CN111426892B - Unit online state statistical method under complex condition - Google Patents
Unit online state statistical method under complex condition Download PDFInfo
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- CN111426892B CN111426892B CN202010161566.8A CN202010161566A CN111426892B CN 111426892 B CN111426892 B CN 111426892B CN 202010161566 A CN202010161566 A CN 202010161566A CN 111426892 B CN111426892 B CN 111426892B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R21/00—Arrangements for measuring electric power or power factor
- G01R21/001—Measuring real or reactive component; Measuring apparent energy
- G01R21/002—Measuring real component
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C3/00—Registering or indicating the condition or the working of machines or other apparatus, other than vehicles
- G07C3/08—Registering or indicating the production of the machine either with or without registering working or idle time
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- Supply And Distribution Of Alternating Current (AREA)
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Abstract
The invention discloses a unit online state statistical method under a complex condition, which comprises the following steps: step S1: starting a program; step S2: calling a generator set initial online state statistical process to carry out initial online state statistics; step S3: after the initial online state counting process is completed, after the preset waiting time is over, the real-time online state counting process of the generator set is called, real-time online counting is carried out, and after one round of real-time online counting is over, the next round of real-time online counting is carried out in a circulating mode. The method can meet the requirement of a user on real-time control of the state of the generator set, and reduces the loss of historical statistical information.
Description
Technical Field
The invention relates to the technical field of information statistics of power system automation, in particular to a unit online state statistical method under a complex condition.
Background
The conventional online state statistical method of the unit only simply performs statistics from the start time of function operation, and does not consider the historical online operation condition of the unit. If the model changes in the function operation process, the changed part cannot be counted in real time, and the function needs to be restarted. If the interruption time of the function operation is too long and the calculation is not restarted immediately, the statistics must be restarted and the historical statistical information is discarded. Such an approach results in inefficiency and is prone to statistical errors.
Disclosure of Invention
In view of the above, the present invention provides a method for counting an online state of a unit under a complex condition, which can adjust a counting range in real time when a generator model of a system changes, and a function is abnormal or exits, fully utilize historical statistical information and sampling information, complement the missing statistical information as much as possible, ensure the completeness and comprehensiveness of a counting result, and improve the fault tolerance of the function.
The purpose of the invention is realized by the following technical scheme:
the method comprises the following steps:
step S1: starting a statistical process;
step S2: calling a generator set initial online state statistical process to carry out initial online state statistics;
step S3: and after the initial online state counting process is completed and the preset waiting time is over, calling the real-time online state counting process of the generator set to complete the real-time online state counting of the generator set.
Further, the statistical process of the initial online state of the generator set comprises the following substeps:
step S21, an empty unit online information list is adjusted;
step S22, reading parameter information of all economic units, reading data of a real-time generator set table and all existing prior state information in a unit online state statistical table, wherein the data of the real-time generator set table is used for acquiring the online output condition of the current generator set and matching the corresponding economic units, if the data can be matched with the prior output condition of the current generator set, the current actual state of the unit is obtained through analysis according to the current P value, and if the data can not be matched with the prior output condition of the current generator set, the current actual state of the unit cannot be processed in the later period; reading all the prior state information in the unit online state statistical table, wherein if the historical statistical information exists in the unit online state statistical table, the initial value of all the economic units adopts the historical online statistical information, and if the historical information does not exist, the economic units do not process the historical information;
step S23, generating an initial value of the online state of the current generator set;
step S24: determining the time range of the sampling values, and acquiring active sampling values of all the generator sets within the specified time range;
step S25: updating relevant parameter information of the unit according to the sampling data;
step S26: updating the analyzed current online state of the generator set into a database;
step S27: completing the initial online state statistics.
Further, in step S24, if there is historical statistical information, the update time with the range of history to the current time is adopted; if no historical statistical information exists, the sampling range is from 7 days ago to the current time.
Further, in step S25, the relevant parameter information includes: start-stop time, start-stop state, continuous start-stop minutes, deep peak regulation minutes, and continuous rise and fall minutes.
Further, the real-time online status statistic process of the generator set in step S3 includes the following steps:
step S31: reading the online state statistical information of the unit before the waiting time;
step S32, reading the parameter information of the real-time generator set table, obtaining the current online output condition of the generator set,
step S33: judging whether the number of the generator sets is changed, if so, finding out the id of a newly added generator, finding out the information of a corresponding economic set, calculating the related parameter information of the newly added set, and entering the next step; if not, directly entering the next step;
step S34: judging the current state, and updating relevant online statistical information of the corresponding unit;
step S35: updating the analyzed current online state of the generator set into a database;
step S36: and entering waiting time, and circulating the steps after the waiting time is finished.
Further, the set waiting time is 5 min.
Further, in step S33, the information of the relevant parameters of the newly added unit includes startup and shutdown time, startup and shutdown state, number of minutes of continuous startup and shutdown, number of minutes of deep peak shaving, and number of minutes of continuous rising and falling.
Further, in step S3, real-time online statistics is performed, and after one round of real-time online statistics is finished, the next round of real-time online statistics is performed in a loop.
The invention has the beneficial effects that:
the invention provides a method for counting the online state of a generator set under a complex condition, which can meet the requirement of a user on real-time control on the state of the generator set and reduce the loss of historical statistical information.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof.
Drawings
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a main flow chart of generator set online state statistics;
FIG. 2 is a flow chart of a generator set initial online status statistic;
fig. 3 is a flow chart of real-time online statistics of the generator set.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
The present invention provides a statistical method of unit online status under complex conditions, it is noted that, as used in this disclosure, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. It should be understood that the use of any and all examples, or exemplary language ("e.g.," such as, "etc.), provided herein is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.
As shown in fig. 1, the method of the present invention comprises the steps of:
step S1: starting a statistical process; as one step in the actual operation process, in this embodiment, as a selectable operation qualification determination mode, a step of determining whether the input parameter is qualified is further included, if not, a correct input parameter format needs to be input, otherwise, the process is ended, after the input parameter is determined to be qualified, program registration is performed, and then the next step is performed;
step S2: calling a generator set initial online state statistical process to carry out initial online state statistics; the statistical information of the invention is mainly stored in a unit online state statistical table, and the unit online state statistical table comprises a unit unique code (phyunit _ id), the latest startup time (last _ on), the latest shutdown time (last _ off), the current actual state of the unit (cur _ commit, 0: offline, 1: online), the current output of the unit (cur _ mw), the number of minutes of continuous startup and shutdown (on _ off _ length), the number of minutes of deep peak shaving (deep _ length), the number of minutes of continuous rising and falling (up _ down _ length), and the update time (update _ time). The unique code (phyunit _ id) of the unit is a table main key, and the deep peak regulation minutes is duration minutes of which the active value of the unit is smaller than the minimum technical output and larger than the lower limit of the deep peak regulation. The number of minutes of continuous lifting is: compared with the active value at the previous moment, the active value at the previous moment is larger than or equal to the active value at the previous moment and continuously rises, and the active value at the previous moment is smaller than or equal to the active value at the previous moment and continuously falls. The number of minutes of continuous ascending and descending is greater than 0, the number of minutes of continuous ascending and less than 0, the number of minutes of continuous descending.
As shown in fig. 2, in this embodiment, the generator set initial online status statistics process includes the following sub-steps:
step S21, an empty unit online information list is adjusted to be used for the next real-time data filling;
step S22, reading parameter information of all economic units in an economic unit table, reading data (including id and power P values) of a real-time generator unit table and all existing prior state information in a unit online state statistical table, wherein the data of the real-time generator unit table is used for the online output condition of the current generator unit and is matched with the corresponding economic unit, if the data can be matched with the prior state information, the current actual state of the unit is obtained through analysis according to the current P value, and if the data can not be matched, the current actual state of the unit cannot be processed in the later period; reading all the prior state information in the unit online state statistical table, wherein if the historical statistical information exists in the unit online state statistical table, the initial value of all the economic units adopts the historical online statistical information, and if the historical information does not exist, the economic units do not process the historical information; it should be noted that the economic unit table, the real-time power generation unit table, and the unit online state statistical table mentioned in this step are existing and available pre-existing unit tables, and the data of the unit tables can be obtained by an existing conventional unit data statistical system.
Step S23, generating an initial value of the online state of the current generator set;
step S24: determining the time range of the sampling values, and acquiring active sampling values of all the generator sets within the specified time range; in this embodiment, if there is historical statistical information, the update time in the range of history to the current time is adopted; if no historical statistical information exists, the sampling range is from 7 days ago to the current time;
step S25: updating relevant parameter information of the unit according to the sampling data; in this embodiment, the related parameter information includes: start-stop time, start-stop state, continuous start-stop minutes, deep peak regulation minutes and continuous lifting minutes;
step S26: updating the analyzed current online state of the generator set into a database; the database architecture can adopt the existing mature commercial database architecture;
step S27: completing the initial online state statistics.
Step S3: after the initial online state counting process is completed, after the preset waiting time is over, the real-time online state counting process of the generator set is called, real-time online counting is carried out, and after one round of real-time online counting is over, the next round of real-time online counting is carried out in a circulating mode, so that uninterrupted real-time online data counting work is realized. In this embodiment, the set waiting time is 5 min. Of course, the setting may be made according to actual conditions.
As shown in fig. 3, the real-time online status statistical process of the generator set includes the following steps:
step S31: reading the online state statistical information of the unit before the waiting time;
step S32, reading parameter information (including id and power P value) of the real-time generator set table, obtaining the current online output condition of the generator set,
step S33: judging whether the number of the generator sets is changed, if so, finding out the id of a newly added generator, finding out the information of a corresponding economic set, calculating the related parameter information of the newly added set, and entering the next step; if not, directly entering the next step; the related parameter information of the newly added unit comprises startup and shutdown time, startup and shutdown states, continuous startup and shutdown minutes, deep peak regulation minutes and continuous ascending and descending minutes.
Step S34: judging the current state, and updating relevant online statistical information of the corresponding unit;
step S35: updating the analyzed current online state of the generator set into a database;
step S36: entering a waiting time, and after the waiting time is over, circulating the steps
The specific implementation mode is as follows: the method is characterized in that the on-line resident real-time statistics is carried out during the statistics, after the functions are operated, the initial on-line state statistics is carried out firstly, and then the real-time on-line statistics is carried out every 5 minutes.
When the initial online state is counted, firstly, the initial state of the generator set is confirmed, the statistical information in the generator set online state statistical table is preferentially selected, the real-time generator set online state is obtained from the real-time generator set table if the statistical information is not available, the method for judging the real-time generator set online state is to judge the active value of the generator set in the current period, when the absolute value of the active value of the generator set is larger than the limit value, the generator set is judged to be started, otherwise, the generator set is judged to be stopped. Through the two paths, the initial value of the online state of the current generator set can be generated. And then the indexes of the unit, such as the starting and stopping time, the starting and stopping state, the continuous starting and stopping minutes, the deep peak regulation minutes, the continuous lifting minutes and the like, are calculated through sampling information. When the sampling time range is determined, the unit has historical statistical time, and the 'update time' of the historical record is taken as the sampling start time. The time before 7 days without history is taken as the sampling start time, and the sampling end time is taken as the current time. After the active sampling value is obtained, each index value can be calculated and written into the online state statistical table of the unit.
Real-time online statistics is counted as a timed statistical function every 5 minutes. And acquiring the output of the current generator set in real time, judging whether the number of the generator sets changes or not by taking the number of the real-time generator sets acquired last time as a standard, if so, comparing and finding out the information of the newly added generator set, correspondingly finding out the information of the newly added economic generator set, and calculating the related index value of the currently added generator set. If no change exists, the corresponding index statistical value is directly updated, and then all updated information is written into the unit online state statistical table.
It should be noted that any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and that the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, any one or a combination of the following techniques, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, and the program may be stored in a computer readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (7)
1. A method for counting the online state of a unit under a complex condition is characterized by comprising the following steps: the method comprises the following steps:
step S1: starting a statistical process;
step S2: calling a generator set initial online state statistical process to carry out initial online state statistics;
the statistical process of the initial online state of the generator set comprises the following substeps:
step S21, an empty unit online information list is adjusted;
step S22, reading parameter information of all economic units, reading data of a real-time generator set table and all existing prior state information in a unit online state statistical table, wherein the data of the real-time generator set table is used for acquiring the online output condition of the current generator set and matching the corresponding economic units, if the data can be matched with the prior output condition of the current generator set, the current actual state of the unit is obtained through analysis according to the current P value, and if the data can not be matched with the prior output condition of the current generator set, the current actual state of the unit cannot be processed in the later period; reading all the prior state information in the unit online state statistical table, wherein if the historical statistical information exists in the unit online state statistical table, the initial value of all the economic units adopts the historical online statistical information, and if the historical information does not exist, the economic units do not process the historical information;
step S23, generating an initial value of the online state of the current generator set;
step S24: determining the time range of the sampling values, and acquiring active sampling values of all the generator sets within the specified time range;
step S25: updating relevant parameter information of the unit according to the sampling data;
step S26: updating the analyzed current online state of the generator set into a database;
step S27: completing initial online state statistics;
step S3: and after the initial online state counting process is completed and the preset waiting time is over, calling the real-time online state counting process of the generator set to complete the real-time online state counting of the generator set.
2. The online state statistical method for the unit under the complex condition as claimed in claim 1, characterized in that: in step S24, if there is historical statistical information, the update time with the range of history to the current time is adopted; if no historical statistical information exists, the sampling range is from 7 days ago to the current time.
3. The online state statistical method for the unit under the complex condition as claimed in claim 1, characterized in that: in step S25, the relevant parameter information includes: start-stop time, start-stop state, continuous start-stop minutes, deep peak regulation minutes, and continuous rise and fall minutes.
4. The online state statistical method for the unit under the complex condition as claimed in claim 1, characterized in that: the real-time online state statistical process of the generator set in the step S3 comprises the following steps:
step S31: reading the online state statistical information of the unit before the waiting time;
step S32, reading the parameter information of the real-time generator set table, obtaining the current online output condition of the generator set,
step S33: judging whether the number of the generator sets is changed, if so, finding out the id of a newly added generator, finding out the information of a corresponding economic set, calculating the related parameter information of the newly added set, and entering the next step; if not, directly entering the next step;
step S34: judging the current state, and updating relevant online statistical information of the corresponding unit;
step S35: updating the analyzed current online state of the generator set into a database;
step S36: and entering waiting time, and circulating the steps after the waiting time is finished.
5. The online state statistical method for the unit under the complex condition as claimed in claim 1 or claim 4, wherein: the set waiting time is 5 min.
6. The online state statistical method for the unit under the complex condition as claimed in claim 4, characterized in that: in step S33, the information of the parameters related to the newly added unit includes startup and shutdown time, startup and shutdown state, number of minutes of continuous startup and shutdown, number of minutes of deep peak shaving, and number of minutes of continuous rising and falling.
7. The online state statistical method for the unit under the complex condition as claimed in claim 1, characterized in that: in step S3, real-time online statistics is performed, and after one round of real-time online statistics is finished, the next round of real-time online statistics is performed in a loop.
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