Self-adaptive optimization method for cold end operation mode of expansion unit system thermal power generating unit

Abstract

The invention relates to a self-adaptive optimization method for a cold-end operation mode of an expanded unit system thermal power generating unit, and designs a set of self-optimization method for the cold-end operation mode based on historical data mining and working condition accurate discrimination technologies, so as to accurately obtain operation parameters and states of all working conditions of cold-end equipment of a steam turbine. According to the method, heavy and complex field test work and modeling calculation of a traditional test are avoided, the situation that cold end optimization parameters are not suitable any more due to aging of the unit can be avoided, the optimal operation mode of cold end equipment is continuously found, and the operation economy of the unit is improved.

Description

Technical Field

[0001] The present invention relates to a method for adaptively optimizing the cold-end operation mode of an extended unit-type thermal power unit. Background Art

[0002] The cold-end equipment of a steam turbine consists of components such as the last stage of the low-pressure cylinder, a condenser, a cooling tower, and a circulating water pump. When the new steam flow rate of the unit is constant, an increase in the circulating water flow rate will cause the exhaust pressure to decrease and the output power of the unit to increase. However, the power consumption of the circulating water pump will also increase accordingly. Therefore, when the difference between the increase in the unit's power and the increase in the power consumption of the circulating water pump is the largest, the net revenue power of the unit is the largest, and at this time, the condenser pressure is the optimal back pressure.

[0003] Traditional cold-end optimization requires measurements of dozens of operating conditions under boundary conditions such as different loads, back pressures, circulating water temperatures, and circulating water pump operating modes. This not only requires a large amount of human and material resources but also makes it difficult to obtain data for certain operating conditions, resulting in a significant extension of the test period. Moreover, as the operating years of the unit increase, the cold-end equipment deteriorates in a way that is difficult to repair, causing the previous cold-end optimization test results to become inapplicable. Therefore, it is necessary to conduct cold-end optimization tests again to ensure that the unit operates under the best vacuum state. Thus, its improvement and innovation are imperative. Summary of the Invention

[0004] In view of the above situation, to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a method for adaptively optimizing the cold-end operation mode of an extended unit-type thermal power unit, which can effectively solve the problem of finding the best operation mode of the cold-end equipment.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A method for adaptively optimizing the cold-end operation mode of an extended unit-type thermal power unit, comprising the following steps:

[0007] The first step: judging the stability of the operating condition

[0008] Collect historical data of the generator power and the ambient temperature, and judge the stability of the historical data. The judgment formula is as shown in the following formula:

[0009]

[0010] In the formula, n represents the number of data points within the time period [t - d, d]; represents the parameter value corresponding to the moment i; represents the average value of the parameters within the time period [t - d, d]; ξ represents the set threshold, taking 95%;

[0011] The historical data is judged for stability every 5 minutes. The historical data that does not meet the requirements of the above formula is excluded as the unit's variable operating condition data, and the data that meets the requirements of the above formula is used as the stable operating condition data for subsequent steps;

[0012] Step 2: Conduct condition classification according to the operating characteristics of the cold-end equipment of the thermal power unit

[0013] After obtaining the stable operating condition data, classify it according to the condition categories. The main boundary conditions that have a greater impact on the operating conditions of the unit's cold end are load and ambient temperature. Conduct fuzzy clustering on the actual operating historical data of load and ambient temperature respectively. Divide the minimum operating load to the maximum operating load of the unit into one category every 20 MW, with a total of n clustering numbers, and divide the minimum ambient temperature to the maximum ambient temperature into one category every 1 °C, with a total of m clustering numbers. Divide the unit's operating conditions into n×m categories;

[0014] Step 3: Calculate the benefits of different circulating pump operating modes under each condition

[0015] Define the "net power" of the unit as: the output of the unit after correcting the current back pressure to the rated back pressure, minus the current power consumption of the circulating pump:

[0016] P = E + P Gain -P cost

[0017] In the formula: P represents the "net power" of the unit, in kW; E represents the current output of the unit, in kW; P gain represents the increase in output after correcting the unit to the rated back pressure, in kW; P cost represents the power consumption of the circulating water pump, in kW;

[0018] The optimization rule is: the vacuum corresponding to the maximum "net power" of the unit is the optimal vacuum under the current load and the current circulating water inlet temperature, and the circulating pump operating mode corresponding to the flow rate of the optimal vacuum is the optimal circulating pump operating mode.

[0019] Step 4: Explore the best circulating pump operating mode under each condition

[0020] Based on the first three steps, different circulating pump operating modes under the same conditions are obtained. With the maximum "net power" as the goal, find the best operating mode among different operating modes, store it in the database and display it to the operation. Whenever the unit runs to a certain condition, a best circulating pump operating mode is provided.

[0021] The method of the present invention provides a self-optimization method for the cold-end operation mode based on historical data mining and accurate working condition discrimination technology, which designs a set of self-optimization methods for the cold-end operation mode, accurately obtains the operation parameters and states of all working conditions of the steam turbine cold-end equipment, avoids the heavy and complex on-site tests and modeling calculations of traditional tests, and can avoid the inapplicability of the cold-end optimization parameters caused by the aging of the unit. It continuously searches for the best operation mode of the cold-end equipment and improves the operation economy of the unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0023] The following further describes in detail the specific embodiments of the present invention in conjunction with the drawings and embodiments.

[0024] As Figure 1 shown, an adaptive optimization method for the cold-end operation mode of an extended unit-type thermal power unit of the present invention includes the following steps:

[0025] The first step: Judging the stability of the operating condition

[0026] Collect the historical data of the generator power and the ambient temperature, and judge the stability of the historical data. The judgment formula is shown as follows:

[0027]

[0028] In the formula, n represents the number of data points in the time period [t - d, d]; represents the parameter value corresponding to the moment i; represents the average value of the parameters in the time period [t - d, d]; ξ represents the set threshold, taking 95%;

[0029] Judge the stability of the historical data every 5 minutes. The historical data that does not meet the requirements of the above formula is excluded as the unit variable condition data, and the historical data that meets the requirements of the above formula is used as the stable condition data for the subsequent steps;

[0030] The historical data contains both stable and variable operating data of the unit. Only the stable operating data is of practical significance for finding the benchmark value of the index. Therefore, the historical data must first be processed for "stability judgment" to eliminate the data of unstable operating conditions. This step is based on the ASME unit performance test procedures and the actual situation of the unit under the comprehensive consideration of the influence of external constraints. The main operating parameters that reflect whether the cold end of the unit is in a stable state are motor power and ambient temperature. When judging stability, the reasonable range of the upper and lower fluctuations of the load and ambient temperature is ±5%, and the corresponding operating stability index value is 95%; then the load and ambient temperature within the current time range are given through data screening and comparison. The corresponding average value. When the result of the operation data of this section is stable, it is considered that the data within 5 minutes can be used for the next algorithm.

[0031] Step 2: Divide the working conditions according to the operating characteristics of the cold end equipment of the thermal power unit

[0032] After obtaining the stable operating condition data, it is divided into operating condition categories. The main boundary conditions that have a greater impact on the cold end operating conditions of the unit are load and ambient temperature. Fuzzy clustering is performed on the actual operating history data of load and ambient temperature. The unit's lowest operating load to the maximum operating load is divided into n clusters with 20MW as one category, and the lowest ambient temperature to the highest ambient temperature is divided into m clusters with 1℃ as one category. The unit's operating conditions are divided into n×m categories.

[0033] Taking a 300MW expansion unit as an example, from the lowest operating load of 80MW to the highest operating load of 300MW, there are 11 clusters of operating conditions for every 20MW, and the number of clusters is 23 for the ambient temperature, which starts from the lowest temperature of 9℃ throughout the year to the highest temperature of 32℃. The 11 types of circulating pump operation modes divide the unit operation status into 11×23=253 types, as shown in the following table:

[0034] Table 1 Working condition classification table

[0035]

[0036] Step 3: Calculation of benefits of different circulating pump operation modes under various working conditions

[0037] Under certain load and circulating water inlet temperature conditions, which circulating water pump operation mode can maximize the unit "benefit" depends on the interaction of multiple factors such as circulating water inlet temperature, unit load, condenser heat transfer coefficient, circulating water pump characteristic parameters, boiler, turbine thermal characteristics, etc. Increasing the number of circulating water pumps in operation or increasing the frequency of variable frequency pumps will increase the circulating water flow rate and circulating water pump power consumption, which will cause two effects:

[0038] (1) When the circulating water flow rate increases, the temperature rise of the circulating water in the condenser will decrease, the exhaust pressure of the unit will increase, and the output of the generator will increase;

[0039] (2) When the power consumption of the circulating water pump increases, the auxiliary power of the power plant will increase, and the power supply of the unit will decrease. Therefore, under certain circulating water inlet temperature and unit load conditions, it is necessary to determine through optimization and comparison which operating mode of the circulating water pump can maximize the "benefit" of the whole machine.

[0040] Define the "net power" of the unit as: the output of the unit after the current back pressure of the unit is corrected to the rated back pressure, minus the current power consumption of the circulating water pump:

[0041] P = E + P Gain -P cost

[0042] In the formula: P represents the "net power" of the unit, kW; E represents the current output of the unit, kW; P gain represents the increase in the output of the unit after being corrected to the rated back pressure, kW; P cost represents the power consumption of the circulating water pump, kW;

[0043] The optimization rule is: the vacuum corresponding to the maximum "net power" of the unit is the optimal vacuum under the current load and the current circulating water inlet temperature, and the operating mode of the circulating water pump corresponding to the optimal vacuum and flow rate is the optimal operating mode of the circulating water pump.

[0044] Step 4: Explore the best operating mode of the circulating water pump under each working condition

[0045] According to the first three steps, different operating modes of the circulating water pump under the same working conditions are obtained. Taking the maximum "net power" as the goal, find the best operating mode among different operating modes, store it in the database and display it to the operation. Whenever the unit runs to a certain working condition, an optimal operating mode of the circulating water pump is provided. The total calculation process of the method is as Figure 1 shown.

[0046] Taking a 300MW extended unit type thermal power unit as an example, a one-week traditional cold end optimization test was carried out on site, and measurements and calculations were carried out for 20 working conditions with different unit loads and environmental temperatures as boundary conditions. During the test process, the cold end operating parameters were adaptively optimized using this system at the same time, and the calculated results were the same as the test measurement and calculation results. The specific data is shown in the following table:

[0047]

Claims

1. An adaptive optimization method for expanding the cold end operation mode of a unit thermal power unit, characterized in that: The following steps are involved: Step 1: Determine whether the operating condition is stable Collect historical data of generator power and ambient temperature, and judge the stability of historical data. The judgment formula is as follows: Where n represents the number of data points in the time period [td,d]; Indicates the parameter value corresponding to time i; represents the parameter mean value in the time period [td,d]; ξ represents the set threshold value, which is 95%; The historical data is judged to be stable every 5 minutes. The historical data that does not meet the requirements of the above formula will be eliminated as the unit variable operating condition data, and the historical data that meets the requirements of the above formula will be used as the stable operating condition data for subsequent steps; Step 2: Divide the working conditions according to the operating characteristics of the cold end equipment of the thermal power unit After obtaining the stable operating condition data, it is divided into operating condition categories. The main boundary conditions that have a greater impact on the cold end operating conditions of the unit are load and ambient temperature. Fuzzy clustering is performed on the actual operating history data of load and ambient temperature. The unit's lowest operating load to the maximum operating load is divided into n clusters with 20MW as one category, and the lowest ambient temperature to the highest ambient temperature is divided into m clusters with 1℃ as one category. The unit's operating conditions are divided into n×m categories. Step 3: Calculation of benefits of different circulating pump operation modes under various working conditions The definition of "net power" of the unit is: the unit output after the current back pressure of the unit is corrected to the rated back pressure, minus the current circulating pump power consumption: P=E+P Gain -P cost Where: P represents the "net power" of the unit, kW; E represents the current output of the unit, kW; P gain Indicates the increase in output when the unit is corrected to rated back pressure, kW; P cost Indicates the power consumption of the circulating water pump, kW; The optimization rule is: the vacuum corresponding to the maximum "net power" of the unit is the optimal vacuum under the current load and current circulating water inlet temperature, and the circulating pump operation mode corresponding to the flow rate of the optimal vacuum is the optimal circulating pump operation mode. Step 4: Explore the best pump operation mode under various working conditions Based on the first three steps, different circulating pump operation modes under various same working conditions are obtained. Taking the maximum "net power" as the goal, the best operation mode among different operation modes is found and stored in the database and displayed to the operation. Whenever the unit runs to a certain working condition, an optimal circulating pump operation mode is provided.

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