Load balance optimization method for peak regulation of thermal power generating unit

Abstract

The invention relates to the technical field of electric power systems, and discloses a load balance optimization method for peak regulation of a thermal power generating unit, which comprises the following steps of: 1, monitoring the operation state of a coal mill in the thermal power generating unit, acquiring data and forming an operation data set of the coal mill, and 2, monitoring the operation state of a water feeding pump in the thermal power generating unit, and acquiring the data and forming an operation data set of the coal mill. The method comprises the following steps of 1, monitoring the operation state of a steam turbine in a thermal power generating unit, collecting data and forming a water feeding pump operation data set, 2, monitoring the operation state of a boiler in the thermal power generating unit, collecting data and forming a steam turbine operation data set, and 3, monitoring the operation state of the boiler in the thermal power generating unit, collecting data and forming a boiler operation data set. 5, calculating a coal mill operation data set, a water feeding pump operation data set, a steam turbine operation data set and a boiler operation data set, and 6, analyzing the operation states of the coal mill, the water feeding pump, the steam turbine and the boiler, and judging whether corresponding adjustment is needed or not according to the states to comprehensively monitor the abnormal condition of the equipment.

Description

Technical Field

[0001] The invention relates to the technical field of power systems, and in particular to a load balancing optimization method for peak load regulation of thermal power generation units. Background Art

[0002] Peak shaving of thermal power units is an important technology in the power system. It aims to cope with fluctuations in grid load and ensure the stability and reliability of power supply. It is a process of adjusting the power generation capacity to meet demand according to changes in grid load. Peak shaving of thermal power units is crucial to ensuring the safe and stable operation of the power system. Especially in the context of the increasing proportion of new energy, the peak shaving capacity of thermal power units has become a key factor in absorbing new energy and improving the regulation capacity of the power system. It is an indispensable link in the power system. By adopting advanced control technology and optimized scheduling strategies, we can fully tap the peak shaving potential of thermal power units, improve the utilization and stability of the power grid, reduce energy consumption and pollution, and contribute to the realization of the "dual carbon" goals.

[0003] The coal mill in the thermal power unit is a key equipment for grinding coal into fine powder, the water feed pump is an important equipment for sending water to the boiler, the steam turbine is the prime mover, and the boiler is a device for generating steam. The performance degradation and failure of the equipment will directly affect the power output and peak-shaving capacity of the unit, making the thermal power unit unresponsive during peak-shaving and unable to meet load demands, or even causing power shortages, leading to load imbalance during peak-shaving of the thermal power unit. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the deficiencies in the prior art, the present invention provides a load balancing optimization method for peak load regulation of thermal power units. The method has the advantages of comprehensively analyzing the operating status of the equipment by combining various data of the coal mill, feed water pump, steam turbine, and boiler, accurately and comprehensively monitoring abnormal conditions of the equipment, timely discovering abnormal conditions of the equipment, improving the reliability and operating efficiency of the equipment, extending the life of the equipment, and realizing peak load balancing optimization of the thermal power units.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a load balancing optimization method for peak load regulation of thermal power units, comprising the following steps:

[0008] Step 1: monitor the operating status of the coal mill in the thermal power unit, and collect coal mill operating data to form a coal mill operating data set;

[0009] Step 2: monitor the operating status of the feedwater pump in the thermal power unit, and collect the feedwater pump operating data to form a feedwater pump operating data set;

[0010] Step 3: monitor the operating status of the steam turbine in the thermal power unit, and collect the steam turbine operating data to form a steam turbine operating data set;

[0011] Step 4: monitor the operating status of the boiler in the thermal power unit and collect boiler operating data to form a boiler operating data set;

[0012] Step 5, respectively analyzing the coal mill operation data set MMyx, the feed water pump operation data set GSyx, the steam turbine operation data set QLyx, and the boiler operation data set GLyx according to the coal mill operation data set, the feed water pump operation data set, the steam turbine operation data set, and the boiler operation data set;

[0013] Step six, analyze the operating status of the coal mill, feed water pump, steam turbine and boiler according to the coal mill operating data group MMyx, feed water pump operating data group GSyx, steam turbine operating data group QLyx and boiler operating data group GLyx, and determine whether corresponding adjustments are needed according to the corresponding operating status. If adjustments are needed, send abnormal signals for maintenance.

[0014] Preferably, in step 1, the coal mill operation data set includes coal mill vibration data, coal mill temperature data, and coal mill flow data, and the coal mill operation data set is expressed as: {MM zd ,MM wd ,MM ll}, where MM zd Represents the vibration data of coal mill, MM wd Represents the coal mill temperature data, MM ll Represents coal mill flow data.

[0015] Preferably, in step 2, the water supply pump operation data set includes water supply pump outlet pressure data, water supply pump flow data, and water supply pump vibration data. The expression of the water supply pump operation data set is: {GS yl GS ll GS zd}, where GS yl Represents the outlet pressure data of the feedwater pump, GS ll Represents the feedwater pump flow data, GS zd Represents the vibration data of the water supply pump.

[0016] Preferably, in step 3, the turbine operation data set includes turbine vibration data, turbine pressure data, turbine flow data, and turbine temperature data, and the expression of the turbine operation data set is: {QLzd ,QL yl ,QL ll ,QL wd}, where QL zd Represents the turbine vibration data, QL yl Represents the turbine pressure data, QL ll Represents the turbine flow data, QL wd Represents turbine temperature data.

[0017] Preferably, in step 4, the boiler operation data set includes boiler water level data, boiler temperature data, and boiler pressure data, and the expression of the boiler operation data set is: {GL sw GL wd GL yl}, where GL sw Represents boiler water level data, GL wd Represents boiler temperature data, GL yl Represents boiler pressure data.

[0018] Preferably, in step 5, the calculation formula of the coal mill operation data set MMyx is as follows:

[0019]

[0020] In the above formula, Represents the highest vibration data of coal mill under standard conditions. Represents the maximum temperature data of the coal mill under standard conditions, Represents the minimum flow rate data of the coal mill under standard conditions, The difference between the vibration data of the coal mill and the maximum vibration data of the coal mill under standard conditions. The difference between the coal mill temperature data and the coal mill maximum temperature data under standard conditions. It represents the difference between the coal mill flow rate data and the minimum flow rate data of the coal mill under standard conditions.

[0021] Preferably, in step 5, the calculation formula of the water supply pump operation data group GSyx is as follows:

[0022]

[0023] In the above formula, Represents the minimum outlet pressure data of the water supply pump under standard conditions. Represents the minimum flow data of the water supply pump under standard conditions. Represents the highest vibration data of the water supply pump under standard conditions. Represents the feedwater pump pressure difference between the feedwater pump outlet pressure data and the feedwater pump minimum outlet pressure data under standard conditions. Represents the difference between the feed water pump flow rate data and the minimum feed water pump flow rate data under standard conditions. Represents the vibration difference between the water supply pump vibration data and the maximum vibration data of the water supply pump under standard conditions.

[0024] Preferably, in step 5, the calculation formula of the steam turbine operation data group QLyx is as follows:

[0025]

[0026] In the above formula, Represents the maximum vibration data of the steam turbine under standard conditions. Represents the lowest pressure data of the steam turbine under standard conditions. Represents the minimum flow data of the steam turbine under standard conditions, Represents the maximum temperature data of the steam turbine under standard conditions, Represents the difference between the turbine vibration data and the maximum turbine vibration data under standard conditions. Represents the turbine pressure difference between the turbine pressure data and the minimum turbine pressure data under standard conditions. Represents the difference between the turbine flow rate data and the minimum turbine flow rate data under standard conditions. Represents the turbine temperature difference between the turbine temperature data and the maximum turbine temperature data under standard conditions.

[0027] Preferably, in step 5, the boiler operation data group GLyx is calculated as follows:

[0028]

[0029] In the above formula, Represents the lowest water level data of the boiler under standard conditions. Represents the maximum boiler temperature data under standard conditions. Represents the lowest boiler pressure data under standard conditions. Represents the boiler water level difference between the boiler water level data and the boiler minimum water level data under standard conditions. Represents the boiler temperature difference between the boiler temperature data and the boiler maximum temperature data under standard conditions, Represents the boiler pressure difference between the boiler pressure data and the boiler minimum pressure data under standard conditions.

[0030] Preferably, in the step six, when any value in the coal mill operation data group MMy, the feed water pump operation data group GSyx, the turbine operation data group QLyx, and the boiler operation data group GLyx is less than 0, it means that the corresponding equipment has a fault or performance degradation, and the corresponding fault signal is sent to the maintenance module.

[0031] Compared with the prior art, the present invention provides a load balancing optimization method for peak load regulation of thermal power units, which has the following beneficial effects:

[0032] 1. The present invention combines various data of coal mill, feed water pump, steam turbine and boiler, and then compares them with corresponding standard values ​​to comprehensively analyze the operating status of the above equipment, so as to realize accurate and comprehensive monitoring of abnormal conditions of equipment, timely discover abnormal conditions of equipment, carry out early warning and maintenance, avoid the occurrence of equipment failure, improve the reliability and availability of equipment, and ensure the stable operation of thermal power units.

[0033] 2. The present invention analyzes the operating status of the coal mill, feed water pump, steam turbine and boiler according to the coal mill operating data group MMyx, the feed water pump operating data group GSyx, the turbine operating data group QLyx and the boiler operating data group GLyx in many aspects, and can reduce unplanned downtime by accurately predicting and preventing failures, which together contribute to the stable operation and economic benefits of the entire thermal power unit, improve the reliability and operating efficiency of the equipment, extend the life of the equipment, and achieve peak load balance optimization of the thermal power unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a step diagram of the method of the present invention; DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] See also Figure 1 A load balancing optimization method for peak load regulation of a thermal power unit comprises the following steps:

[0037] Step 1: monitor the operating status of the coal mill in the thermal power unit, and collect coal mill operating data to form a coal mill operating data set;

[0038] The coal mill operation data set includes coal mill vibration data, coal mill temperature data, and coal mill flow data. The expression of the coal mill operation data set is: {MM zd ,MMwd ,MM ll}, where MM zd Represents the vibration data of coal mill, MM wd Represents the coal mill temperature data, MM ll Represents coal mill flow data;

[0039] Step 2: monitor the operating status of the feedwater pump in the thermal power unit, and collect the feedwater pump operating data to form a feedwater pump operating data set;

[0040] The feed pump operation data set includes the feed pump outlet pressure data, feed pump flow data, and feed pump vibration data. The expression of the feed pump operation data set is: {GS yl GS ll GS zd}, where GS yl Represents the outlet pressure data of the feedwater pump, GS ll Represents the feedwater pump flow data, GS zd Represents the vibration data of the water supply pump;

[0041] Step 3: monitor the operating status of the steam turbine in the thermal power unit, and collect the steam turbine operating data to form a steam turbine operating data set;

[0042] The steam turbine operation data set includes steam turbine vibration data, steam turbine pressure data, steam turbine flow data, and steam turbine temperature data. The expression of the steam turbine operation data set is: {QL zd ,QL yl ,QL ll ,QL wd}, where QL zd Represents the turbine vibration data, QL yl Represents the turbine pressure data, QL ll Represents the turbine flow data, QL wd Represents the turbine temperature data;

[0043] Step 4: monitor the operating status of the boiler in the thermal power unit and collect boiler operating data to form a boiler operating data set;

[0044] The boiler operation data set includes boiler water level data, boiler temperature data, and boiler pressure data. The expression of the boiler operation data set is: {GL sw GL wd GL yl}, where GL sw Represents boiler water level data, GL wd Represents boiler temperature data, GL yl Represents boiler pressure data;

[0045] Step 5, respectively analyzing the coal mill operation data set MMyx, the feed water pump operation data set GSyx, the steam turbine operation data set QLyx, and the boiler operation data set GLyx according to the coal mill operation data set, the feed water pump operation data set, the steam turbine operation data set, and the boiler operation data set;

[0046] The calculation formula of coal mill operation data group MMyx is as follows:

[0047]

[0048] In the above formula, Represents the highest vibration data of coal mill under standard conditions. Represents the maximum temperature data of the coal mill under standard conditions, Represents the minimum flow rate data of the coal mill under standard conditions, The difference between the vibration data of the coal mill and the maximum vibration data of the coal mill under standard conditions. The difference between the coal mill temperature data and the coal mill maximum temperature data under standard conditions. The difference between the coal mill flow rate data and the coal mill minimum flow rate data under standard conditions;

[0049] By combining the coal mill vibration data, coal mill temperature data, and coal mill flow data and comparing them with the corresponding standard values, the operation status of the coal mill is comprehensively analyzed from the aspects of coal mill vibration, temperature, and flow, so as to realize accurate and comprehensive monitoring of the abnormal conditions of the coal mill, and timely discover the abnormal conditions of the coal mill, such as imbalance, overheating, etc., so as to carry out early warning and maintenance, avoid the occurrence of equipment failure, improve the reliability and availability of equipment, and ensure the stable operation of thermal power units;

[0050] The calculation formula of the feedwater pump operation data group GSyx is as follows:

[0051]

[0052] In the above formula, Represents the minimum outlet pressure data of the water supply pump under standard conditions. Represents the minimum flow data of the water supply pump under standard conditions. Represents the highest vibration data of the water supply pump under standard conditions. Represents the feedwater pump pressure difference between the feedwater pump outlet pressure data and the feedwater pump minimum outlet pressure data under standard conditions. Represents the difference between the feed water pump flow rate data and the minimum feed water pump flow rate data under standard conditions. Represents the vibration difference between the feed water pump vibration data and the maximum vibration data of the feed water pump under standard conditions;

[0053] By combining the water pump outlet pressure data, water pump flow data, and water pump vibration data and comparing them with the corresponding standard values, abnormal conditions of the water pump equipment can be discovered in time, such as excessive pressure fluctuations, unstable flow, or excessive vibration, so as to carry out early warning and maintenance to avoid water pump equipment failures.

[0054] The calculation formula of the steam turbine operation data group QLyx is as follows:

[0055]

[0056] In the above formula, Represents the maximum vibration data of the steam turbine under standard conditions. Represents the lowest pressure data of the steam turbine under standard conditions. Represents the minimum flow data of the steam turbine under standard conditions, Represents the maximum temperature data of the steam turbine under standard conditions, Represents the difference between the turbine vibration data and the maximum turbine vibration data under standard conditions. Represents the turbine pressure difference between the turbine pressure data and the minimum turbine pressure data under standard conditions. Represents the difference between the turbine flow rate data and the minimum turbine flow rate data under standard conditions. Represents the turbine temperature difference between the turbine temperature data and the maximum turbine temperature data under standard conditions;

[0057] By combining the steam turbine operation data set including steam turbine vibration data, steam turbine pressure data, steam turbine flow data, and steam turbine temperature data, and then comparing them with the corresponding standard values, the operation status of the steam turbine is comprehensively analyzed from the aspects of steam turbine vibration, pressure, flow, and temperature. Abnormal conditions of the steam turbine, such as imbalance, pressure fluctuation, unstable flow, or overheating, can be discovered in time, so as to carry out early warning and maintenance to avoid the occurrence of steam turbine failures;

[0058] The calculation method of boiler operation data set GLyx is as follows:

[0059]

[0060] In the above formula, Represents the lowest water level data of the boiler under standard conditions. Represents the maximum boiler temperature data under standard conditions. Represents the lowest boiler pressure data under standard conditions. Represents the boiler water level difference between the boiler water level data and the boiler minimum water level data under standard conditions. Represents the boiler temperature difference between the boiler temperature data and the boiler maximum temperature data under standard conditions, Represents the boiler pressure difference between the boiler pressure data and the boiler minimum pressure data under standard conditions;

[0061] By combining the boiler water level data, boiler temperature data, and boiler pressure data and comparing them with the corresponding standard values, the boiler's operating status can be comprehensively analyzed from the aspects of boiler water level, temperature, and pressure. This can timely detect abnormal conditions of the boiler, such as low water level, high temperature, or pressure fluctuations, so as to provide early warning and maintenance to avoid boiler failures.

[0062] Step 6: Analyze the operating status of the coal mill, feed water pump, steam turbine and boiler according to the coal mill operating data group MMyx, the feed water pump operating data group GSyx, the steam turbine operating data group QLyx and the boiler operating data group GLyx, and judge whether corresponding adjustments are needed according to the corresponding operating status, and send an abnormal signal for maintenance if adjustments are needed;

[0063] When any value in the coal mill operation data group MMy, the feed water pump operation data group GSyx, the turbine operation data group QLyx, and the boiler operation data group GLyx is less than 0, it means that the corresponding equipment has a fault or performance degradation, and the corresponding fault signal is sent to the maintenance module;

[0064] The operating status of the coal mill, feed water pump, steam turbine and boiler is analyzed in many aspects according to the coal mill operation data group MMyx, the feed water pump operation data group GSyx, the turbine operation data group QLyx and the boiler operation data group GLyx. Accurate prediction and prevention of failures can reduce unplanned downtime, which together contributes to the stable operation and economic benefits of the entire thermal power unit, improves the reliability and operating efficiency of the equipment, extends the life of the equipment, and realizes the peak load balance optimization of the thermal power unit.

[0065] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A load balancing optimization method for peak load regulation of thermal power units, characterized in that: The following steps are involved: Step 1: monitor the operating status of the coal mill in the thermal power unit, and collect coal mill operating data to form a coal mill operating data set; Step 2: monitor the operating status of the feedwater pump in the thermal power unit, and collect the feedwater pump operating data to form a feedwater pump operating data set; Step 3: monitor the operating status of the steam turbine in the thermal power unit, and collect the steam turbine operating data to form a steam turbine operating data set; Step 4: monitor the operating status of the boiler in the thermal power unit and collect boiler operating data to form a boiler operating data set; Step 5, respectively analyzing the coal mill operation data set MMyx, the feed water pump operation data set GSyx, the steam turbine operation data set QLyx, and the boiler operation data set GLyx according to the coal mill operation data set, the feed water pump operation data set, the steam turbine operation data set, and the boiler operation data set; Step six, analyze the operating status of the coal mill, feed water pump, steam turbine and boiler according to the coal mill operating data group MMyx, feed water pump operating data group GSyx, turbine operating data group QLyx and boiler operating data group GLyx, and determine whether corresponding adjustments are needed according to the corresponding operating status. If adjustments are needed, send abnormal signals for maintenance.

2. The load balancing optimization method for peak load regulation of thermal power generation units according to claim 1, characterized in that: In the step 1, the coal mill operation data set includes coal mill vibration data, coal mill temperature data, and coal mill flow data. The expression of the coal mill operation data set is: {MM zd ,MM wd ,MM ll }, where MM zd Represents the vibration data of coal mill, MM wd Represents the coal mill temperature data, MM ll Represents coal mill flow data.

3. The load balancing optimization method for peak load regulation of thermal power generation units according to claim 2 is characterized in that: In the step 2, the water supply pump operation data set includes the water supply pump outlet pressure data, the water supply pump flow data, and the water supply pump vibration data. The expression of the water supply pump operation data set is: {GS yl GS ll GS zd }, where GS yl Represents the outlet pressure data of the feedwater pump, GS ll Represents the feedwater pump flow data, GS zd Represents the vibration data of the water supply pump.

4. The load balancing optimization method for peak load regulation of thermal power generation units according to claim 3 is characterized in that: In step 3, the turbine operation data set includes turbine vibration data, turbine pressure data, turbine flow data, and turbine temperature data. The expression of the turbine operation data set is: {QL zd ,QL yl ,QL ll ,QL wd }, where QL zd Represents turbine vibration data, QL yl Represents the turbine pressure data, QL ll Represents the turbine flow data, QL wd Represents turbine temperature data.

5. The load balancing optimization method for peak load regulation of thermal power generation units according to claim 4 is characterized in that: In step 4, the boiler operation data set includes boiler water level data, boiler temperature data, and boiler pressure data. The expression of the boiler operation data set is: {GL sw GL wd GL yl }, where GL sw Represents boiler water level data, GL wd Represents boiler temperature data, GL yl Represents boiler pressure data.

6. The load balancing optimization method for peak load regulation of thermal power generation units according to claim 5, characterized in that: In step 5, the calculation formula of the coal mill operation data set MMyx is as follows: In the above formula, Represents the highest vibration data of coal mill under standard conditions. Represents the maximum temperature data of the coal mill under standard conditions, Represents the minimum flow rate data of the coal mill under standard conditions, The difference between the vibration data of the coal mill and the maximum vibration data of the coal mill under standard conditions. The difference between the coal mill temperature data and the coal mill maximum temperature data under standard conditions. It represents the difference between the coal mill flow rate data and the minimum flow rate data of the coal mill under standard conditions.

7. The load balancing optimization method for peak load regulation of thermal power generation units according to claim 6, characterized in that: In step 5, the calculation formula of the water supply pump operation data group GSyx is as follows: In the above formula, Represents the minimum outlet pressure data of the water supply pump under standard conditions. Represents the minimum flow data of the water supply pump under standard conditions. Represents the highest vibration data of the water supply pump under standard conditions. Represents the feedwater pump pressure difference between the feedwater pump outlet pressure data and the feedwater pump minimum outlet pressure data under standard conditions. Represents the difference between the feed water pump flow rate data and the minimum feed water pump flow rate data under standard conditions. Represents the vibration difference between the water supply pump vibration data and the maximum vibration data of the water supply pump under standard conditions.

8. The load balancing optimization method for peak load regulation of thermal power generation units according to claim 7, characterized in that: In step 5, the calculation formula of the steam turbine operation data group QLyx is as follows: In the above formula, Represents the maximum vibration data of the steam turbine under standard conditions. Represents the lowest pressure data of the steam turbine under standard conditions. Represents the minimum flow data of the steam turbine under standard conditions, Represents the maximum temperature data of the steam turbine under standard conditions, Represents the difference between the turbine vibration data and the maximum turbine vibration data under standard conditions. Represents the turbine pressure difference between the turbine pressure data and the minimum turbine pressure data under standard conditions. Represents the difference between the turbine flow rate data and the minimum turbine flow rate data under standard conditions. Represents the turbine temperature difference between the turbine temperature data and the maximum turbine temperature data under standard conditions.

9. The load balancing optimization method for peak load regulation of thermal power generation units according to claim 8, characterized in that: In step 5, the boiler operation data group GLyx is calculated as follows: In the above formula, Represents the lowest water level data of the boiler under standard conditions. Represents the maximum boiler temperature data under standard conditions. Represents the lowest boiler pressure data under standard conditions. Represents the boiler water level difference between the boiler water level data and the boiler minimum water level data under standard conditions. Represents the boiler temperature difference between the boiler temperature data and the boiler maximum temperature data under standard conditions, Represents the boiler pressure difference between the boiler pressure data and the boiler minimum pressure data under standard conditions.

10. The load balancing optimization method for peak load regulation of thermal power generation units according to claim 9, characterized in that: In the step six, when any value in the coal mill operation data group MMy, the feed water pump operation data group GSyx, the turbine operation data group QLyx, and the boiler operation data group GLyx is less than 0, it means that the corresponding equipment has a fault or performance degradation, and the corresponding fault signal is sent to the maintenance module.

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