A method and system for operating a cold-end system of a gas combined cycle unit

Abstract

The application discloses a kind of gas combined cycle unit cold end system operation control method and system, with the lowest unit power generation of cold end system as optimization target, constructs the coupling strength of equipment is considered cold end system multi-objective collaborative optimization mathematical model, model is with equipment operation constraint, process index constraint, safety constraint and multi-device collaborative constraint as boundary condition, solves cold end system multi-objective collaborative optimization mathematical model using preset algorithm, output optimal operating state instruction and collaborative control strategy, according to optimal operating state instruction and collaborative control strategy to gas combined cycle unit cold end system is controlled, avoid the problem that other link energy consumption is caused by pursuing single index optimum and increases sharply, solve the technical problems that existing gas combined cycle unit cold end system operation control mode only focuses on condenser vacuum maximization, it is difficult to realize the comprehensive energy efficiency optimization and the lowest operating cost of entire cold end system even unit.

Description

Technical Field

[0001] This invention relates to the field of gas combined cycle unit operation optimization technology, and in particular to a method and system for operating control of the cold end system of a gas combined cycle unit. Background Technology

[0002] A gas turbine combined cycle (GTCB) generator set is a power generation unit consisting of a gas turbine, a waste heat boiler, and a steam turbine. It generates electricity by driving the steam turbine with exhaust gas from the gas turbine, primarily used in power generation and combined heat and power (CHP) applications. The cold end system mainly encompasses core components such as the condenser, cooling tower, circulating water pump, and extraction system. These components play an indispensable and crucial role in the normal operation of the cold end system. Sensors are installed at key parameter monitoring points in the cold end system. These key parameter monitoring points include locations that accurately reflect the system's operating status, such as condenser vacuum, circulating water inlet temperature, circulating water pump current, and cooling tower outlet water temperature. The sensors can continuously and in real-time collect various data closely related to system operation throughout the entire operation of the cold end system and transmit this data to the control unit promptly and accurately. The control unit strictly follows pre-verified and carefully set empirical formulas or simple mathematical models, such as a simplified model based on the condenser heat balance equation. Such models can reflect the heat exchange relationship of the system relatively clearly and accurately to a certain extent, providing an important basis for subsequent control operations.

[0003] Existing gas-fired combined cycle (GWC) cold-end systems prioritize maximizing condenser vacuum as the sole optimization objective, employing particle swarm optimization (PSO) to comprehensively, deeply, and meticulously optimize the number and speed of circulating water pumps. This complex and rigorous optimization process aims to rationally adjust the circulating water flow rate, thereby reducing turbine back pressure and improving overall unit operating efficiency, allowing the unit to operate in a more economical, environmentally friendly, and efficient manner. However, this focus solely on maximizing condenser vacuum in the GWC cold-end system's operation and control fails to adequately consider the energy costs of individual devices within the cold-end system and their interrelationships. This can lead to a surge in energy consumption in other areas while pursuing the optimization of a single indicator, making it difficult to achieve optimal overall energy efficiency and lowest operating costs for the entire cold-end system and even the entire unit. Summary of the Invention

[0004] This invention provides a method and system for operating control of the cold-end system of a gas-fired combined cycle unit, which solves the technical problem that existing operating control methods for the cold-end system of gas-fired combined cycle units only focus on maximizing the condenser vacuum, making it difficult to achieve optimal overall energy efficiency and lowest operating cost for the entire cold-end system and even the unit.

[0005] In view of this, the first aspect of the present invention provides a method for operating control of the cold end system of a gas combined cycle unit, comprising:

[0006] With the goal of minimizing the unit power generation of the cold-end system, a multi-objective collaborative optimization mathematical model for the cold-end system is constructed, taking into account the coupling strength between equipment. The multi-objective collaborative optimization mathematical model for the cold-end system includes objective functions and constraints, including equipment operation constraints, process index constraints, safety constraints, and multi-equipment collaborative constraints.

[0007] A pre-defined algorithm is used to solve the multi-objective collaborative optimization mathematical model of the cold-end system, and the optimal operating state command and collaborative control strategy are output.

[0008] The cold-end system of the gas combined cycle unit is controlled according to the optimal operating state command and the cooperative control strategy.

[0009] Optionally, the objective function is:

[0010]

[0011]

[0012]

[0013] Where f represents the energy consumption per unit of power generation in the cold-end system. This represents the total energy consumption of the cold-end system. The energy savings corresponding to the synergistic benefits For the unit's power generation, A represents one type of equipment to be evaluated, and B represents another type of equipment to be evaluated. The coupling strength between devices of types A and B. For Class A equipment, For Class B equipment, The correlation coefficient between Class A and Class B equipment. This is the coefficient for switching collaborative strategies. The current unit load, This represents the unit load at the previous moment. The ambient temperature at the current moment. The ambient temperature at the previous moment. This refers to the rated load or reference load of the unit.

[0014] Optionally, process parameters constraints include circulating water temperature rise constraints, condenser vacuum constraints, and circulating water inlet temperature constraints.

[0015] The circulating water temperature rise constraint is:

[0016]

[0017] The vacuum constraint of the condenser is:

[0018]

[0019] The circulating water inlet temperature constraint is:

[0020]

[0021] in, The inlet temperature of the circulating water. The outlet temperature of the circulating water. To minimize the allowable temperature rise, For the maximum allowable temperature rise, To the minimum permissible vacuum level, For condenser vacuum, This is the maximum permissible inlet water temperature.

[0022] Optionally, the equipment operation constraints include constraints on the number of circulating water pumps in operation, matching constraints between fans and water pumps, and matching constraints between the upper tower gate and water pumps;

[0023] The constraint on the number of circulating water pumps in operation is:

[0024]

[0025] in, To minimize the number of units in operation, The maximum number of operating pumps is n, where n is the total number of circulating water pumps in operation. This represents the operating status of the i-th circulating water pump.

[0026] The matching constraints between the fan and the water pump are:

[0027]

[0028] in, The number of large pumps in operation. Here, j represents the number of small pumps in operation, j is the serial number identifier for the fan used to distinguish different mechanical tower fans, and m is the total number of mechanical tower fans in the cold end system. The operating status of the j-th fan;

[0029] The matching constraints between the upper tower gate and the water pump are:

[0030]

[0031] Where k is the serial number identifier of the upper tower gate, used to distinguish different upper tower gates in the cold end system; p is the total number of upper tower gates in the cold end system; i is the serial number identifier of the circulating water pump, used to distinguish different circulating water pumps in the cold end system; and n is the total number of circulating water pumps in the cold end system. Let be the operating state variable of the k-th tower gate.

[0032] Optionally, security constraints include equipment standby constraints and equipment switching interval constraints;

[0033] Equipment standby constraints are:

[0034]

[0035] in, The main pump is in standby mode. For the lubricating oil pressure of the large pump, This refers to the bearing temperature of the large pump.

[0036] The equipment switching interval constraint is:

[0037]

[0038] in, The interval between two start-ups and shutdowns of the same device.

[0039] Optionally, the preset algorithm is an improved particle swarm optimization-simulated annealing algorithm.

[0040] Optionally, a pre-defined algorithm is used to solve the multi-objective cooperative optimization mathematical model of the cold-end system, outputting the optimal operating state command and cooperative control strategy, including:

[0041] S1. Initialize the particle swarm algorithm by configuring the particle swarm population size, initial state, and fitness.

[0042] S2. Initialize the simulated annealing algorithm by setting the initial temperature and obtaining the initial solution;

[0043] S3. Substitute the initial solution into the particle swarm algorithm to update the particle velocity and position using the velocity and orientation information of the individual particle limit values ​​and the global particle limit values.

[0044] S4. Calculate particle swarm fitness based on the updated particle velocity and position;

[0045] S5. Based on the calculated particle swarm fitness for the optimal positions of individual particles and the optimal positions of the swarm population, the evaluation function and new solution are obtained.

[0046] S6. Determine whether the iteration termination condition has been met. If yes, output the current optimal solution. If no, substitute the new solution into the velocity and orientation information of the particle swarm algorithm based on the individual particle limit values ​​and the global particle limit values ​​to update the particle velocity and position, and return to step S4.

[0047] Optionally, it also includes:

[0048] Collect real-time operating data of the cold-end system of the gas-fired combined cycle unit;

[0049] Calculate the actual unit power generation energy consumption or synergistic efficiency factor of the cold end system of the gas combined cycle unit based on real-time operating data.

[0050] If the actual unit power generation energy consumption deviates from the optimal solution of the multi-objective collaborative optimization mathematical model of the cold end system obtained by using the preset algorithm by a greater than the first threshold, or the collaborative efficiency factor is lower than the second threshold, then the initialization parameters and collaborative control strategy of the preset algorithm are adjusted, the multi-objective collaborative optimization mathematical model of the cold end system is solved again, and the optimal operating state command and collaborative control strategy are output.

[0051] Optionally, the first threshold is 5%, and the second threshold is 0.85.

[0052] A second aspect of the present invention provides an operation control system for the cold end system of a gas combined cycle unit, comprising:

[0053] The multi-objective model construction module is used to construct a multi-objective collaborative optimization mathematical model of the cold-end system with the goal of minimizing the unit power generation of the cold-end system. The model considers the coupling strength between equipment. The multi-objective collaborative optimization mathematical model of the cold-end system includes objective functions and constraints. The constraints include equipment operation constraints, process index constraints, safety constraints, and multi-equipment collaborative constraints.

[0054] The solver module is used to solve the multi-objective collaborative optimization mathematical model of the cold-end system using a preset algorithm, and output the optimal operating state command and collaborative control strategy.

[0055] The operation control module is used to control the cold end system of the gas combined cycle unit according to the optimal operating state command and the cooperative control strategy.

[0056] As can be seen from the above technical solutions, the operation control method for the cold end system of a gas-fired combined cycle unit provided by the present invention has the following advantages:

[0057] The present invention provides a method for operating and controlling the cold-end system of a gas-fired combined cycle unit. With the goal of minimizing the unit power generation of the cold-end system, a multi-objective collaborative optimization mathematical model of the cold-end system considering the coupling strength between equipment is constructed. The model uses equipment operation constraints, process index constraints, safety constraints, and multi-equipment collaborative constraints as boundary conditions. A pre-set algorithm is used to solve the multi-objective collaborative optimization mathematical model of the cold-end system, outputting optimal operating state commands and collaborative control strategies. The cold-end system of the gas-fired combined cycle unit is controlled according to these optimal operating state commands and collaborative control strategies. This avoids the problem of energy consumption in other aspects surging in pursuit of a single optimal index. It solves the technical problem that existing operating and control methods for the cold-end system of gas-fired combined cycle units only focus on maximizing condenser vacuum, making it difficult to achieve optimal overall energy efficiency and minimum operating costs for the entire cold-end system and even the entire unit.

[0058] Meanwhile, the gas-fired combined cycle unit cold-end system operation control method provided by this invention uses an improved particle swarm optimization-simulated annealing algorithm to solve the multi-objective collaborative optimization mathematical model of the cold-end system, outputting the optimal operating state command and collaborative control strategy. It integrates the powerful global search capability of the improved particle swarm optimization algorithm and the good local optimization and escape capabilities of the simulated annealing algorithm, effectively solving the problems of single algorithms being prone to getting trapped in local optima, slow convergence speed, or insufficient optimization accuracy. When dealing with the complex dynamic characteristics and multivariate coupling relationships of the cold-end system, it exhibits better global optimization capability and convergence speed, enabling the system to maintain high control accuracy and good robustness under variable operating conditions such as unit load fluctuations, ambient temperature changes, and water quality differences, avoiding the defects of traditional PID control or single algorithms in response lag and accuracy reduction during operating condition switching.

[0059] The gas-fired combined cycle unit cold-end system operation control method provided by this invention places safety constraints in an important position during the model construction and optimization process. For example, key safety indicators such as the lower limit of circulating water flow and the upper and lower limits of vacuum are strictly incorporated into the constraint conditions. The hybrid optimization algorithm strictly adheres to these safety boundaries during the optimization process, ensuring the safety and stability of the cold-end system and even the entire gas-fired combined cycle unit under various operating conditions. This avoids serious consequences such as equipment damage or unplanned unit shutdown that may result from neglecting safety factors or failing to detect faults in a timely manner during the optimization process. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a flowchart illustrating an operation control method for the cold end system of a gas-fired combined cycle unit provided in an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of the operation and control system of a gas combined cycle unit cold end system provided in an embodiment of the present invention. Detailed Implementation

[0063] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] For easier understanding, please refer to Figure 1 This invention provides an embodiment of an operation control method for the cold end system of a gas combined cycle unit, comprising:

[0065] Step 101: With the minimum unit power generation of the cold-end system as the optimization objective, construct a multi-objective collaborative optimization mathematical model for the cold-end system that considers the coupling strength between equipment. The multi-objective collaborative optimization mathematical model for the cold-end system includes objective functions and constraints. The constraints include equipment operation constraints, process index constraints, safety constraints, and multi-equipment collaborative constraints.

[0066] It should be noted that, with the goal of minimizing the unit power generation of the cold-end system (i.e., the ratio of total energy consumption of the cold-end system to the unit's power generation), a multi-objective collaborative optimization mathematical model for the cold-end system, considering the coupling strength between equipment, is constructed. The mathematical model for minimizing the unit power generation of the cold-end system is as follows:

[0067]

[0068] Where f represents the energy consumption per unit of power generation in the cold-end system, with units of kW·h / (kW·h). This represents the total energy consumption of the cold-end system, expressed in kW·h. The energy savings corresponding to the synergistic benefits are expressed in kW·h. This refers to the generator's power output, measured in kW.

[0069] Energy savings corresponding to synergistic benefits The formula for calculation is:

[0070]

[0071] in, The synergistic benefit benchmark coefficient ranges from 0.03 to 0.08 and can be dynamically adjusted according to the unit load; the higher the load, the larger the value. For the coordination efficiency between equipment of type i, the equipment type corresponding to i=1 is water pump-fan, the equipment type corresponding to i=2 is fan-upper tower gate, and the equipment type corresponding to i=3 is condenser-circulating water.

[0072] Pump-fan coordination efficiency The formula for calculation is:

[0073]

[0074] in, This represents the actual total flow rate of the water pump. The rated total flow rate of the water pump This represents the actual average speed of the fan. The rated speed of the fan. This is the matching coefficient, ranging from 1.2 to 1.8. It can be dynamically adjusted according to the water hardness, with a larger value for poorer water quality.

[0075] Wind turbine-upper tower gate coordination efficiency The formula for calculation is:

[0076]

[0077]

[0078] in, This represents the effective heat dissipation area after the upper tower door is opened. Let j be the heat dissipation area corresponding to the tower door on the j-th platform. Let be the gate opening coefficient on the j-th platform. This represents the maximum heat dissipation area when the upper tower door is fully open.

[0079] Condenser-circulating water synergistic efficiency The formula for calculation is:

[0080]

[0081]

[0082] in, This represents the actual temperature rise of the circulating water. To achieve the optimal temperature rise for the condenser. This represents the actual load of the generating unit, in MW. The ambient temperature is expressed in °C.

[0083] A collaborative efficiency factor is introduced to characterize the collaborative operation effect of equipment. The formula for calculating the collaborative efficiency factor is:

[0084]

[0085] In the formula: The collaborative efficiency factor (range 0.5-1.0) indicates that the closer it is to 1.0, the better the collaborative operation of the equipment. For pump-fan coordination efficiency; For the coordinated efficiency of the wind turbine and the upper tower gate; For condenser-circulating water synergistic efficiency; For the weighting coefficients, satisfying Take respectively .

[0086] Total energy consumption of cold end system The formula for calculation is:

[0087]

[0088] in, This refers to the total power of the circulating water pump. This refers to the total power of the mechanical tower fan. This represents the total power of the upper tower gate drive unit.

[0089] Total power of circulating water pump The formula for calculation is:

[0090]

[0091] Where n is the total number of circulating water pumps. The rated power of the i-th circulating water pump is... This represents the operating status of the i-th circulating water pump, where 1 indicates operation and 0 indicates shutdown.

[0092] Total power of the mechanical tower fan The formula for calculation is:

[0093]

[0094] Where m represents the total number of mechanical tower fans. The rated power of the j-th wind turbine, This represents the operating status of the j-th wind turbine, where 1 indicates operation and 0 indicates shutdown.

[0095] Total power of the upper tower gate drive unit The formula for calculation is:

[0096]

[0097] Where p is the total number of upper tower gates. The rated power of the gate drive unit on the kth tower is... This represents the operating status of the k-th tower gate, where 1 indicates it is open and 0 indicates it is closed.

[0098] Unit power generation The formula for calculation is:

[0099]

[0100]

[0101] in, To input heat into the gas, For heat loss of the unit, The total power of the unit serves as a collaborative efficiency factor, ranging from 0.5 to 1.0. It is used to quantify the collaborative operation effect of the circulating water pump, fan, and upper tower gate. When the matching degree of equipment operating status is high, the value tends to be close to 1.0. , and These are the weighting coefficients, and .

[0102] The mathematical model for multi-objective cooperative optimization of a cold-end system, considering the coupling strength between equipment, also includes the coupling strength between equipment to determine the priority of cooperative control. The coupling strength model is as follows:

[0103]

[0104]

[0105] in, The coupling strength between devices of types A and B. For Class A equipment, For Class B equipment, This represents the correlation coefficient between Class A and Class B equipment, ranging from 0.3 to 0.9. A higher value indicates a stronger correlation. This is the coefficient for switching collaborative strategies. The current unit load, This represents the unit load at the previous moment. The ambient temperature at the current moment. The ambient temperature at the previous moment. This refers to the rated load or reference load of the unit (unit: MW).

[0106] The application rules for the coupling strength model between devices are as follows:

[0107] When the value is greater than 1.2, equipment A should be adjusted first.

[0108] 0.8 When the value is ≤1.2, adjust devices A and B synchronously;

[0109] When the value is ≤0.8, device B should be adjusted first.

[0110] For varying operating conditions, the coefficients are switched using a collaborative strategy. Dynamically adjust the coordination logic of the circulating water pump, fan, and upper tower gate. The switching rules for the circulating water pump, fan, and upper tower gate are as follows:

[0111] when When the value is ≤0.05 (under steady-state conditions), a precise coordination strategy is adopted to prioritize ensuring... , , ≥0.9;

[0112] When 0.05 > When the value is ≤0.15 (under slight variable operating conditions), a rapid coordination strategy is adopted, allowing... , , ≥0.8, prioritizing system stability;

[0113] when When the value is greater than 0.15 (under severe variable operating conditions), an emergency coordination strategy is adopted to lock the core equipment (such as at least one large pump in operation), prioritize safety constraints, and restore precise coordination after the operating conditions stabilize.

[0114] The constraints include equipment operation constraints, process parameter constraints, safety constraints, and multi-equipment coordination constraints.

[0115] Process constraints include circulating water temperature rise constraints, condenser vacuum constraints, and circulating water inlet temperature constraints.

[0116] Since the temperature difference between the inlet and outlet of the circulating water needs to be within a reasonable range to ensure the heat exchange efficiency of the condenser, and also needs to be matched with the flow rate of the circulating water pump and the speed of the fan, the expression for the circulating water temperature rise constraint is as follows:

[0117]

[0118] in, The circulating water inlet temperature (°C). The circulating water outlet temperature (°C). The minimum allowable temperature rise (value is 7°C). The maximum allowable temperature rise (valued at 12°C). A new synergy coefficient is added. (Values ​​range from 0.9 to 1.1), used to correct the temperature rise range under different equipment combinations.

[0119] The condenser vacuum level must be higher than the minimum allowable value to avoid a decrease in unit efficiency, and it must also be optimized in conjunction with circulating water flow rate and fan speed. Therefore, the condenser vacuum constraint is:

[0120]

[0121] in, The minimum permissible vacuum level (value is 30 kPa). This refers to the vacuum level of the condenser.

[0122] The outlet circulating water temperature of the mechanical tower must be below a threshold value, and must be controlled in conjunction with the opening of the upper tower gate and the fan speed. Therefore, the circulating water inlet temperature constraint is:

[0123]

[0124] in, The maximum permissible inlet water temperature (valued at 35°C)

[0125] The equipment operation constraints include the number of circulating water pumps in operation, the matching constraints between the fan and the water pump, and the matching constraints between the upper tower gate and the water pump.

[0126] The number of operating pumps is determined based on the unit load and water temperature, and must also be coordinated with the operating status of the blower and upper tower gate. Therefore, the constraint on the number of circulating water pumps in operation is:

[0127]

[0128] in, This is the minimum number of machines that can be run (with a value of 1). The maximum number of operating pumps is 2, where n is the total number of circulating water pumps in operation. This represents the operating status of the i-th circulating water pump.

[0129] The number of operating fans must match the capacity of the circulating water pumps. For large pumps (rated flow ≥ 1000 m³ / h), four fans need to be in operation; for small pumps (rated flow < 1000 m³ / h), two fans need to be in operation. A dynamic coordinated adjustment mechanism for fan speed and pump flow rate is also added. The matching constraints between fans and pumps are as follows:

[0130]

[0131] in, The number of large pumps in operation. Here, j represents the number of small pumps in operation, j is the serial number identifier of the fan (starting from 1), used to distinguish different mechanical tower fans in the cold end system, and m is the total number of mechanical tower fans in the cold end system. The operating status of the j-th fan is as follows: A new speed adjustment coefficient is added. (Value range: 0.5-1.0), used to dynamically adjust the fan speed according to the water pump flow rate.

[0132] The number of upper tower gates opened must match the number of circulating water pumps in operation. Each pump corresponds to two upper tower gates being opened. A new coordinated adjustment mechanism between the upper tower gate opening and the circulating water flow rate is also implemented. Therefore, the matching constraint between the upper tower gates and the pumps is as follows:

[0133]

[0134] Where k is the serial number identifier of the upper tower gate (starting from 1), used to distinguish different upper tower gates in the cold end system, p is the total number of upper tower gates in the cold end system, i is the serial number identifier of the circulating water pump, and n is the total number of circulating water pumps in the cold end system. Let be the operating state variable of the k-th tower gate.

[0135] Safety constraints include equipment standby constraints and equipment switchover interval constraints.

[0136] When the main pump is shut down, it must meet good standby conditions, namely, lubricating oil pressure ≥ 0.2 MPa and bearing temperature ≤ 65℃, and it must also be associated with the coordinated start-up logic of the standby pump. Therefore, the equipment standby constraints are:

[0137]

[0138] in, The main pump is in standby status (1 for good standby, 0 for non-standby). This refers to the lubricating oil pressure of the large pump (unit: MPa). The bearing temperature of the large pump (unit: °C);

[0139] To avoid mechanical damage caused by frequent equipment start-ups and shutdowns, the interval between two start-ups and shutdowns of the same equipment should not be less than 10 minutes. Furthermore, the switching sequence must be coordinated with that of other equipment to ensure stable system operation. Therefore, the equipment switching interval constraint is:

[0140]

[0141] in, The interval between two start-ups and shutdowns of the same device.

[0142] Step 102: Use a pre-set algorithm to solve the multi-objective collaborative optimization mathematical model of the cold end system, and output the optimal operating state command and collaborative control strategy.

[0143] It should be noted that the cold-end system optimization model is characterized by multiple variables and strong coupling, making it prone to getting trapped in local optima using traditional single algorithms. Therefore, this invention employs a hybrid algorithm of improved particle swarm optimization-simulated annealing (IPSO-SA) to solve the multi-objective collaborative optimization mathematical model of the cold-end system. The solution process is as follows:

[0144] S1. Initialize the particle swarm optimization algorithm, configuring the particle swarm population size, initial state, and fitness. Configure the particle swarm population size as m and the maximum number of iterations. The velocity possessed by the particle swarm in its initial state is used The position of the particle swarm over the entire domain is represented by... The calculation of particle swarm fitness is defined as follows: The optimal position of an individual particle in a particle swarm is used The optimal position of the particle swarm is represented by... express.

[0145] S2. Initialize the simulated annealing algorithm by setting the initial temperature T and obtaining the initial solution. ,in, For inertial weights, For individual cognitive factors, It is a social learning factor.

[0146] S3. Substitute the initial solution into the particle swarm algorithm to update the particle velocity and position using the velocity and orientation information of the individual particle limit values ​​and the global particle limit values.

[0147] The initial solution Substituting the velocity and orientation information of the individual particle limit values ​​and the global particle limit values ​​into the particle swarm optimization algorithm, the particle velocity and position are updated. The specific formula is as follows:

[0148]

[0149]

[0150] in, The number is a random number between 0 and 1, and t is the number of iterations. Let be the current velocity of the i-th particle in the d-dimensional search space at the t-th iteration. Let be the updated velocity of the i-th particle in the d-dimensional search space at the (t+1)-th iteration. Let be the optimal position of the i-th particle in the t-th iteration. Let be the optimal position of the particle swarm at the t-th iteration. Let be the current position of the i-th particle in the d-dimensional search space at the t-th iteration. This represents the current position of the i-th particle in the d-dimensional search space at the (t+1)-th iteration.

[0151] Individual cognitive factors Social learning factors The dynamic learning factor, which is related to the number of iterations, is expressed as follows:

[0152]

[0153]

[0154] in, The initial value for the individual cognitive factor is 2.5. This is the termination value for the individual cognitive factor, taking a value of 1.0. The initial value for the social learning factor is 2.5. is the termination value of the social learning factor, which takes the value of 1.0, and k is the current iteration number.

[0155] Inertial weight Substituting this into the context of a single particle swarm, the formula becomes:

[0156]

[0157]

[0158] The particle velocity and position are updated based on the above formula.

[0159] S4. Based on the updated particle velocity and position, perform particle swarm adaptation. calculate.

[0160] Particle Swarm Optimization The formula for calculation is:

[0161]

[0162] in, This represents the equipment operation combination scheme corresponding to the i-th particle; for Total energy consumption of the cold end system under the scheme (unit: ); for Synergistic benefits and energy savings under the plan (unit: ); for Power generation of the unit under the plan (unit: kW); The constraint deviation function; Only when the scheme violates the constraints ( When the value is greater than 0, a penalty is applied; when the constraint is satisfied, the penalty term is 0. for The coupling strength of key equipment pairs (such as water pump-fan, condenser-circulating water) under the scheme reflects the degree of equipment coordination. The maximum possible coupling strength between devices (taken as 1.5-2.0) is used to normalize the coupling strength index. The values ​​are 0.6-0.8, 0.1-0.3, and 0.05-0.1.

[0163] S5. Based on the calculated particle swarm fitness for the optimal position of individual particles and the optimal position of the swarm as a whole, obtain the evaluation function and the new solution.

[0164] Based on particle swarm fitness The calculation results are used to determine the optimal position of each individual particle. The optimal position of the particle swarm Update the function to obtain the evaluation function. This leads to a new solution. ,in, For the updated inertia weights, For the updated individual cognitive factors, This is the updated social learning factor. Evaluation function. It is the core basis for judging the merits of equipment operation combination schemes during the optimization process. By quantifying performance, balancing multiple objectives, and considering constraints, it guides the evolution of particle swarms to find the optimal operating state instructions and collaborative control strategies. Based on particle swarm fitness The selected "optimal fit value" is the core input of the evaluation function.

[0165] S6. Determine whether the iteration termination condition has been met. If yes, output the current optimal solution. If no, substitute the new solution into the velocity and orientation information of the particle swarm algorithm based on the individual particle limit values ​​and the global particle limit values ​​to update the particle velocity and position, and return to step S4.

[0166] Based on the new solution, continue substituting and solving to obtain a new fitness score. This leads to a new evaluation function. .calculate ,if <0, then , Update the speed and location of the solution. .in, This is the temperature decay coefficient, with a value ranging from 0.8 to 0.95. If it satisfies... If it is greater than rand(0,1), then , It will still accept the speed and location of its updates. If not satisfied If S < 0, then S remains unchanged, and the S value is still used to substitute and solve the fitness problem.

[0167] If the current iteration count has reached the maximum iteration count If the current optimal solution is reached, then the calculation stops. If the current iteration count has not reached the maximum iteration count... Then, the new solution is substituted into the particle swarm algorithm to update the particle velocity and position using the velocity and orientation information of the individual particle limit values ​​and the global particle limit values, and the algorithm returns to step S4.

[0168] Step 103: Control the cold end system of the gas combined cycle unit according to the optimal operating state command and the cooperative control strategy.

[0169] It should be noted that, based on the optimal operating states (start-stop commands) and collaborative control strategies of the circulating water pumps, fans and upper tower gates obtained by solving the multi-objective collaborative optimization mathematical model of the cold end system, the corresponding equipment is started, stopped or adjusted according to the collaborative timing sequence to achieve collaborative operation of multiple equipment.

[0170] In one embodiment, after step 103, the method further includes:

[0171] Step 104: Collect real-time operating data of the cold end system of the gas combined cycle unit.

[0172] Step 105: Calculate the actual unit power generation energy consumption or collaborative efficiency factor of the cold end system of the gas combined cycle unit based on real-time operating data.

[0173] Step 106: If the deviation between the actual unit power generation energy consumption and the optimal solution of the multi-objective collaborative optimization mathematical model of the cold-end system obtained by using the preset algorithm is greater than the first threshold, or the collaborative efficiency factor is lower than the second threshold, then adjust the initialization parameters and collaborative control strategy of the preset algorithm, re-solve the multi-objective collaborative optimization mathematical model of the cold-end system, and output the optimal operating state command and collaborative control strategy. In one embodiment, the first threshold is 5%, and the second threshold is 0.85.

[0174] The system collects real-time data at a frequency of 1Hz on the operating status, power parameters, circulating water inlet and outlet temperatures, condenser vacuum, unit gas input heat, and heat dissipation losses of the circulating water pumps, fans, and upper tower gates. Simultaneously, it collects the coordinated operating status parameters between the equipment. Based on the real-time operating data, the system calculates the actual unit power generation energy consumption or coordinated efficiency factor of the cold-end system of the gas-fired combined cycle unit. If the deviation between the actual unit power generation energy consumption and the optimal solution of the multi-objective coordinated optimization mathematical model of the cold-end system obtained using a preset algorithm exceeds 5%, or if the coordinated efficiency factor is lower than 0.85, the initialization parameters and coordinated control strategy of the preset algorithm are adjusted, the multi-objective coordinated optimization mathematical model of the cold-end system is re-solved, and the optimal operating status command and coordinated control strategy are output to achieve dynamic optimization closed-loop control.

[0175] The present invention provides a method for operating and controlling the cold-end system of a gas-fired combined cycle unit. With the goal of minimizing the unit power generation of the cold-end system, a multi-objective collaborative optimization mathematical model of the cold-end system considering the coupling strength between equipment is constructed. The model uses equipment operation constraints, process index constraints, safety constraints, and multi-equipment collaborative constraints as boundary conditions. A pre-set algorithm is used to solve the multi-objective collaborative optimization mathematical model of the cold-end system, outputting optimal operating state commands and collaborative control strategies. The cold-end system of the gas-fired combined cycle unit is controlled according to these optimal operating state commands and collaborative control strategies. This avoids the problem of energy consumption in other aspects surging in pursuit of a single optimal index. It solves the technical problem that existing operating and control methods for the cold-end system of gas-fired combined cycle units only focus on maximizing condenser vacuum, making it difficult to achieve optimal overall energy efficiency and minimum operating costs for the entire cold-end system and even the entire unit.

[0176] Meanwhile, the gas-fired combined cycle unit cold-end system operation control method provided by this invention uses an improved particle swarm optimization-simulated annealing algorithm to solve the multi-objective collaborative optimization mathematical model of the cold-end system, outputting the optimal operating state command and collaborative control strategy. It integrates the powerful global search capability of the improved particle swarm optimization algorithm and the good local optimization and escape capabilities of the simulated annealing algorithm, effectively solving the problems of single algorithms being prone to getting trapped in local optima, slow convergence speed, or insufficient optimization accuracy. When dealing with the complex dynamic characteristics and multivariate coupling relationships of the cold-end system, it exhibits better global optimization capability and convergence speed, enabling the system to maintain high control accuracy and good robustness under variable operating conditions such as unit load fluctuations, ambient temperature changes, and water quality differences, avoiding the defects of traditional PID control or single algorithms in response lag and accuracy reduction during operating condition switching.

[0177] The gas-fired combined cycle unit cold-end system operation control method provided by this invention places safety constraints in an important position during the model construction and optimization process. For example, key safety indicators such as the lower limit of circulating water flow and the upper and lower limits of vacuum are strictly incorporated into the constraint conditions. The hybrid optimization algorithm strictly adheres to these safety boundaries during the optimization process, ensuring the safety and stability of the cold-end system and even the entire gas-fired combined cycle unit under various operating conditions. This avoids serious consequences such as equipment damage or unplanned unit shutdown that may result from neglecting safety factors or failing to detect faults in a timely manner during the optimization process.

[0178] For easier understanding, please refer to Figure 2 This invention provides an embodiment of an operation control system for the cold end system of a gas combined cycle unit, comprising:

[0179] The multi-objective model construction module is used to construct a multi-objective collaborative optimization mathematical model of the cold-end system with the goal of minimizing the unit power generation of the cold-end system. The model considers the coupling strength between equipment. The multi-objective collaborative optimization mathematical model of the cold-end system includes objective functions and constraints. The constraints include equipment operation constraints, process index constraints, safety constraints, and multi-equipment collaborative constraints.

[0180] The solver module is used to solve the multi-objective collaborative optimization mathematical model of the cold-end system using a preset algorithm, and output the optimal operating state command and collaborative control strategy.

[0181] The operation control module is used to control the cold end system of the gas combined cycle unit according to the optimal operating state command and the cooperative control strategy.

[0182] In one embodiment, the objective function is:

[0183]

[0184]

[0185]

[0186] Where f represents the energy consumption per unit of power generation in the cold-end system. This represents the total energy consumption of the cold-end system. The energy savings corresponding to the synergistic benefits For the unit's power generation, A represents one type of equipment to be evaluated, and B represents another type of equipment to be evaluated. The coupling strength between devices of types A and B. For Class A equipment, For Class B equipment, The correlation coefficient between Class A and Class B equipment. This is the coefficient for switching collaborative strategies. The current unit load, This represents the unit load at the previous moment. The ambient temperature at the current moment. The ambient temperature at the previous moment. This refers to the rated load or reference load of the unit (unit: MW).

[0187] In one embodiment, process parameter constraints include circulating water temperature rise constraints, condenser vacuum constraints, and circulating water inlet temperature constraints.

[0188] The circulating water temperature rise constraint is:

[0189]

[0190] The vacuum constraint of the condenser is:

[0191]

[0192] The circulating water inlet temperature constraint is:

[0193]

[0194] in, The inlet temperature of the circulating water. The outlet temperature of the circulating water. To minimize the allowable temperature rise, For the maximum allowable temperature rise, To the minimum permissible vacuum level, For condenser vacuum, This is the maximum permissible inlet water temperature.

[0195] In one embodiment, the equipment operation constraints include constraints on the number of circulating water pumps in operation, matching constraints between fans and water pumps, and matching constraints between the upper tower gate and water pumps.

[0196] The constraint on the number of circulating water pumps in operation is:

[0197]

[0198] in, To minimize the number of units in operation, The maximum number of operating pumps is n, where n is the total number of circulating water pumps in operation. This represents the operating status of the i-th circulating water pump.

[0199] The matching constraints between the fan and the water pump are:

[0200]

[0201] in, The number of large pumps in operation. Here, j represents the number of small pumps in operation, j is the serial number identifier for the fan (starting from 1) used to distinguish different mechanical tower fans, and m is the total number of mechanical tower fans in the cold end system. The operating status of the j-th fan;

[0202] The matching constraints between the upper tower gate and the water pump are:

[0203]

[0204] Where k is the serial number identifier of the upper tower gate (starting from 1), used to distinguish different upper tower gates in the cold end system; p is the total number of upper tower gates in the cold end system; i is the serial number identifier of the circulating water pump (starting from 1), used to distinguish different circulating water pumps in the cold end system; and n is the total number of circulating water pumps in the cold end system. The value is 0 or 1, which represents the operating status of the k-th tower gate. 1 indicates that the tower gate is in the open state, and 0 indicates that the tower gate is in the closed state.

[0205] In one embodiment, security constraints include equipment backup constraints and equipment switching interval constraints;

[0206] Equipment standby constraints are:

[0207]

[0208] in, The main pump is in standby mode. This refers to the lubricating oil pressure of the large pump (unit: MPa). The bearing temperature of the large pump (unit: °C);

[0209] The equipment switching interval constraint is:

[0210]

[0211] in, The interval between two start-ups and shutdowns of the same device.

[0212] In one embodiment, the preset algorithm is an improved particle swarm-simulated annealing algorithm.

[0213] In one embodiment, a pre-defined algorithm is used to solve the multi-objective collaborative optimization mathematical model of the cold-end system, outputting optimal operating state instructions and collaborative control strategies, including:

[0214] S1. Initialize the particle swarm algorithm by configuring the particle swarm population size, initial state, and fitness.

[0215] S2. Initialize the simulated annealing algorithm by setting the initial temperature and obtaining the initial solution;

[0216] S3. Substitute the initial solution into the particle swarm algorithm to update the particle velocity and position using the velocity and orientation information of the individual particle limit values ​​and the global particle limit values.

[0217] S4. Calculate particle swarm fitness based on the updated particle velocity and position;

[0218] S5. Based on the calculated particle swarm fitness for the optimal positions of individual particles and the optimal positions of the swarm population, the evaluation function and new solution are obtained.

[0219] S6. Determine whether the iteration termination condition has been met. If yes, output the current optimal solution. If no, substitute the new solution into the velocity and orientation information of the particle swarm algorithm based on the individual particle limit values ​​and the global particle limit values ​​to update the particle velocity and position, and return to step S4.

[0220] In one embodiment, it also includes:

[0221] Collect real-time operating data of the cold-end system of the gas-fired combined cycle unit;

[0222] Calculate the actual unit power generation energy consumption or synergistic efficiency factor of the cold end system of the gas combined cycle unit based on real-time operating data.

[0223] If the actual unit power generation energy consumption deviates from the optimal solution of the multi-objective collaborative optimization mathematical model of the cold end system obtained by using the preset algorithm by a greater than the first threshold, or the collaborative efficiency factor is lower than the second threshold, then the initialization parameters and collaborative control strategy of the preset algorithm are adjusted, the multi-objective collaborative optimization mathematical model of the cold end system is solved again, and the optimal operating state command and collaborative control strategy are output.

[0224] In one embodiment, the first threshold is 5% and the second threshold is 0.85.

[0225] The gas-fired combined cycle unit cold-end system operation control system provided in this invention is used to execute the gas-fired combined cycle unit cold-end system operation control method provided in this invention. Its principle and the technical effects achieved are the same as those of the gas-fired combined cycle unit cold-end system operation control method provided in this invention, and will not be repeated here.

[0226] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0227] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for operating and controlling the cold-end system of a gas-fired combined cycle unit, characterized in that, include: With the goal of minimizing the unit power generation of the cold-end system, a multi-objective collaborative optimization mathematical model for the cold-end system is constructed, taking into account the coupling strength between equipment. The multi-objective collaborative optimization mathematical model for the cold-end system includes objective functions and constraints, including equipment operation constraints, process index constraints, safety constraints, and multi-equipment collaborative constraints. A pre-defined algorithm is used to solve the multi-objective collaborative optimization mathematical model of the cold-end system, and the optimal operating state command and collaborative control strategy are output. The cold-end system of the gas combined cycle unit is controlled according to the optimal operating state command and the cooperative control strategy.

2. The method for operating and controlling the cold-end system of a gas-fired combined cycle unit according to claim 1, characterized in that, The objective function is: Where f represents the energy consumption per unit of power generation in the cold-end system. This represents the total energy consumption of the cold-end system. The energy savings corresponding to the synergistic benefits For the unit's power generation, A represents one type of equipment to be evaluated, and B represents another type of equipment to be evaluated. The coupling strength between devices of types A and B. For Class A equipment, For Class B equipment, The correlation coefficient between Class A and Class B equipment. This is the coefficient for switching collaborative strategies. The current unit load, This represents the unit load at the previous moment. The ambient temperature at the current moment. The ambient temperature at the previous moment. This refers to the rated load or reference load of the unit (unit: MW).

3. The method for operating and controlling the cold-end system of a gas-fired combined cycle unit according to claim 1, characterized in that, Process constraints include circulating water temperature rise constraints, condenser vacuum constraints, and circulating water inlet temperature constraints. The circulating water temperature rise constraint is: The vacuum constraint of the condenser is: The circulating water inlet temperature constraint is: in, The inlet temperature of the circulating water. The outlet temperature of the circulating water. To minimize the allowable temperature rise, For the maximum allowable temperature rise, To the minimum permissible vacuum level, For condenser vacuum, This is the maximum permissible inlet water temperature.

4. The method for operating and controlling the cold-end system of a gas-fired combined cycle unit according to claim 1, characterized in that, The equipment operation constraints include constraints on the number of circulating water pumps in operation, matching constraints between fans and water pumps, and matching constraints between the upper tower gate and water pumps. The constraint on the number of circulating water pumps in operation is: in, To minimize the number of units in operation, The maximum number of operating pumps is n, where n is the total number of circulating water pumps in operation. This represents the operating status of the i-th circulating water pump. The matching constraints between the fan and the water pump are: in, The number of large pumps in operation. Here, j represents the number of small pumps in operation, j is the serial number of the fan used to distinguish different mechanical tower fans, and m is the total number of mechanical tower fans in the cold end system. The operating status of the j-th fan; The matching constraints between the upper tower gate and the water pump are: Where k is the serial number identifier of the upper tower gate, used to distinguish different upper tower gates in the cold end system; p is the total number of upper tower gates in the cold end system; i is the serial number identifier of the circulating water pump, used to distinguish different circulating water pumps in the cold end system; and n is the total number of circulating water pumps in the cold end system. Let be the operating state variable of the k-th tower gate.

5. The method for operating and controlling the cold-end system of a gas-fired combined cycle unit according to claim 1, characterized in that, Safety constraints include equipment redundancy constraints and equipment switching interval constraints; Equipment standby constraints are: in, The main pump is in standby mode. For the lubricating oil pressure of the large pump, This refers to the bearing temperature of the large pump. The equipment switching interval constraint is: in, The interval between two start-ups and shutdowns of the same device.

6. The method for operating and controlling the cold-end system of a gas-fired combined cycle unit according to claim 1, characterized in that, The preset algorithm is an improved particle swarm optimization-simulated annealing algorithm.

7. The method for operating and controlling the cold-end system of a gas-fired combined cycle unit according to claim 6, characterized in that, A pre-defined algorithm is used to solve the multi-objective cooperative optimization mathematical model of the cold-end system, outputting the optimal operating state command and cooperative control strategy, including: S1. Initialize the particle swarm algorithm by configuring the particle swarm population size, initial state, and fitness. S2. Initialize the simulated annealing algorithm by setting the initial temperature and obtaining the initial solution; S3. Substitute the initial solution into the particle swarm algorithm to update the particle velocity and position using the velocity and orientation information of the individual particle limit values ​​and the global particle limit values. S4. Calculate particle swarm fitness based on the updated particle velocity and position; S5. Based on the calculated particle swarm fitness for the optimal positions of individual particles and the optimal positions of the swarm population, the evaluation function and new solution are obtained. S6. Determine whether the iteration termination condition has been met. If yes, output the current optimal solution. If no, substitute the new solution into the velocity and orientation information of the particle swarm algorithm based on the individual particle limit values ​​and the global particle limit values ​​to update the particle velocity and position, and return to step S4.

8. The method for operating and controlling the cold-end system of a gas-fired combined cycle unit according to claim 1, characterized in that, Also includes: Collect real-time operating data of the cold-end system of the gas-fired combined cycle unit; Calculate the actual unit power generation energy consumption or synergistic efficiency factor of the cold end system of the gas combined cycle unit based on real-time operating data. If the actual unit power generation energy consumption deviates from the optimal solution of the multi-objective collaborative optimization mathematical model of the cold end system obtained by using the preset algorithm by a greater than the first threshold, or the collaborative efficiency factor is lower than the second threshold, then the initialization parameters and collaborative control strategy of the preset algorithm are adjusted, the multi-objective collaborative optimization mathematical model of the cold end system is solved again, and the optimal operating state command and collaborative control strategy are output.

9. The method for operating and controlling the cold-end system of a gas-fired combined cycle unit according to claim 8, characterized in that, The first threshold is 5%, and the second threshold is 0.

85.

10. A control system for the cold end system of a gas-fired combined cycle unit, characterized in that, include: The multi-objective model construction module is used to construct a multi-objective collaborative optimization mathematical model of the cold-end system with the goal of minimizing the unit power generation of the cold-end system. The model considers the coupling strength between equipment. The multi-objective collaborative optimization mathematical model of the cold-end system includes objective functions and constraints. The constraints include equipment operation constraints, process index constraints, safety constraints, and multi-equipment collaborative constraints. The solver module is used to solve the multi-objective collaborative optimization mathematical model of the cold-end system using a preset algorithm, and output the optimal operating state command and collaborative control strategy. The operation control module is used to control the cold end system of the gas combined cycle unit according to the optimal operating state command and the cooperative control strategy.

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