CN110796567A - Production early-newspaper application framework based on regulation cloud - Google Patents
Production early-newspaper application framework based on regulation cloud Download PDFInfo
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Abstract
The invention discloses a production early-report application framework based on a regulation cloud, which comprises a source data layer, an application analysis layer and a data objectification layer. The application analysis layer comprises a whole-grid power and electricity quantity unit, a regional power and electricity quantity unit, a main power unit starting and stopping unit, a power grid accident abnormity unit, a current power grid stopping and returning equipment unit, a one-week weather forecast unit, a whole-grid power balance condition unit, a direct-current power planning unit, an equipment maintenance risk early warning unit and a holiday temperature, load and electricity consumption condition unit. The data objectification layer comprises an accident abnormity data objectification module and a maintenance work order data objectification module. The production early report application framework based on the regulation cloud integrates the production early report and the regulation cloud power system analysis advanced application, displays relevant key conclusions of power grid operation depth simulation analysis, and supports scheduling daily application by researching multi-topic templated storage and configurable automatic generation technology of the early report.
Description
Technical Field
The invention relates to a production early-report application framework based on a regulation cloud.
Background
The early newspaper production is an important hand for the production, operation and management of Shanghai companies. The regulation center has accumulated abundant experience in the aspect of production early report compiling application. The early production report requires timely and accurate collection of professional information of power grid dispatching operation, and correlation analysis is deeply developed.
At present, the compilation of the early newspaper production is mainly completed by a way of manufacturing PPT by professional personnel. With the continuous promotion of the construction of the 'regulation cloud' of the national power grid company, the regulation cloud-based production early report objectification and analysis technology research conditions are provided, so that the intelligent data convergence advantage of a regulation cloud big data platform is fully exerted, the production early report compiling efficiency and the data correlation analysis capability are further improved, and powerful support is provided for the safe production of the power grid.
The early production report is a typical power grid dispatching big data application scene, closely meets the production application requirements, and has high practical value and application and popularization value in the related technology.
The national power grid company highly attaches importance to the research and application of the big data technology, and builds a national power grid cloud platform with more flexible resource allocation, more centralized and intelligent data utilization, more uniform and efficient service integration, and quicker and more convenient application and development. The regulation cloud platform with physical distribution and logic concentration is built by taking 'inheritance and innovation are combined, practical effects fall on the ground' as a basic principle, combining the operation state of a regulation system, introducing new generation information technologies such as cloud computing and big data and the like.
In the aspect of scheduling data objectification, the regulation and control cloud platform fully absorbs the thinking and experience of model data standards related to the intelligent power grid scheduling control technology at home and abroad, including IEC61970 and the localized expansion of CIM/E technical standards, and solves the problem of model data objectification information sharing between scheduling data centers and between various specialties in the scheduling centers. In the specific technical implementation, the regulation and control cloud deploys the functions of uniformly maintaining and distributing the metadata and the data dictionary, and each application end and source data end can subscribe and receive the metadata and the dictionary data in a subscription mode.
The data cloud platform is used as an important component of a regulation cloud, provides data management and data service, and comprises a physical power grid, entity equipment and an organization structure oriented system, performs abstract modeling and standard-division design on the relationship between power regulation business objects and the relationship between the power regulation business objects, and establishes a standard and uniform 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 models, operation and real time are achieved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a production early report Application (APP) framework based on a regulation cloud, which integrates the production early report with the advanced analysis application of a regulation cloud power system, shows the key conclusion about the simulation analysis of the operation depth of a power grid, and also supports the daily application of the scheduling by researching the multi-topic templated storage and configurable automatic generation technology of the early report.
The technical scheme for realizing the purpose is as follows: a production early report application framework based on a regulation cloud comprises a source data layer, an application analysis layer and a data objectification layer, wherein:
the source data layer comprises an accident and abnormity module, a maintenance work list, a meteorological database, an electric power and electric energy compiling system and a scheduling database;
the application analysis layer comprises the following units:
whole grid electric power quantity unit: the data used for analyzing the power and the electric quantity of the whole network comprise air temperature, power load, power consumption peak-valley difference, received power, generated power output, power consumption, generated energy, received power, power load rate and generated load rate data, wherein the generated energy data comprise a historical maximum value, a monthly accumulated value, a monthly growth rate, an annual accumulated value and an annual growth rate;
regional electric power quantity unit: the method is used for analyzing yesterday highest load, historical highest load, current month highest year-year same-year, daily electricity, month accumulation, month growth, year accumulation and year growth data of each region;
the main power unit starts and stops the 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 and a power plant object from a regulation cloud, further providing information about unit capacity and whether the unit is a coal-fired unit or a peak shaving unit, and finally storing the processed data into a unit equipment outage and restoration schedule;
the power grid accident exception unit: the source data come from an accident and exception module, the accident and exception module is divided into 7 modules which are respectively an accident scheduling module, an exception scheduling module, a direct-current accident and exception module, an operation exception module, an accident monitoring module, an exception monitoring module and a local dispatching accident and exception 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;
the current power grid outage and restoration equipment unit: the source data come from a maintenance work list, and the running state of the equipment is checked and 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, completion time, flow state and equipment type data; matching the equipment object and the plant station object from the regulation cloud; further providing equipment voltage grade information; finally, storing the processed data into a machine set equipment outage schedule;
a one-week weather forecasting unit: the source data is from a meteorological database, and the source data needing to be extracted in the processing process comprises morning weather, evening weather, lowest temperature and highest temperature;
full-grid power balance condition unit: the source data come from an electric power and energy compiling system and a scheduling database, and the source data needing to be extracted comprise the predicted highest power utilization load today, the highest power receiving power of the peak today, the highest power generation output today, the available load today, the spinning reserve of the peak today, the minimum value of the spinning reserve of the peak today, the load of the valley today, the ordered power utilization demand of the day, the gate fuel stock of yesterday, the available days of the gate fuel stock of yesterday, the power plant coal stock of yesterday, the average available days of the power plant coal stock of yesterday; extracting the predicted highest air temperature and the predicted lowest air temperature today from a meteorological database;
a direct current power planning unit: checking the real-time power according to the direct current state and the rated power of the equipment;
equipment overhaul risk early warning unit: analyzing 220KV and above main network one-week overhaul mode risk assessment early warning control measures, and analyzing 35KV and above distribution network current overhaul mode risk assessment and early warning control measures;
temperature, load and electricity consumption situation unit in holidays: analyzing the daily maximum air temperature/minimum air temperature, the average maximum air temperature/average minimum air temperature of the holidays in the current year, and corresponding values, loads and power consumption conditions of the same date in the last year;
the data objectification layer comprises an accident abnormal data objectification module and a maintenance work order data objectification module, and the accident abnormal data objectification module adopts uniform plant station equipment information to objectify, standardize and structure the original accident abnormity description in the power grid accident abnormity unit; and the overhaul work sheet data objectification module obtains the outage time sequence data of each type of equipment by analyzing the overhaul work sheet.
The production early-reporting application framework based on the regulation cloud is characterized in that the accident scheduling module and the abnormal scheduling module correspondingly store scheduling accidents and scheduling abnormalities of equipment with the voltage level of 220kV or more one by one, and the field of original accident abnormality source data extracted from the accident scheduling module and the abnormal scheduling module comprises a station name, an equipment name, a protection action, a fault phase, mode adjustment, line patrol conditions, a partition and a ring release, fault location information/unit emergency start-stop conditions, unit processing and regulation conditions, load transfer conditions, load loss, lower-level automatic switching action conditions, single power supply risks of 35kV or more, user protection conditions, important user conditions, power loss user conditions and reclosing conditions;
the direct current accident and abnormity module stores direct current accidents and abnormity of equipment with the voltage grade of 220kV or above, and the field of original accident abnormity source data extracted from the direct current accident and abnormity module comprises the emergency starting and stopping condition of a unit, mode adjustment, line patrol condition, unit processing and adjusting condition, load adjustment, network regulation and reduction power receiving condition, power transfer condition, frequency change condition, counter measure, accident influence, power transmission size before locking and direct current accident abnormity condition;
the operation abnormity module stores the operation abnormity of the equipment with the voltage grade of 220kV or above, and the field of the original accident abnormity source data extracted from the operation abnormity module comprises the equipment name, the station name, the operation content and result of triggering the accident abnormity and the subsequent operation;
the accident monitoring module and the abnormity monitoring module correspondingly store monitoring accidents and monitoring abnormity of equipment with the voltage grade of 220kV or above, and the field of original accident abnormity source data extracted from the monitoring accidents comprises a recovery condition, an accident handling condition, a fault/abnormity condition, an equipment name and a plant and station name;
the dispatching accident and abnormity module stores dispatching accidents and abnormity with the voltage class of equipment of 220kV or above, and the field of original accident abnormity source data extracted from the dispatching accidents and abnormity module comprises equipment name, station name, lower level automatic switching action condition, single power supply risk of 35kV or above, user protection condition, important user condition, power loss user condition, reclosing condition and loss load.
The production early-reporting application framework based on the regulation cloud is characterized in that the power grid accident exception unit extracts original accident exception source data from an accident scheduling module, an exception scheduling module, a direct-current accident and exception module, an operation exception module, an accident monitoring module, an exception monitoring module and a local regulation accident and exception module; in the process of analyzing accident abnormal source data, 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, and then finds out a corresponding object from a regulation and control cloud equipment library according to the station name and the equipment name; and then according to corresponding keywords, segmenting the original description text, extracting required information section by section to form corresponding standardized record data, and dividing the standardized and structured record data into 6 major categories, including direct current category, power plant category, abnormal operation category, local dispatching category, main network line equipment category and main network non-line equipment category.
The production early-report application framework based on the regulation cloud comprises a maintenance work sheet data objectification module, a production early-report application module and a management cloud application module, wherein the maintenance work sheet data objectification module extracts unarchived data, archived data and unprocessed data from a maintenance work sheet every hour, and respectively extracts equipment names, plant station names, equipment types, flow states, planning times, start-up times, completion times and equipment states;
matching the extracted equipment name, plant station name and equipment type with data stored in a 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 then constructing out-of-service time of the unit equipment according to the flow state, the planning time, the start-up time and the completion time of the maintenance work order;
then dividing the equipment into power grid equipment and unit equipment according to different equipment types;
judging whether the unit equipment is a peak shaving unit or a coal-fired unit or whether the capacity is more than or equal to 300MW, if so, setting the data to be displayed in a report, and storing the processed data in a unit equipment outage schedule;
judging whether the power grid equipment is direct current, judging whether the direct current of the equipment is reed direct current, and if the direct current of the equipment is reed direct current, setting the direct current equipment not to be displayed on PPT; if the voltage level is not direct current, continuously judging whether the equipment voltage level is 1000/500/220kV, if the voltage level is 1000/500kV, the equipment type is not main transformer/bus/line, and the equipment type is not displayed in a report; if the voltage level is 220kV, the type of the power grid equipment is not a 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 and restoration schedule.
The production early report application framework based on the regulation cloud integrates the production early report and the regulation cloud power system analysis advanced application, displays relevant key conclusions of power grid operation depth simulation analysis, and supports scheduling daily application by means of multi-topic templated storage and configurable automatic generation technology for researching the early report.
Drawings
Fig. 1 is a schematic structural diagram of a production early-warning application framework based on a regulatory cloud.
Detailed Description
In order that those skilled in the art will better understand the technical solution of the present invention, the following detailed description is given with reference to the accompanying drawings:
referring to fig. 1, a preferred embodiment of the present invention, a production early-report application framework based on a regulation cloud, 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 abnormity module, a maintenance work list, a meteorological database, an electric power and electric energy compiling system and a scheduling database; the accident and abnormity module and the maintenance work list are stored in the OMS, and the scheduling database adopts a D5000 database.
The application analysis layer 2 comprises a whole grid power and electricity unit 20, a regional power and electricity unit 21, a main power unit start-stop unit 22, a grid accident abnormity unit 23, a current grid stop-and-go equipment unit 24, a one-week weather forecast unit 25, a whole grid power balance condition unit 26, a direct current power planning unit 27, an equipment maintenance risk early warning unit 28 and a holiday temperature, load and electricity consumption condition unit 29.
The data objectification layer 3 includes an accident anomaly data objectification module 31 and a service work order data objectification module 32.
1. The whole grid power and electricity quantity unit 20 is used for analyzing data of whole grid power and electricity quantity, including air temperature, power load, power peak-valley difference, received power, generated power output, power consumption, generated energy, received power, power load rate and generated load rate data, wherein the generated energy data includes historical maximum value, monthly accumulated value, monthly growth rate, yearly accumulated value and yearly growth rate;
the average power generation amount can be calculated according to 24 hours in the D5000 database, and the average power generation amount can also be obtained according to the actual value of the total power generation amount in the EMS daily reporting module.
The data to be analyzed for the power generation amount include a historical maximum value, a monthly cumulative value, a monthly growth rate, an annual cumulative value and an annual growth rate.
The historical maximum value is the maximum value of the historical power generation data in the OMS.
The monthly accumulation is the monthly accumulation value of yesterday in the OMS plus the generating capacity of the day.
The monthly growth takes historical accumulated data in the OMS, and the calculation formula is as follows: month growth (current month accumulation-previous month accumulation)/previous month accumulation 100%.
The year accumulation is the sum of yesterday's year accumulation value in the OMS and the current day's power generation amount.
The annual growth takes historical accumulated data in the OMS, and the calculation formula is as follows: annual growth-last year/last year 100%.
The electrical load factor is a calculated value. The daily electricity consumption is firstly taken, then the highest electricity consumption load is taken, and the specific calculation formula is as follows: the power consumption rate is the power consumption/(the highest power consumption 24) 100%.
The power generation load factor is a calculated value. Firstly, the daily generated energy is taken, and then the highest generated output is taken, wherein the specific calculation formula is as follows: the power generation load factor is the amount of power generation/(maximum power generation output 24) 100%.
The power generation load factor (new) is a calculated value. The above-mentioned middle-day generated energy is firstly taken, and the "new highest generated output" is calculated. The new generated output calculation formula is as follows: the new electric output is the total highest electric output-16 electric power plants 'electric power output +16 electric power plants' set rated electric power output after starting up (note: 16 electric home appliances factory includes outer high bridge, outer high bridge two factory, outer high bridge three factory, stone cave mouth abnormity, stone cave mouth two factory, new stone two factory, upper electric Jing, Wujing six stage, Wujing two factory, Jing thermoelectricity, Xianxian gas turbine, stone cave mouth gas turbine, upper temporary gas turbine, Lujing gas turbine, Xijing gas turbine, Xixian two factory). And then removing the maximum value of the new power generation output to obtain the new highest power generation output. The new calculation formula of the power generation load rate is as follows: the power generation load factor is the power generation amount/(new maximum power generation output 24) 100%.
2. The region power and power unit 21 is used for analyzing data of 11 regions such as Shanghai Pudong, North City, south City, City district, Jiading, Songjiang, Fengxian, Jinshan mountain, Qingpu, Chongming and Changxing, such as the highest yesterday load, the highest year-on-year ratio in the month, the daily power, the monthly accumulation, the monthly growth, the annual accumulation and the annual growth;
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 includes: the method comprises the steps that a unit name, a power plant name, planning time (dispatching desk batch time), start time (dispatching desk waiting to start-dispatching command time), completion time (dispatching desk waiting to finish-construction completion time), process state, equipment state and equipment type are matched from a regulation cloud, a unit object and a power plant object are matched, unit capacity and information whether the unit is a coal-fired unit or a peak regulation unit are further provided, and finally processed data are stored in a unit equipment stop-return schedule;
4. the source data of the power grid accident exception unit 23 come from an accident and exception module, the accident and exception module is divided into 7 modules which are respectively an accident scheduling module, an exception scheduling module, a direct current accident and exception module, an operation exception module, an accident monitoring module, an exception monitoring module and a local dispatching accident and exception module, and the objects to be extracted are plant station objects and equipment objects; the content to be extracted includes a fault description, fault impact, fault cause, impact recovery, and device recovery data.
The method comprises the steps that dispatching accidents and dispatching anomalies of equipment with the voltage class of 220kV or more are stored in an accident dispatching module and an anomaly dispatching module in a one-to-one correspondence mode, and fields of original accident anomaly source data extracted from the accident dispatching module comprise station names, equipment names, protection actions, fault phases, mode adjustment, line patrol conditions, subareas and ring-opening conditions, fault location information/unit emergency start-stop conditions, unit processing and adjusting conditions, load transfer conditions, lost loads, lower-level automatic switching action conditions, single power risks of 35kV or more, user protection conditions, important user conditions, power-loss user conditions and reclosing conditions;
the direct current accident and abnormity module stores direct current accidents and abnormity of equipment with the voltage grade of 220kV or above, and fields of original accident abnormity source data extracted from the direct current accident and abnormity module comprise unit emergency starting and stopping conditions, mode adjustment, line patrol conditions, unit processing and adjusting conditions, load adjustment, network regulation and reduction power receiving conditions, power transfer conditions, frequency change conditions, countermeasures, accident influence, power transmission size before locking and direct current accident abnormity conditions;
the operation abnormity module stores the operation abnormity of the equipment with the voltage grade of 220kV or above, and the field of the original accident abnormity source data extracted from the operation abnormity module comprises the equipment name, the station name, the operation content and the result of triggering the accident abnormity and the subsequent operation;
the accident monitoring module and the abnormity monitoring module correspondingly store monitoring accidents and monitoring abnormity of equipment with the voltage grade of 220kV or above, and the field of original accident abnormity source data extracted from the monitoring accidents comprises recovery conditions, accident handling conditions, failure/abnormity conditions, equipment names and station names;
the dispatching accident and abnormity module stores dispatching accidents and abnormity with the voltage class of equipment of 220kV or above, and the field of original accident abnormity source data extracted from the dispatching accidents and abnormity module comprises equipment name, station name, lower level automatic switching action condition, single power supply risk of 35kV or above, protection user condition, important user condition, power loss user condition, reclosing condition and loss load.
5. The source data of the current power grid outage and restoration equipment unit 24 come from a maintenance work list, and the equipment running state checks the real-time running state of equipment from a D5000 database; the source data to be extracted in the processing process comprises equipment name, station name, planning time, start time, completion time, flow state and equipment type data; matching the equipment object and the plant station object from the regulation cloud; further providing equipment voltage grade information; and finally, storing the processed data into a machine set equipment outage schedule.
6. The source data of the one-week weather forecast unit 25 is from a weather database, and the source data to be extracted in the processing process comprises morning weather, evening weather, lowest temperature and highest temperature; data analysis is directly obtained from the meteorological database.
7. The source data of the whole-grid power balance condition unit 26 is from a power and energy planning system and a scheduling database, and the source data to be extracted includes the predicted highest power utilization load at this day, the highest power receiving power at the peak at this day, the highest power generation output at this day, the available load at this day, the peak spinning reserve at this day, the minimum value of the peak spinning reserve at this day, the valley load at this day, the ordered power utilization demand at this day, the gate fuel stock at yesterday, the available days of the gate fuel stock at yesterday, the power plant coal stock at yesterday, and the average available days of the power plant coal stock at yesterday; extracting the predicted highest air temperature and the predicted lowest air temperature today 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 according to the dc status and the rated power of the device.
9. The source data of the equipment overhaul risk early warning unit 28 is from the main network risk early warning and the distribution network risk early warning in the OMS system, the state is published, and the date is the current day. The equipment overhaul risk early warning unit 28 is used for analyzing the early warning control measures of 220KV and above main network one-week overhaul mode risk assessment, and analyzing the risk assessment and early warning control measures of 35KV and above distribution network current overhaul mode;
10. the holiday air temperature, load and electricity consumption condition unit 29 is used for analyzing the daily maximum air temperature/minimum air temperature, holiday average maximum air temperature/average minimum air temperature and corresponding values, loads and electricity consumption conditions of the same date of the last year during the holiday of the current year;
the dates are arranged in chronological order downwards. The last row shows the average over this period. Each data value retains a one-bit fraction. In addition, the maximum "highest temperature" value in the same period of the last year and the present year is marked red, and the minimum "lowest temperature value is marked red. The data of the actual highest load electricity load in the current year, the highest/lowest electricity load daily in the same date in the last year, the daily electricity quantity and the like are required to be taken in the holidays, and the corresponding growth rate is also required. And calculating an average value of each attribute over the holiday date. Marking the red of each table as follows, and marking the red with the minimum value of the actual maximum load and the minimum load of the current year/last year and the daily electric quantity; the average growth rate is marked red at the maximum. In case there is no value on a certain day, it is not added to the calculation of the mean value. All data growth rates in the table are reserved with two decimal places, and the remaining values are reserved with integers. The average value of the average peak voltage and the average valley voltage of 16 stations or 14 partitions, the maximum value of the highest voltage, the minimum value of the lowest voltage, the total value of the upper limit-crossing times and the lower line-crossing times and the average value of the qualified rate. One decimal is reserved for all voltage values, and two decimal places are reserved for the qualification rate
The accident anomaly data objectification module 31 adopts uniform plant station equipment information to objectify, standardize and structure the original accident anomaly description in the power grid accident anomaly unit. The power grid accident exception unit 23 extracts original accident exception source data from an accident scheduling module, an exception scheduling module, a direct current accident and exception module, an operation exception module, an accident monitoring module, an exception monitoring module and a local dispatching accident and exception module; in the process of analyzing the accident anomaly source data, the accident anomaly data objectification 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 anomaly unit 23, and then finds out a corresponding object from the regulation and control cloud equipment library according to the station name and the equipment name; and then according to corresponding keywords, segmenting the original description text, extracting required information section by section to form corresponding standardized record data, and dividing the standardized and structured record data into 6 major categories, including direct current category, power plant category, abnormal operation category, ground dispatching category, main network line equipment category and main network non-line equipment category.
The maintenance work order data objectification module 32 obtains the outage sequence data of each type of equipment by analyzing the maintenance work order. The overhaul work sheet data objectification module 32 extracts unarchived and archived but unprocessed data from the overhaul work sheet every hour, and respectively extracts the equipment name, the plant station name, the equipment type, the flow state, the planning time, the start time, the completion time and the equipment state;
matching the extracted equipment name, plant station name and equipment type with data stored in a 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 then constructing out-of-service time of the unit equipment according to the flow state, the planning time, the start-up time and the completion time of the maintenance work order;
then dividing the equipment into power grid equipment and unit equipment according to different equipment types;
judging whether the unit equipment is a peak shaving unit or a coal-fired unit or whether the capacity is more than or equal to 300MW, if so, setting the data to be displayed in a report, and storing the processed data in a unit equipment outage schedule;
judging whether the power grid equipment is direct current, judging whether the direct current of the equipment is reed direct current, and if the direct current of the equipment is reed direct current, setting the direct current equipment not to be displayed on PPT; if the voltage level is not direct current, continuously judging whether the equipment voltage level is 1000/500/220kV, if the voltage level is 1000/500kV, the equipment type is not main transformer/bus/line, and the equipment type is not displayed in a report; if the voltage level is 220kV, the type of the power grid equipment is not a bus/line, and the power grid equipment is not displayed in a report; and then storing the processed data into the outage and restoration time of the power grid equipment.
According to the production early report application framework based on the regulation cloud, the production early report is a summary, extraction and intelligent mining analysis of a large amount of data related to the scheduling operation condition of the power grid. The method relates to various specialties such as scheduling, planning, relay protection, communication, automation, synthesis and the like and various related business departments of companies. The information required for summarizing and analyzing the production daily report scheduling comprises the following information: the system comprises power grid model information, scheduling log information, power generation plan information, load information, power and electricity balance, power grid equipment operation information, stability control information, overhaul information, power grid fault information, important section tidal current information, frequency modulation and peak regulation operation information, new equipment commissioning, tie line plan and transaction information, external activity and power protection item information, weather forecast, disaster event information and the like. The production schedule scheduling needs to analyze the production operation condition of the power grid in the previous day and also needs to analyze and predict the operation plan in the next day and a subsequent period of time.
The early production report research is based on a construction method of a regulation cloud data platform. Based on a CIM/E power system object model, structured information, unstructured information and geographic information related to power grid production and operation are collected.
The early production report is subjected to data analysis processing by adopting technologies such as artificial intelligence technology, data mining and the like. 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. The data preparation is to select required data from related 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 regular expression represents the found regular expression in a way (such as visualization) which can be understood by a user as much as possible. Specific data analysis methods of data mining include association analysis, cluster analysis, classification analysis, anomaly analysis, specific group analysis, evolution analysis and the like.
The production early newspaper research visualization technology realizes the automatic generation of the scheduling daily newspaper content. The visualization technology mainly aims to clearly and effectively convey and communicate information by means of graphical means, so that the data can be conveniently understood and known by related people, and the visualization technology comprises two-dimensional, three-dimensional, geographic map and animation visualization display methods.
In summary, the production early report application framework based on the regulation cloud integrates the production early report and the regulation cloud power system analysis advanced application, displays relevant key conclusions of power grid operation depth simulation analysis, and supports scheduling daily application by researching multi-topic templated storage and configurable automatic generation technology of the early report.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.
Claims (4)
1. The utility model provides a production early newspaper application framework based on regulation and control cloud which characterized in that, includes source data layer, application analysis layer and data objectification layer, wherein:
the source data layer comprises an accident and abnormity module, a maintenance work list, a meteorological database, an electric power and electric energy compiling system and a scheduling database;
the application analysis layer comprises the following units:
whole grid electric power quantity unit: the data used for analyzing the power and electricity quantity of the whole network comprise air temperature, electricity load, electricity peak-valley difference, received electricity, electricity generation output, electricity consumption, electricity generation quantity, electricity receiving quantity, electricity load rate and electricity generation load rate data, wherein the electricity generation quantity data comprise a historical maximum value, a month accumulated value, a month growth rate, an year accumulated value and an year growth rate;
regional electric power quantity unit: the method is used for analyzing yesterday highest load, historical highest load, current month highest year-year same-year, daily electricity, month accumulation, month growth, year accumulation and year growth data of each region;
the main power unit starts and stops the 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 and a power plant object from a regulation cloud, further providing information on unit capacity and whether the unit is a coal-fired unit or a peak shaving unit, and finally storing the processed data into a unit equipment stop-and-go schedule;
the power grid accident exception unit: the source data come from an accident and exception module, the accident and exception module is divided into 7 modules which are respectively an accident scheduling module, an exception scheduling module, a direct-current accident and exception module, an operation exception module, an accident monitoring module, an exception monitoring module and a local dispatching accident and exception 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;
the current power grid outage and restoration equipment unit: the source data come from a maintenance work list, and the running state of the equipment is checked and 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, completion time, flow state and equipment type data; matching the equipment object and the plant station object from the regulation cloud; further providing equipment voltage grade information; finally, storing the processed data into a machine set equipment outage schedule;
a one-week weather forecasting unit: the source data is from a meteorological database, and the source data needing to be extracted in the processing process comprises morning weather, evening weather, lowest temperature and highest temperature;
full-grid power balance condition unit: the source data come from an electric power and energy compiling system and a scheduling database, and the source data needing to be extracted comprise the predicted highest power utilization load today, the highest power receiving power of the peak today, the highest power generation output today, the available load today, the spinning reserve of the peak today, the minimum value of the spinning reserve of the peak today, the valley load today, the ordered power utilization demand today, the gate fuel stock of yesterday, the available days of the gate fuel stock of yesterday, the power plant coal stock of yesterday, the average available days of the power plant coal stock of yesterday; extracting the predicted highest air temperature and the predicted lowest air temperature today from a meteorological database;
a direct current power planning unit: checking the real-time power according to the direct current state and the rated power of the equipment;
equipment overhaul risk early warning unit: analyzing the risk assessment early warning control measures of the one-week overhaul mode of the main network of 220 kilovolt or more, and analyzing the risk assessment and early warning control measures of the current overhaul mode of the distribution network of 35 kilovolt or more;
temperature, load and electricity consumption situation unit in holidays: analyzing the daily maximum air temperature/minimum air temperature, the average maximum air temperature/average minimum air temperature of the holidays in the current year, and corresponding values, loads and power consumption conditions of the same date in the last year;
the data objectification layer comprises an accident abnormal data objectification module and a maintenance work order data objectification module, and the accident abnormal data objectification module adopts uniform plant station equipment information to objectify, standardize and structure the original accident abnormity description in the power grid accident abnormity unit; and the overhaul work sheet data objectification module obtains the outage time sequence data of each type of equipment by analyzing the overhaul work sheet.
2. The production early-warning application framework based on the regulation cloud as claimed in claim 1, wherein the accident scheduling module and the abnormal scheduling module store the scheduling accidents and the scheduling abnormalities of the equipment with the voltage class of 220kV and above in a one-to-one correspondence manner, and the field of the original accident abnormality source data extracted from the accident scheduling module includes a station name, an equipment name, a protection action, a fault phase, a mode adjustment, a line patrol condition, a partition and a ring release, fault location information/a unit emergency start-stop condition, a unit processing regulation condition, a load transfer condition, a loss load, a lower level auto-switch-off action condition, a unit 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 abnormity module stores direct current accidents and abnormity of equipment with the voltage grade of 220kV or above, and the field of original accident abnormity source data extracted from the direct current accident and abnormity module comprises the emergency start-stop condition of a unit, mode adjustment, line patrol condition, unit processing and adjusting condition, load adjustment, network regulation and reduction power receiving condition, power transfer condition, frequency change condition, countermeasure, accident influence, power transmission size before locking and direct current accident abnormity condition;
the operation abnormity module stores the operation abnormity of the equipment with the voltage grade of 220kV or above, and the field of the original accident abnormity source data extracted from the operation abnormity module comprises the equipment name, the station name, the operation content and result of triggering the accident abnormity and the subsequent operation;
the accident monitoring module and the abnormity monitoring module correspondingly store monitoring accidents and monitoring abnormity of equipment with the voltage grade of 220kV or above, and the field of original accident abnormity source data extracted from the monitoring accidents comprises recovery conditions, accident handling conditions, failure/abnormity conditions, equipment names and station names;
the dispatching accident and abnormity module stores dispatching accidents and abnormity with the voltage class of equipment of 220kV or above, and the field of original accident abnormity source data extracted from the dispatching accident and abnormity module comprises equipment name, station name, lower level automatic switching action condition, single power supply risk of 35kV or above, user protection condition, important user condition, power loss user condition, reclosing condition and loss load.
3. The production early-warning application framework based on the regulation cloud as claimed in claim 1, wherein the power grid accident exception unit extracts original accident exception source data from an accident scheduling module, an exception scheduling module, a direct current accident and exception module, an operation exception module, an accident monitoring module, an exception monitoring module, a local dispatching accident and exception module; in the process of analyzing accident abnormal source data, 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, and then finds out a corresponding object from a regulation and control cloud equipment library according to the station name and the equipment name; and then according to corresponding keywords, segmenting the original description text, extracting required information section by section, forming corresponding standardized record data, and dividing the standardized and structured record data into 6 major categories, including direct current category, power plant category, abnormal operation category, local dispatching category, main network line equipment category and main network non-line equipment category.
4. The production early-report application framework based on the regulation cloud is characterized in that a maintenance work sheet data objectification module extracts unarchived and archived but unprocessed data from a maintenance work sheet every hour, and extracts equipment name, station name, equipment type, flow state, planning time, start-up time, completion time and equipment state from the unarchived and archived but unprocessed data;
matching the extracted equipment name, plant station name and equipment type with data stored in a 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 then constructing the out-of-service time of the unit equipment according to the flow state, the planning time, the start-up time and the completion time of the maintenance work order;
then dividing the equipment into power grid equipment and unit equipment according to different equipment types;
judging whether the unit equipment is a peak shaving unit or a coal-fired unit or whether the capacity is more than or equal to 300MW, if so, setting the data to be displayed in a report, and storing the processed data in a unit equipment outage schedule;
judging whether the power grid equipment is direct current, judging whether the direct current of the equipment is reed direct current, and if the direct current of the equipment is reed direct current, setting the direct current equipment not to be displayed on PPT; if the voltage level is not direct current, continuously judging whether the equipment voltage level is 1000/500/220kV, if the voltage level is 1000/500kV, the equipment type is not main transformer/bus/line, and the equipment type is not displayed in a report; if the voltage level is 220kV, the type of the power grid equipment is not a 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 and restoration schedule.
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