A method for real-time statistics of the number and capacity of a thermal power unit under multiple operating conditions

Abstract

The present invention discloses a method for real-time statistics of the number of units and capacity in multiple operating conditions of a thermal power unit, including: Step 1: Standardization of data tag names and acquisition of characteristic data of unit power and steam turbine speed; Step 2: Judgment of the operating state of the unit by using unit power data and the operating trend data of the steam turbine speed; Step 3: Judgment of the start-up and shutdown states of the unit by using unit power data and the operating trend data of the steam turbine speed; Step 4: Judgment of the non-stop and standby states of the unit by using unit power and the operating trend data of the steam turbine speed; Step 5: Judgment of the maintenance state of the unit by using on-site characteristic data; Step 6: Aggregate and statistically analyze the number of units in the operating condition state by using the classification retrieval method according to power plants, branches, and the group; Step 7: Aggregate and statistically analyze the capacity of the units in the operating condition state by using the classification retrieval method according to power plants, branches, and the group. The present invention realizes the online statistics of the number of units and capacity in multiple operating conditions of a thermal power unit.

Description

Technical Field

[0001] The present invention relates to a method for counting the number and capacity of thermal power units under multiple operating conditions, and more particularly to a method for real-time counting of the number and capacity of thermal power units under multiple operating conditions. Background Art

[0002] Currently, with the rapid development of power enterprises, the number of installed units in China has increased significantly. How to enable group management personnel to promptly understand the operation of group units and analyze the operation of the group has become an important focus of group supervision. In previous group supervision information, since it was difficult to achieve automatic logical judgment for maintenance, standby, and out-of-service states, manual reporting was mainly used. Due to the large number of group supervision units, the lag in manual reporting plans, changes, and other reasons, the efficiency of information supervision has been reduced. Therefore, an online statistical automatic statistical program for the number and capacity of multiple operating conditions has been developed in the existing production real-time supervision system. Through this program, it is possible to automatically distinguish the start-up, shutdown, operation, standby, out-of-service, and maintenance conditions of the units online, and classify and summarize the number and capacity of each unit state. This has saved a large amount of labor costs for manually reporting unit status and conducting statistical summaries, improved the efficiency and quality of the company's production management, enabling safety production management personnel to promptly understand and supervise whether the units of each power plant are operating stably and economically, and providing safety production guidance in a timely manner. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for real-time counting of the number and capacity of thermal power units under multiple operating conditions to solve the problems raised in the above-mentioned prior art.

[0004] The present invention is achieved through the following technical solutions:

[0005] A method for real-time counting of the number and capacity of thermal power units under multiple operating conditions includes the following steps:

[0006] Step 1: Standardization of data label names and collection of characteristic data of unit power and steam turbine speed;

[0007] Step 2: Determination of unit operating status using unit power data and steam turbine speed operation trend data;

[0008] Step 3: Determination of unit start-up and shutdown status using unit power data and steam turbine speed operation trend data;

[0009] Step 4: Determination of unit out-of-service and standby status using unit power and steam turbine speed operation trend data;

[0010] Step 5: Determination of unit maintenance status using on-site characteristic data;

[0011] Step 6: Use the classification retrieval method to summarize and count the number of unit operating conditions by power station, branch company, and group;

[0012] Step 7: Use the classification retrieval method to summarize and count the capacity of unit operating conditions by power station, branch company, and group.

[0013] A further improvement of the present invention is that the acquisition of sampled sample data is uniformly classified and processed, specifically including the following steps:

[0014] Step S101: Standardize the naming of the collected data tags in the order of branch company name\power station tag name\unit number\equipment tag abbreviation;

[0015] Step S102: Collect and store the unit power and steam turbine speed standardized by the data measurement point name into the real-time database of production process data.

[0016] A further improvement of the present invention is that the determination of the unit operating state by using the unit power data and the operating trend data of the steam turbine speed specifically includes the following steps:

[0017] Obtain the generator power and steam turbine speed values at every set time interval. The set scan period is 5 minutes. If the average value of the unit power sample is greater than 5% of the Se unit rated capacity and the average value of the steam turbine speed is 3000 ± 1 rpm, it indicates that the unit maintains the operating state. Judge that the unit is in the operating state this time and write the operating state data into the database; if the unit power is less than 5% of the Se, it indicates that the unit maintains the standby state and is initially judged to be in the standby state.

[0018] A further improvement of the present invention is that the determination of the unit startup and shutdown states by using the unit power data and the operating trend data of the steam turbine speed specifically includes the following steps:

[0019] When 1% of the Se < S < 5% of the average operating power of the unit during the scan period, it indicates that the unit is operating in an unstable transient process. Continue to judge the steam turbine speed value in the next scan period. If the speed is maintained at 3000 ± 1 rpm, it is judged to be in the startup state and write the startup state data into the database; if there is speed data continuously less than 2995 rpm, it is judged to be in the shutdown state and write the shutdown state data into the database; affected by the power grid frequency, before and after the generator is connected to the grid, the steam turbine speed will be controlled at 3000 ± 1 rpm, while after normal shutdown, the steam turbine speed will drop at a relatively fast speed.

[0020] A further improvement of the present invention is that the determination of the unit non-stop and standby states by using the unit power and the operating trend data of the steam turbine speed specifically includes the following steps:

[0021] When the average power of the unit S in the next scan cycle is less than 1% of Se, it indicates that the unit starts to disconnect from the grid. Immediately obtain the turbine speed data of the scan cycle at this moment. If there is data where the speed is continuously greater than 3002 revolutions per minute, it is judged as the non-stop state, and write the non-stop state data into the database; during non-stop, there is no slow load reduction process in the early stage of the unit, and a sudden generator trip will cause the turbine to speed up briefly after the trip, which is a significant feature of abnormal shutdown.

[0022] A further improvement of the present invention lies in that the determination of the maintenance state of the unit by using on-site characteristic data specifically includes the following steps:

[0023] After the non-stop state discrimination, when the operating state of the generator is still in the standby state, continue to judge the generator maintenance signal state. When the generator maintenance state is true, it is determined that the unit is in the maintenance state, and write the maintenance state data into the database; otherwise, the unit state is still standby, and write the standby state data into the database. Thus, the data scan judgment of one cycle ends, and wait for the next cycle to loop and judge.

[0024] A further improvement of the present invention lies in that the number of units in various operating conditions is classified and counted by using the unit classification state data and the scan cycle, specifically including the following steps:

[0025] Step S601, read the latest state value of the unit operating condition written into the database within one statistical cycle and write it into the data set D n [s1, s2, s3, s4], where s1 represents the current operating state value of the nth unit, s2 represents the current non-stop state value of the nth unit, s3 represents the maintenance state value, and s4 represents the standby state value. The state value takes the latest state data written into the database as the logical comparison condition. If it is true, it is a value of 1; if it is false, it is a value of 0;

[0026] Step S602, continue to sum the D n [s1, s2, s3, s4] data sets with the same plant name to obtain the summary statistical data set F n [s1, s2, s3, s4]; continue to sum the data sets F n with the same branch company name to obtain the summary statistical times M n of each unit in the branch company, and finally sum the data set M n to obtain the set N[s1, s2, s3, s4] of the total number of units in all states within the group.

[0027] A further improvement of the present invention lies in that the capacity of each unit operating condition is summarized and counted by using the classification retrieval method according to the plant, branch company and group, specifically including the following steps:

[0028] Classify the latest number of operating units, number of out-of-service units, number of maintenance units, and number of standby units of the unit into the data set D according to the scan cycle n Classify the latest power and capacity of the unit [s1, s2, s3, s4] into the data set C n [c1, c2, c3, c4], where c1 is the latest power of the unit and c2 - c4 are the capacity values of the unit, and combine D n .C n Obtain O n =[s1×c1, s2×c2, s3×c3, s4×c4]; continue to sum the data sets of O with the same power plant name n to obtain the aggregated statistical data set P of the capacity of each power plant n ; continue to sum the data sets P with the same branch company name n to obtain the aggregated statistical capacity Q of each unit of the branch company n , and finally sum the data set Q n to obtain the aggregated capacity set R[r1, r2, r3, r4] of all units within the group, that is, the aggregated operating load, out-of-service capacity, maintenance capacity, and standby capacity

[0029] The present invention has at least the following beneficial technical effects:

[0030] 1. Before logical processing, the present invention standardizes the field measurement point data labels and classifies them in the way of branch company name + power plant name + unit name + equipment name, which provides a prerequisite for the automatic loop determination of the program and makes it possible to automatically determine a large number of units within the group;

[0031] 2. The present invention refers to the start-up and shutdown curves of multiple types of thermal power units. In addition to making a reference comparison and judgment on the load according to the shutdown curve, it also introduces the important parameter of the steam turbine speed and combines the normal shutdown procedures of each power plant to make an auxiliary judgment on the shutdown category of the unit;

[0032] 3. The present invention uses the real-time database to not only analyze and compare a large amount of load data when the unit changes its working conditions, but also refers to the characteristics of the change in the steam turbine speed after the unit's working conditions change to analyze and compare the change in the unit's working conditions, providing a reliable basis for the correct judgment of the unit's out-of-service;

[0033] 4. The original unit status such as standby, out-of-service, and maintenance has been mainly filled in manually due to the relatively complex logical judgment. This method automatically determines the multi-condition status of thermal power units in real time based on the operating characteristic data of thermal power units, reducing a large amount of manual filling time, reducing the false alarm rate caused by the lag and omission of manual filling, and improving the safety supervision efficiency.

[0034] 5. Finally, based on the automatically judged multiple operating states of the unit and combined with the standardized measuring point naming method, the number and capacity of the unit under various working conditions are statistically summarized in this invention.

[0035] 6. Data anomaly handling is added in this invention, and data anomaly alarms are given to improve the reliability of the system.

[0036] In summary, this method has been successfully applied at present, realizing the online statistics of the number and capacity of the unit under various working conditions of thermal power units. The production duty personnel of the group can understand the summary of the number and capacity of the units under various working conditions of the whole group in the first time, and have an effective method for safety production management. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is the shutdown curve of a certain type of unit.

[0038] Figure 2 It is the flowchart of this program.

[0039] Figure 3 It is the display effect of a certain branch company.

[0040] Figure 4 It is the network configuration diagram of a typical SIS system.

[0041] Figure 5 It is the system data flow. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. Hereinafter, the present invention will be described in detail with reference to the drawings and in combination with the embodiments.

[0043] 1 Network Foundation

[0044] See Figure 4, for most power plants currently deploying the Supervisory Information System in Plant Level (SIS system), we will use the existing SIS system to upload real-time data from each plant station side to the group. The SIS network is located in the middle layer of the power plant network and plays a connecting role. Currently, we will use the interface machine to connect to the DCS system in the secure zone I and send the primary data of the unit-related operating status information of this system to the mirror database server in zone III of the SIS network in real time.

[0045] 2 System Composition

[0046] See Figure 5 , the unit status data is sent to the group's supervision system through the remote dedicated line to the group developed by our company's data transmission software; several virtual machines are divided from the hyper-converged all-in-one system in this supervision system, and a data receiving server, a database server, a website publishing server, and an alarm logic calculation server are deployed; among them, the data receiving server is responsible for receiving the unit status information sent from each plant station, and writing the data into the real-time database server after the measurement point standardization corresponding processing in the way of branch company name + power plant name + unit number + equipment name, and the alarm logic processing program is responsible for logically processing the latest unit operation information in real time, judging the latest operation status of the unit and writing it into the database, and the publishing server finally reads and summarizes the unit operation status data of each plant station from the database and realizes benchmark management in the form of graphical display on the intranet website. In order to more effectively improve the office efficiency, after passing through the network isolation gateway protection on the intranet, we deploy a WeChat publishing server, so that a set of group application management software can be deployed on the user's mobile phone, providing a decision-making basis for the realization of the group's mobile office and production management.

[0047] 3. Embodiment

[0048] 3.1 Calculation Process, See Figure 2

[0049] Step 1: Standardization of data label names and classification processing of collected data

[0050] In step S101, the data labels are named standardly, and the order is such as branch company + plant station label name + unit number + equipment label abbreviation. For example, the tripping state of the generator circuit breaker of unit 1 in QB power plant of HN branch company is named: HN.QB.N1.GCB_State

[0051] Step S102 encodes the on-site data and standardizes the corresponding measuring point names. Using the plant name QB and the equipment name GCB_State as the same classification conditions, and the unit number N*. as the loop writing condition, the sample data within each interval scan period is classified and defined into the vector set of A1…A N such as the set A1[s1……s n represents the sample set of the generator circuit breaker trip state of Unit 1 in Plant QB, and [s1……s n are the n state values obtained within the scan period;

[0052] Step S103 performs validity processing on the n samples in the data set A N If n = 1, the last sample in the A N-1 data set is supplemented to the set A N in chronological order. If n = 0, it means that no sample data is obtained, and this loop is exited. After such loop analysis is performed 3 times, if sample data cannot be obtained each time, it is determined that the system has a fault;

[0053] Step 2: Judgment of operating and standby states

[0054] Step S201 obtains the generator power and turbine speed values at every set time interval (the scan period set in this program is 5 minutes). If the average value of the unit power sample is greater than 5% of the Se unit rated capacity and the average turbine speed is 3000 ± 1 rpm, it indicates that the unit is in the operating state. It is determined that the current unit is in the operating state and the operating state data is written into the database; if the unit power is less than 5% of Se, it indicates that the unit is in the standby state and is initially judged to be in the standby state.

[0055] Step 3: Judgment of unit startup and shutdown states

[0056] According to Right S201, when the average value of the operating power of the unit during the scan period is 1% of Se < S < 5% of Se, it indicates that the unit is operating in an unstable transient process. Continue to judge the turbine speed value in the next scan period. If the speed remains at 3000 ± 1 rpm, it is judged to be in the startup state and the startup state data is written into the database; if there is speed data continuously less than 2995 rpm, it is judged to be in the shutdown state and the shutdown state data is written into the database. Because of the influence of the grid frequency, before and after the generator is connected to the grid, the turbine speed will be strictly controlled at 3000 ± 1 rpm, while after normal shutdown, the turbine speed will drop at a relatively fast speed.

[0057] Step 4: Judgment of unit forced outage and standby states

[0058] In step S401, when the average power of the unit in the next scan cycle is S < 1% of Se, it indicates that the unit starts to disconnect from the grid. Immediately obtain the turbine speed data of the scan cycle data at this moment. If there is data with the speed continuously greater than 3002 revolutions per minute, it is judged as the non-stop state, and the non-stop state data is written into the database. Because when the unit is in the non-stop state, there is no slow load reduction process, and suddenly tripping the generator will cause the turbine to speed up briefly after the trip, which is a significant feature of abnormal shutdown.

[0059] Step 5: Determination of the unit maintenance status

[0060] In step S501, after the non-stop state discrimination, when the operating state of the generator is still in the standby state, continue to judge the generator maintenance signal state. When the generator maintenance state is true, it is determined that the unit is in the maintenance state, and the maintenance state data is written into the database. Otherwise, the unit state is still standby, and the standby state data is written into the database. Currently, for the unit maintenance signal, there is generally no automatic signal measuring point on the substation side. At present, we can obtain the maintenance signal value based on the grounding closing signal of the generator circuit breaker grounding knife switch and the opening signal of the disconnector of the grid-connected circuit breaker or the on-site manual filling plan, and assign this value to the standardized maintenance state measuring point when the measuring point label corresponds. Thus, the data scanning judgment of one cycle is received, and it waits for the next cycle to loop and judge.

[0061] Step 6: Use the classification retrieval method to summarize and count the number of unit operating conditions by substation, branch company, and group

[0062] In step S601, read the latest state value of the unit operating condition written into the database within one statistical cycle and write it into the data set D n [s1, s2, s3, s4], where s1 represents the current operating state value of the nth unit, s2 represents the current non-stop state value of the nth unit, s3 represents the maintenance state value, and s4 represents the standby state value. The state value takes the latest state data written into the database as the logical comparison condition. If it is true, it is a value of 1; if it is false, it is a value of 0.

[0063] In step S602, continue to sum the D n [s1, s2, s3, s4] data sets with the same substation name to obtain the summary statistical data set F of each substation n [s1, s2, s3, s4]; continue to sum the data sets F with the same branch company name n to obtain the summary statistical times M of each unit in the branch company n , and finally sum the data set M n to obtain the summary state number set N[s1, s2, s3, s4] of all units within the group.

[0064] Step 7: Use the classification retrieval method to summarize and count the capacity of unit operating conditions by substation, branch company, and group

[0065] Step S701 classifies the latest operating unit number, outage unit number, maintenance unit number, and standby unit number of the unit into the data set D according to the scan cycle n [s1, s2, s3, s4], and classifies the latest power and capacity of the unit into the data set C n [c1, c2, c3, c4], where c1 is the latest power of the unit, and c2 - c4 are the capacity values of the unit, and combines D n .C n to obtain O n = [s1 × c1, s2 × c2, s3 × c3, s4 × c4]; continue to sum the O data sets with the same substation name to obtain the summary statistical data set P of the capacities of each substation n ; continue to sum the data sets P with the same branch company name n to obtain the summary capacity Q of each unit of the branch company n ; finally, sum the data set Q n to obtain the summary capacity set R[r1, r2, r3, r4] of all units within the group, that is, the summary operating load, outage capacity, maintenance capacity, and standby capacity n

[0066] Although the present invention has been described in detail above with general descriptions and specific embodiments, based on the present invention, some modifications or improvements can be made, which are obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection required by the present invention​

Claims

1. A method for real-time statistics of the number and capacity of a thermal power unit under multiple operating conditions, characterized in that, The following steps are involved: Step 1: Standardization of data label names and collection of unit power and turbine speed characteristic data; Step 2: Use the unit power data and turbine speed operation trend data to determine the unit operation status, including: obtaining the generator power and turbine speed values at each set time interval, where the scanning cycle is set to 5 minutes. If the unit power sample average value is greater than 5%Se of the unit rated capacity and the turbine speed average value is 3000±1 rpm, it means that the unit remains in operation, and the unit is judged to be in operation, and the operation status data is written to the database; if the unit power is less than 5%Se, it means that the unit remains in standby state, and it is preliminarily judged to be in standby state; Step 3: Use the unit power data and turbine speed operation trend data to determine the unit startup and shutdown status, including: when the unit scan cycle operating power average value 1% Se <S<5%Se时,说明机组运行在不稳定暂态过程,继续判断下一扫描周期的汽机转速值如转速维持在3000±1转 / 分,在判断为启动状态,写入启动状态数据到数据库;如果有转速数据连续小于2995转 / 分,则判断为停机状态,写入停机状态数据到数据库;受电网频率影响,发电机并网前后,汽轮机转速将控制在3000±1转 / 分而对与正常停机后,汽轮机转速将较快速度下降; Step 4: Use the unit power and turbine speed operation trend data to determine the non-stop and standby states of the unit, including: when the unit average power S<1%Se in the next scanning cycle, it means that the unit starts to run off-grid, and immediately obtain the turbine speed data of the scanning cycle data at that moment. If there is data with a speed continuously greater than 3002 rpm, it is judged as a non-stop state, and the non-stop state data is written to the database; when the unit is not shut down, there is no slow load reduction process in the early stage, and the generator trips suddenly, which will cause the turbine to speed up in a short period after the trip, which is a significant feature of abnormal shutdown; Step 5: Using the on-site characteristic data to determine the maintenance status of the unit, including: after the non-stop state is determined, when the generator operation state is still in the standby state, continue to determine the generator maintenance signal state, when the generator maintenance state is true, determine that the unit is in the maintenance state, and write the maintenance state data to the database; otherwise, the unit state is still in the standby state, write the standby state data to the database, so far, a cycle of data scanning and judgment is completed, waiting for the next cycle of judgment; Step 6: Use the classification search method to summarize and count the number of units in operating status by plant, branch and group; Step 7: Use the classification search method to summarize the unit operating status and capacity by plant, branch and group.

2. The method for real-time statistics of the number of units and capacity under multiple operating conditions of a thermal power unit according to claim 1, wherein The acquisition of sample data for unified classification processing specifically includes the following steps: Step S101, standardize the naming of the collected data labels in the order of branch company name\plant label name\unit number\equipment label abbreviation; Step S102, the unit power and turbine speed are collected and stored in the real-time database of production process data after the data measuring point names are standardized.

3. The method for real-time statistics of the number and capacity of a thermal power unit under multiple operating conditions according to claim 1, characterized in that, Classify and count the number of units in various operating conditions by using the unit classification status data and the scanning cycle. The specific steps are as follows: Step S601: Read the latest status value of the unit operating conditions written to the database within one statistical period and write it into the data set D n [s1, s2, s3, s4], where s1 represents the current operating status value of unit n, s2 represents the current non-stop status value of unit n, s3 represents the maintenance status value, and s4 represents the standby status value. The status values use the latest status data written to the database as the logical comparison condition. If it is true, it is a value of 1; if it is false, it is a value of 0; Step S602, continue to process D with the same substation name n Sum the data sets [s1, s2, s3, s4] to obtain the summary statistical data set F of each substation n [s1, s2, s3, s4]; continue to sum the data sets F with the same branch name n to obtain the summary count M of each unit in the branch n , and finally sum the data set M n to obtain the set N[s1, s2, s3, s4] of the total number of units in the group 4. A method for real-time statistics of the number of units and capacity in multiple operating conditions of a thermal power unit, characterized in that, Use the classification retrieval method to summarize and count the capacity of each unit operating condition by power station, branch company, and group. The specific steps are as follows: Classify the latest operating units, non - operating units, overhaul units, and standby units of the unit into dataset D according to the scan cycle n [s1, s2, s3, s4], classify the latest power and capacity of the unit into dataset C n [c1, c2, c3, c4], where c1 is the latest power of the unit, and c2 - c4 are the capacity values of the unit, combine D n .C n Get O n =[s1×c1, s2×c2, s3×c3, s4×c4]; continue to sum the data sets of O with the same substation name n to obtain the summary statistical data set P of the capacity of each substation n ; continue to sum the data sets P with the same branch name n to obtain the summary capacity Q of each unit of the branch n , and finally sum the data set Q n to obtain the summary capacity set R[r1, r2, r3, r4] of all units within the group, that is, the summary operating load, non - operating capacity, overhaul capacity, and standby capacity

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