of the present day, the present day low valley load, the present day ordered power consumption requirement, the yesterday gate fuel stock available days, the yesterday power plant coal stock and the yesterday power plant coal stock average available days; extracting the estimated highest temperature and the estimated lowest temperature from a meteorological database;
a direct current power planning unit: checking the real-time power according to the direct current state and rated power of the equipment;
equipment overhaul risk early warning unit: analyzing 220 kilovolt and above main network one week overhaul mode risk assessment and early warning control measures, and analyzing 35 kilovolt and above distribution network current overhaul mode risk assessment and early warning control measures;
holiday air temperature, load and electricity consumption condition unit: analyzing the corresponding values, loads and electricity consumption conditions of the highest air temperature/lowest air temperature, the average highest air temperature/average lowest air temperature and the same date in the holiday of the current year;
the data objectification layer comprises an accident abnormal data objectification module and a maintenance work data objectification module, wherein the accident abnormal data objectification module adopts unified station equipment information to objectify, normalize and structure the original accident abnormality description in the power grid accident abnormal unit; and the maintenance work sheet data objectification module obtains the out-of-service time sequence data of each type of equipment by analyzing the maintenance work sheet.
2. The production early report application framework based on the regulation cloud according to claim 1, wherein the accident scheduling module and the abnormal scheduling module store the scheduling accidents and scheduling anomalies of the equipment with the voltage level of 220kV and above in a one-to-one correspondence manner, and the fields of the original accident anomaly source data extracted from the accident anomaly source data comprise a station name, an equipment name, a protection action, a fault phase, mode adjustment, a line inspection condition, a partition and ring release condition, a fault ranging information/unit emergency start-stop condition, a unit processing regulation condition, a load transfer condition, a lost load, a lower-level self-cutting action condition, a single power supply risk of 35kV and above, a protection user condition, an important user condition, a power loss user condition and a reclosing condition;
the direct current accident and abnormality module stores direct current accidents and abnormalities of equipment with voltage levels of 220kV and above, and the fields of original accident abnormality source data extracted from the direct current accident and abnormality module comprise emergency start-stop conditions of a unit, mode adjustment, line inspection conditions, unit processing adjustment conditions, load adjustment, network adjustment and reduction power receiving conditions, power transfer conditions, frequency change conditions, countermeasures, accident influence, power transmission size before locking and direct current accident abnormality conditions;
the operation abnormality module stores operation abnormalities of equipment with voltage class of 220kV and above, and the field of original accident abnormality source data extracted from the operation abnormalities comprises equipment names, station names, operation content and results triggering accident abnormalities and subsequent operations;
the accident monitoring module and the abnormality monitoring module correspondingly store monitoring accidents and monitoring abnormalities of equipment with voltage levels of 220kV and above, and the fields of original accident abnormality source data extracted from the monitoring accidents and the monitoring abnormalities comprise recovery conditions, accident handling conditions, fault/abnormality conditions, equipment names and station names;
the ground-control accident and abnormality module stores dispatching accidents and abnormalities with the equipment voltage level of 220kV and above, and the fields of original accident abnormality source data extracted from the dispatching accidents and abnormalities comprise equipment names, station names, subordinate self-switching action conditions, single power supply risks of 35kV and above, protection user conditions, important user conditions, power-loss user conditions, reclosing conditions and loss loads.
3. The regulation cloud-based production early report application framework of claim 1, wherein the power grid accident anomaly unit extracts original accident anomaly source data from an accident scheduling module, an anomaly scheduling module, a direct current accident and anomaly module, an operation anomaly module, an accident monitoring module, an anomaly monitoring module, a local regulation accident and anomaly module; the accident abnormal data objectification module firstly extracts the station name, the equipment name, the description and the processing condition information in the original data of the power grid accident abnormal unit in the process of analyzing the accident abnormal source data, and then finds out the corresponding object from the regulation cloud equipment library according to the station name and the equipment name; and then, according to the corresponding keywords, cutting words from the original description text, extracting needed information section by section to form corresponding standardized recorded data, and dividing the standardized and structured recorded data into 6 major categories including direct current categories, power plants, abnormal operation categories, ground adjustment categories, main network line equipment categories and main network non-line equipment categories.
4. The regulatory cloud-based production early report application framework of claim 1, wherein the overhaul work data objectification module extracts unaddressed and archived but unprocessed data from an overhaul work sheet every hour, and extracts a device name, a station name, a device type, a flow state, a planning time, a start time, a finishing time and a device state thereof, respectively;
then matching the extracted equipment names, the station names and the equipment types with data stored in the regulation cloud to obtain a unit object and a power plant object; analyzing the equipment state of each equipment, corresponding the equipment state to the standard description in the regulation cloud, and constructing the unit equipment out-of-service time according to the flow state, the planning time, the start time and the finishing time of the overhaul work order;
then, according to different equipment types, dividing the equipment into power grid equipment and unit equipment;
judging whether the unit equipment is a peak shaver unit or a coal-fired unit or whether the capacity is greater than or equal to 300MW, if so, setting the data to be displayed in a report, and storing the processed data into a unit equipment outage time table;
judging whether the power grid equipment is direct current or not, judging whether the direct current to which the equipment belongs is direct current of a reed , and if the direct current is direct current of a reed , setting the direct current equipment not to be displayed on the PPT; if the voltage class of the equipment is 1000/500/220kV, if the voltage class is 1000/500kV, the equipment type is not a main transformer/bus/line, and the equipment type is not displayed in a report; if the voltage class is 220kV, the type of the power grid equipment is not bus/line, and the power grid equipment is not displayed in a report; and then storing the processed data into a power grid equipment outage schedule.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101329395B1 (en) * | 2012-06-04 | 2013-11-14 | 한국남동발전 주식회사 | Power plant equipment management system and control method for thereof |
| CN104156810A (en) * | 2014-07-31 | 2014-11-19 | 国网山东省电力公司 | Power dispatching production management system based on cloud computing and realization method of power dispatching production management system |
| CN105787606A (en) * | 2016-03-24 | 2016-07-20 | 国网辽宁省电力有限公司电力科学研究院 | Power dispatching online trend early warning system based on ultra short term load prediction |
| CN108898246A (en) * | 2018-06-19 | 2018-11-27 | 深圳供电局有限公司 | Load prediction management system based on power system |
| CN109636095A (en) * | 2018-10-31 | 2019-04-16 | 中国电力科学研究院有限公司 | A kind of grid equipment Unified Model management system based on regulation cloud framework |
-
2019
- 2019-11-08 CN CN201911086659.2A patent/CN110796567B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101329395B1 (en) * | 2012-06-04 | 2013-11-14 | 한국남동발전 주식회사 | Power plant equipment management system and control method for thereof |
| CN104156810A (en) * | 2014-07-31 | 2014-11-19 | 国网山东省电力公司 | Power dispatching production management system based on cloud computing and realization method of power dispatching production management system |
| CN105787606A (en) * | 2016-03-24 | 2016-07-20 | 国网辽宁省电力有限公司电力科学研究院 | Power dispatching online trend early warning system based on ultra short term load prediction |
| CN108898246A (en) * | 2018-06-19 | 2018-11-27 | 深圳供电局有限公司 | Load prediction management system based on power system |
| CN109636095A (en) * | 2018-10-31 | 2019-04-16 | 中国电力科学研究院有限公司 | A kind of grid equipment Unified Model management system based on regulation cloud framework |
Non-Patent Citations (1)
| Title |
|---|
| 调控云架构及应用展望;许洪强;电网技术;第41卷(第10期);3104-3111 * |
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