A method and system for optimizing fuel cost per kilowatt-hour based on intelligent algorithms

Abstract

The application discloses a kind of based on intelligent algorithm's degree electric fuel cost optimization method and system, specifically related to intelligent algorithm degree electric fuel cost optimization field, including data acquisition terminal, intelligent algorithm module, actuator and control center;Data acquisition terminal: responsible for real-time acquisition in the key parameters of power generation process, these data provide basic information for subsequent modeling, optimization calculation;Intelligent algorithm module: modeling and optimization calculation are carried out based on the data obtained by data acquisition terminal, it is the core module of realizing degree electric fuel cost optimization, by continuously iterative calculation, provide the basis for adjusting equipment operating state for actuator;The application is by accurately capturing nonlinear relationship and constructing combustion efficiency model containing speed, load rate, ambient temperature, accurately depict the influence of each parameter on fuel cost, compared with traditional single-variable optimization method, degree electric fuel cost reduction range is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent algorithm-based fuel cost optimization technology, and more specifically, to a method and system for optimizing fuel cost per kilowatt-hour based on intelligent algorithms. Background Technology

[0002] In the energy generation sector, the levelized cost of fuel (LCOE) is a core indicator for measuring the economics of power generation. Traditional LCOE optimization methods mainly rely on manual experience to set operating parameters or use fixed models for offline optimization, making it difficult to respond in real time to dynamic changes in fuel characteristics, equipment status, and environmental conditions. Specifically, this manifests in three ways: First, it is difficult to accurately capture the nonlinear coupling relationship between fuel flow and power generation, leading to low fuel utilization. Second, it ignores the impact of parameters such as ambient temperature and equipment speed on combustion efficiency, easily resulting in fuel waste during load fluctuations. Third, it lacks a closed-loop feedback mechanism; when equipment ages or fuel quality changes, the optimization effect significantly decreases, causing large fluctuations in LCOE.

[0003] Therefore, based on the above problems, there is an urgent need to propose a method and system for optimizing fuel cost per kilowatt-hour based on intelligent algorithms. This system can achieve precise control of fuel cost per kilowatt-hour by using intelligent algorithms that can perceive multi-dimensional parameters in real time and dynamically optimize operating strategies. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, the present invention provides a method and system for optimizing fuel cost per kilowatt-hour based on intelligent algorithms, which solves the problems mentioned in the background art through the following solutions.

[0005] To achieve the above objectives, the present invention provides the following technical solution: comprising a data acquisition terminal, an intelligent algorithm module, an actuator, and a control center, wherein the data acquisition terminal, the intelligent algorithm module, and the actuator are all communicatively connected to the control center. The data acquisition terminal is used to collect key parameters during the power generation process in real time. The intelligent algorithm module performs modeling and optimization calculations based on the collected data. The actuator adjusts the equipment operating parameters according to the optimization results. Specifically, the solution includes the following steps:

[0006] S1. High-precision sensors in the data acquisition terminal acquire parameters in real time;

[0007] S2. The intelligent algorithm module initially constructs a mathematical model of fuel cost per kilowatt-hour and introduces a correction coefficient for equipment combustion efficiency.

[0008] S3, the intelligent algorithm module uses an improved particle swarm optimization algorithm to optimize parameters;

[0009] S4, the intelligent algorithm module maps and executes the optimal parameters through the actuator;

[0010] S5: The control center monitors the lowest fuel cost per kilowatt-hour in real time and makes dynamic adjustments through closed-loop feedback.

[0011] Preferably, the parameters include measuring the instantaneous mass flow rate of fuel using a Coriolis mass flow meter, acquiring the power generation P in real time using a power transmitter, measuring the ambient temperature T using a platinum resistance temperature sensor, obtaining the spindle speed n of the equipment using a photoelectric encoder, and obtaining the load rate by calculating the ratio of the power generation to the rated power.

[0012] Preferably, the mathematical model for fuel cost per kilowatt-hour is specifically expressed as follows: Where C represents the fuel cost per kilowatt-hour, and t represents time. Represents the integral variable. The instantaneous mass flow rate of fuel is represented by P, and the power generation is represented by P; the combustion efficiency correction coefficient of the equipment is also mentioned. From rotational speed n, load rate The ambient temperature T was obtained by fitting experimental data, and is specifically expressed as follows: , Indicates the rated speed. Indicates the standard ambient temperature.

[0013] Preferably, the parameter optimization method is as follows: using an improved particle swarm optimization algorithm, with the goal of minimizing C, to search for the optimal parameter combination under the constraint of safe equipment operation. The specific steps are as follows:

[0014] SS1, setting the number of particles N=50, the maximum number of iterations M=100, and the particle position dimension D=3, respectively corresponding to ( The initial positions of the particles are randomly generated within the safe operating range of the equipment. , , initial velocity Setting it to 10% of the location range indicates In the rotational speed n dimension, [-0.04] 0.04 ], at the load factor The dimension is [-0.04, 0.04], and the dimension at ambient temperature T is [-4℃, 4℃].

[0015] SS2. Using the fuel cost per kilowatt-hour (C) as the fitness function, and after introducing combustion efficiency correction, the actual effective fuel flow rate participating in power generation is: The fitness function is specifically expressed as follows: ;

[0016] SS3. In each iteration, the particle velocity is updated according to the following formula: The particle's position is updated according to the following formula: ;in, This represents the velocity of particle i in dimension d at the t-th iteration, where d corresponds to the rotational speed n and the load factor. The three dimensions are: ambient temperature (T). This represents the velocity of particle i in dimension d at the (t+1)th iteration. Indicates inertia weight, , Represents the learning factor. , Represents a random number between [0, 1]. This represents the optimal position of particle i in dimension d at the t-th iteration. Indicates the globally optimal position; This represents the position of particle i in dimension d at the t-th iteration. This represents the position of particle i in dimension d at the (t+1)th iteration;

[0017] SS4. During the iteration process, the particle position must satisfy the device's physical constraints: Rotational speed constraint: n>0.7 And n <= 1.3 Load factor constraints: Ambient temperature constraint: T .

[0018] Preferably, the mapping execution method is as follows: the optimal parameters output by the algorithm ( The lowest fuel cost per kilowatt-hour is obtained by importing the combustion efficiency correction coefficient and fitness function into the equipment. And map the optimal parameters to the equipment execution command: adjust the spindle speed to the specified value via the speed control system. Adjusting power generation through the load distribution system to Adjust the ambient temperature to [temperature] using the air conditioning system. .

[0019] Preferably, the closed-loop feedback includes: uploading the collected parameters in real time with a period of 100ms, performing an optimization iteration every 10 minutes, and triggering an emergency re-optimization mechanism when the fuel calorific value fluctuation exceeds 5% to recalculate the optimal parameters.

[0020] Preferably, a fuel cost optimization system based on intelligent algorithms includes a data acquisition terminal, an intelligent algorithm module, an actuator, and a control center. The data acquisition terminal, intelligent algorithm module, and actuator are all communicatively connected to the control center. The data acquisition terminal is used to collect key parameters during the power generation process in real time. The intelligent algorithm module performs modeling and optimization calculations based on the collected data. The actuator adjusts equipment operating parameters according to the optimization results. Specifically, this includes:

[0021] Data acquisition module: High-precision sensors in the data acquisition terminal acquire parameters in real time;

[0022] Data Analysis Module: The intelligent algorithm module initially constructs a mathematical model of fuel cost per kilowatt-hour and introduces a correction coefficient for equipment combustion efficiency;

[0023] Parameter optimization module: The intelligent algorithm module uses an improved particle swarm optimization algorithm to optimize parameters;

[0024] Optimal Parameter Execution Module: The intelligent algorithm module maps and executes the optimal parameters through the execution mechanism;

[0025] Closed-loop feedback module: The control center monitors the minimum fuel cost per kilowatt-hour in real time and makes dynamic adjustments through closed-loop feedback.

[0026] The technical effects and advantages of this invention are as follows:

[0027] 1. This invention accurately captures nonlinear relationships and constructs a combustion efficiency model that includes speed, load rate, and ambient temperature, precisely characterizing the impact of each parameter on fuel cost. Compared with traditional single-variable optimization methods, the reduction in fuel cost per kilowatt-hour is improved.

[0028] 2. This invention introduces dynamic inertia weights and constraint processing mechanisms through multi-parameter collaborative optimization and the application of an improved particle swarm optimization algorithm. When the equipment load fluctuates, it is faster than the traditional algorithm and shortens the optimization response time.

[0029] 3. By establishing a sound closed-loop feedback mechanism, this invention ensures the system's adaptability to disturbances such as changes in fuel quality and equipment aging, thereby reducing the fluctuation range of fuel cost per kilowatt-hour, which is significantly better than the problem of large fluctuation range of fuel cost per kilowatt-hour in traditional methods. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the device connection according to the present invention;

[0031] Figure 2 This is a schematic diagram of the method structure of the present invention;

[0032] Figure 3 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0034] As attached Figure 1 The method for optimizing fuel cost per kilowatt-hour based on intelligent algorithms includes a data acquisition terminal, an intelligent algorithm module, an actuator, and a control center. The data acquisition terminal, intelligent algorithm module, and actuator are all connected to the control center. The data acquisition terminal is used to collect key parameters in the power generation process in real time. The intelligent algorithm module performs modeling and optimization calculations based on the collected data. The actuator adjusts the equipment operating parameters according to the optimization results.

[0035] Data acquisition terminal: Responsible for collecting key parameters in the power generation process in real time. This data provides the basic information for subsequent modeling and optimization calculations.

[0036] Intelligent algorithm module: Based on the data acquired by the data acquisition terminal, it performs modeling and optimization calculations. It is the core module for optimizing fuel cost per kilowatt-hour. Through continuous iterative calculations, it provides the actuator with the basis for adjusting the operating status of the equipment.

[0037] Actuator: Based on the optimal parameter combination calculated by the intelligent algorithm module, it is converted into specific equipment execution instructions, which are directly applied to the power generation equipment to adjust the equipment's operating status in order to reduce the fuel cost per kilowatt-hour.

[0038] Control Center: The central hub of the entire system. The control center is responsible for coordinating the work between the data acquisition terminal, the intelligent algorithm module, and the actuator. It receives real-time data uploaded by the data acquisition terminal and transmits it to the intelligent algorithm module; it obtains the optimal parameter combination calculated by the intelligent algorithm module, and then sends these parameters to the actuator to instruct the actuator to adjust the equipment. Finally, it establishes a closed-loop mechanism for dynamic adjustment.

[0039] As attached Figure 2 The method for optimizing fuel cost per kilowatt-hour based on intelligent algorithms, as shown, specifically includes the following steps:

[0040] S1. High-precision sensors in the data acquisition terminal acquire parameters in real time;

[0041] In this embodiment, it should be specifically noted that the parameters include the instantaneous fuel mass flow rate measured by a Coriolis mass flow meter. The system acquires the generated power P in real time via a power transmitter, measures the ambient temperature T using a platinum resistance temperature sensor, obtains the spindle speed n using a photoelectric encoder, and calculates the load factor by measuring the ratio of generated power to rated power. .

[0042] S2. The intelligent algorithm module initially constructs a mathematical model of fuel cost per kilowatt-hour and introduces a correction coefficient for equipment combustion efficiency.

[0043] In this embodiment, it should be specifically noted that the mathematical model for fuel cost per kilowatt-hour is specifically expressed as follows: Where C represents the fuel cost per kilowatt-hour, and t represents time. Represents the integral variable. The instantaneous mass flow rate of fuel is represented by P, and the power generation is represented by P; the combustion efficiency correction coefficient of the equipment is also mentioned. From rotational speed n, load rate The ambient temperature T was obtained by fitting experimental data, and is specifically expressed as follows: , Indicates the rated speed. This represents the standard ambient temperature; it's important to understand that the fuel cost per kilowatt-hour (kWh) C represents the quality of fuel consumed per unit of electricity generated, a key indicator for measuring the economics of power generation; integral variable. , representing time, is used for cumulative calculation of fuel flow and power generation over time; the integration upper limit t represents the end point of the time period for calculating cumulative fuel consumption mass and cumulative power generation, with the starting point being 0; equipment combustion efficiency correction coefficient. This is used to measure the difference between the combustion efficiency of equipment under different operating conditions and the ideal state. The value range will vary depending on n. The rated speed changes with the change of T; This is the standard operating speed designed for the equipment, used as a reference value to calculate the impact of rotational speed on combustion efficiency; the equipment combustion efficiency correction coefficient. Data preprocessing is performed before calculation to eliminate the influence of dimensions; a combustion efficiency correction coefficient is introduced. This is because, in actual power generation, the combustion efficiency of the equipment is not constant, but is affected by the rotational speed n and the load factor. And the influence of various factors such as ambient temperature T.

[0044] S3, the intelligent algorithm module uses an improved particle swarm optimization algorithm to optimize parameters;

[0045] In this embodiment, it should be specifically noted that the parameter optimization method is as follows: the intelligent algorithm module adopts an improved particle swarm optimization algorithm, with the goal of minimizing C, to search for the optimal parameter combination under the constraint of safe operation of the equipment. The specific steps are as follows:

[0046] SS1, setting the number of particles N=50, the maximum number of iterations M=100, and the particle position dimension D=3, respectively corresponding to ( The initial positions of the particles are randomly generated within the safe operating range of the equipment. , , initial velocity Setting it to 10% of the location range indicates In the rotational speed n dimension, [-0.04] 0.04 ], at the load factor The dimensions are [-0.04, 0.04], and the dimensions at ambient temperature T are [-4℃, 4℃]. It's important to understand that the number of particles determines the number of initial exploration points in the search space. Setting it to 50 means the algorithm will start searching from 50 different combinations of initial running parameters. A larger number of particles allows the algorithm to cover the solution space more broadly in the early stages of the search, increasing the chance of finding the global optimum. If the number of particles is too small, the algorithm may not be able to fully explore the solution space and is prone to getting stuck in local optima. After multiple experiments and empirical judgment, 50 particles have achieved a good balance between computational efficiency and search performance. The maximum number of iterations limits the algorithm's runtime. In each iteration, particles update their positions according to their velocity and position, and recalculate the fitness function value. Setting it to 100 iterations ensures that the algorithm has enough time to converge to a better solution within this number. The particle position dimension corresponds to the number of variables that need to be optimized. In the optimization of fuel cost per kilowatt-hour, the main variables to be optimized are the spindle speed n and the load rate. And the ambient temperature T, so the dimension D=3. The position of each particle in this three-dimensional space represents a set of equipment operating parameters (n, The algorithm, by continuously adjusting the position of particles in this three-dimensional space, seeks the optimal combination of parameters that minimizes the fuel cost per kilowatt-hour (C). The initial position of the particles is randomly generated within the safe operating range of the equipment, where the rotational speed... Load factor Ambient temperature This random initialization method ensures that particles are evenly distributed in the search space, preventing all particles from starting the search from the same initial point. This increases the likelihood of the algorithm finding the global optimum. These ranges are determined based on the device's safety operating limitations and common working conditions, ensuring that the parameter combinations used in the algorithm's search are feasible in practice. The initial velocity provides an initial setting for the particle's direction of motion and speed at the beginning of the search. The sign of the velocity indicates the particle's direction of motion in the corresponding dimension, and the speed determines the magnitude of the particle's position change in each iteration. Setting it to 10% of the position range allows the particle to have a certain exploration range at the start of the search, but without causing it to jump out of the region where the optimum might exist due to excessive speed.

[0047] SS2. Using the fuel cost per kilowatt-hour (C) as the fitness function, and after introducing combustion efficiency correction, the actual effective fuel flow rate participating in power generation is: The fitness function is specifically expressed as follows: It is important to understand that the fitness function takes into account fuel flow rate. Power generation P and combustion efficiency correction factor These three key parameters. Molecules. The value represents the rate of fuel consumption, and P in the denominator reflects the power output of electricity generation. This demonstrates the impact of combustion efficiency on fuel utilization. Through this combination, the fitness function can comprehensively reflect the fuel cost per kilowatt-hour under different combinations of equipment operating parameters. In the iterative process of the particle swarm optimization algorithm, each particle represents a set of operating parameters, and the corresponding (…) is calculated based on this set of parameters. The algorithm calculates the fitness function value by continuously adjusting the particle's position (i.e., changing the operating parameters), thereby decreasing the fitness function value and ultimately finding the optimal operating parameter combination that minimizes the fuel cost per kilowatt-hour (C), thus optimizing the fuel cost per kilowatt-hour.

[0048] SS3. In each iteration, the particle velocity is updated according to the following formula: The particle's position is updated according to the following formula: ;in, This represents the velocity of particle i in dimension d at the t-th iteration, where d corresponds to the rotational speed n and the load factor. The three dimensions are: ambient temperature (T). This represents the velocity of particle i in dimension d at the (t+1)th iteration. Indicates inertia weight, , Represents the learning factor. , Represents a random number between [0, 1]. This represents the optimal position of particle i in dimension d at the t-th iteration. Indicates the globally optimal position; This represents the position of particle i in dimension d at the t-th iteration. This represents the position of particle i in dimension d at the (t+1)th iteration; it is important to understand that: Its role is to balance the global search and local exploitation capabilities of the algorithm. When When the velocity is large, the particle is more affected by the velocity of the previous moment and tends to search for new regions over a larger area, which is beneficial for global search; when When the particle size is smaller, it focuses more on fine-grained searching of local areas; The step size that controls the particle's movement towards its historical best position. These are random numbers between [0, 1], introducing randomness into the particle's movement direction. This part makes the particle inclined to move closer to the optimal position it has previously reached, reflecting the particle's self-learning ability and helping it to further explore better solutions within its familiar region. This determines the step size by which the particle moves towards the global optimal position. Randomness is also introduced. This allows particles to reference the optimal positions found by other particles in the swarm, giving them social learning capabilities and prompting the entire particle swarm to move towards the global optimum, thus accelerating the algorithm's convergence speed. The velocity update formula calculates the particle's velocity changes in various dimensions, while the position update formula adjusts the particle's position in the search space based on these velocity changes. By continuously iterating and updating velocity and position, the particle moves continuously in the search space, gradually approaching the optimal solution that minimizes the fitness function value. After each iteration, the particle recalculates its fitness function value based on its new position, updating its individual optimal position. and global optimal position This provides a more accurate reference for the next iteration; utilizing the initialization The velocity and position of the particle are updated at each iteration, allowing the particle to move continuously in the search space and gradually approach the optimal solution.

[0049] SS4. During the iteration process, the particle position must satisfy the device's physical constraints: Rotational speed constraint: n>0.7 And n <= 1.3 Load factor constraints: Ambient temperature constraint: T It's important to understand that the speed constraint is set based on the mechanical characteristics and safe operation requirements of the power generation equipment. When the speed is below 0.7... At certain speeds, the equipment may not be able to maintain a stable combustion and power generation state, leading to incomplete combustion, reduced power generation efficiency, and even potential equipment failure; while speeds above 1.3... Excessive rotational speed can cause excessive mechanical stress on the equipment, accelerating wear, shortening its lifespan, and potentially posing safety hazards. During the particle swarm optimization (PSO) algorithm iteration, if the rotational speed (n) of a particle exceeds this range, it needs to be adjusted to the boundary value to ensure that the parameter combination searched by the algorithm is within the equipment's tolerable rotational speed range. When the load factor is less than 0.5, the combustion process of the power generation equipment becomes unstable, leading to incomplete fuel combustion, increasing fuel consumption, and potentially causing environmental pollution. To avoid this, the algorithm stipulates that the particle load factor position must be greater than or equal to 0.5. If the particle load factor during the iteration process... If the ambient temperature is below 0.5, adjust it to 0.5 to ensure the equipment operates under a relatively stable load, improving power generation efficiency and fuel utilization; when the ambient temperature is below When the lubricating oil viscosity increases, the frictional resistance between components increases, which can affect the normal operation of the equipment and may also lead to poor fuel atomization and reduced combustion efficiency; when the ambient temperature is higher than In some cases, heat dissipation difficulties can cause equipment components to overheat, affecting their performance and lifespan. Therefore, the algorithm restricts the ambient temperature and position of particles. If a particle's position exceeds the range, it is adjusted to the boundary value to ensure that the equipment operates under suitable ambient temperature and maintains good power generation performance. These constraint handling mechanisms ensure that the combination of equipment operating parameters searched by the particle swarm optimization algorithm is practically feasible, avoiding the generation of invalid solutions that cannot be applied to actual equipment. By continuously adjusting the particle positions to satisfy the constraints, the algorithm can search for the optimal combination of fuel cost parameters per kilowatt-hour while ensuring the safe and stable operation of the equipment, achieving a balance between the economy and safety of the power generation process.

[0050] S4, the intelligent algorithm module maps and executes the optimal parameters through the actuator;

[0051] In this embodiment, it should be specifically noted that the mapping execution method is as follows: the optimal parameters output by the algorithm ( The lowest fuel cost per kilowatt-hour is obtained by importing the combustion efficiency correction coefficient and fitness function into the equipment. The optimal parameters are mapped to the equipment execution commands: the actuator adjusts the spindle speed to the specified value via the speed control system. Adjusting power generation through the load distribution system to Adjust the ambient temperature to [temperature] using the air conditioning system. It is important to understand that: based on the optimal load factor The load distribution system adjusts the power generation. Since the load factor is calculated as the ratio of generated power to rated power, the actual power generation that needs to be adjusted is... The theoretically optimal parameters obtained by the intelligent algorithm module are transformed into key operational steps for actual power generation equipment, ensuring that the power generation equipment can be precisely adjusted based on the optimization results, thereby reducing the fuel cost per kilowatt-hour.

[0052] S5: The control center monitors the lowest fuel cost per kilowatt-hour in real time and makes dynamic adjustments through closed-loop feedback.

[0053] In this embodiment, the specific details to be explained are as follows: the dynamic adjustment method is as follows: the data acquisition terminal uploads parameters to the control center in real time with a period of 100ms; the intelligent algorithm module performs an optimization iteration every 10 minutes; when the fuel calorific value fluctuation is detected to exceed 5%, an emergency re-optimization mechanism is triggered to recalculate the optimal parameters; simultaneously, the control center establishes a minimum fuel cost threshold per kilowatt-hour. When the lowest fuel cost per kilowatt-hour Still greater than the minimum fuel cost per kilowatt-hour threshold In such cases, an early warning signal is issued to relevant technical personnel. It's important to understand that this data is uploaded to the control center in real time via the communication network, providing the latest information support for subsequent analysis and decision-making. This high-frequency data acquisition can promptly capture subtle changes in parameters during power generation, ensuring the system's accurate perception of its operating status. In each iteration, the algorithm uses the latest data received from the control center to recalculate the fitness function value, update the velocity and position of the particle swarm, and search for new optimal parameter combinations under the constraints of safe equipment operation. Through regular iterations, the system can continuously adjust the equipment's operating parameters based on dynamic changes during power generation, ensuring it always optimizes towards reducing the fuel cost per kilowatt-hour. Significant fluctuations in fuel calorific value directly affect fuel energy release and combustion efficiency, thus altering the fuel cost per kilowatt-hour. Once an emergency re-optimization is triggered, the intelligent algorithm module immediately recalculates, quickly finding new optimal parameter combinations based on the new situation, and promptly adjusting the equipment's operating status to cope with sudden changes, ensuring the stable operation of the power generation system and the control of the fuel cost per kilowatt-hour. The closed-loop feedback and dynamic correction mechanism enables the entire optimization system to form a self-regulating and self-optimizing closed loop. Through real-time data acquisition, regular optimization iterations, and emergency optimization mechanisms, the system can respond promptly to various changes during the power generation process. This dynamic correction ensures that the equipment always operates close to its optimal state, minimizing fuel costs per kilowatt-hour, improving the economy and stability of power generation, and also helping to extend equipment lifespan and reduce equipment failures and maintenance costs.

[0054] Based on the above scheme and appendix Figure 3 This invention also discloses a fuel cost optimization system based on intelligent algorithms, comprising a data acquisition terminal, an intelligent algorithm module, an actuator, and a control center. The data acquisition terminal, intelligent algorithm module, and actuator are all communicatively connected to the control center. The data acquisition terminal is used to collect key parameters during the power generation process in real time. The intelligent algorithm module performs modeling and optimization calculations based on the collected data. The actuator adjusts equipment operating parameters according to the optimization results. Specifically, this includes:

[0055] Data acquisition module: High-precision sensors in the data acquisition terminal acquire parameters in real time;

[0056] Data Analysis Module: The intelligent algorithm module initially constructs a mathematical model of fuel cost per kilowatt-hour and introduces a correction coefficient for equipment combustion efficiency;

[0057] Parameter optimization module: The intelligent algorithm module uses an improved particle swarm optimization algorithm to optimize parameters;

[0058] Optimal Parameter Execution Module: The intelligent algorithm module maps and executes the optimal parameters through the execution mechanism;

[0059] Closed-loop feedback module: The control center monitors the minimum fuel cost per kilowatt-hour in real time and makes dynamic adjustments through closed-loop feedback.

[0060] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0061] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing fuel cost per kilowatt-hour based on intelligent algorithms, characterized in that, It includes a data acquisition terminal, an intelligent algorithm module, an actuator, and a control center. The data acquisition terminal, intelligent algorithm module, and actuator are all communicatively connected to the control center. The data acquisition terminal is used to collect key parameters during the power generation process in real time. The intelligent algorithm module performs modeling and optimization calculations based on the collected data. The actuator adjusts the equipment operating parameters according to the optimization results. Specifically, it includes the following steps: S1. High-precision sensors in the data acquisition terminal acquire parameters in real time; S2. The intelligent algorithm module initially constructs a mathematical model of fuel cost per kilowatt-hour and introduces a correction coefficient for equipment combustion efficiency. The mathematical model for fuel cost per kilowatt-hour is specifically expressed as follows: Where C represents the fuel cost per kilowatt-hour, and t represents time. Represents the integral variable. The instantaneous mass flow rate of fuel is represented by P, and the power generation is represented by P; the combustion efficiency correction coefficient of the equipment is also mentioned. From rotational speed n, load rate The ambient temperature T was obtained by fitting experimental data, and is specifically expressed as follows: , Indicates the rated speed. Indicates standard ambient temperature; S3, the intelligent algorithm module uses an improved particle swarm optimization algorithm to optimize parameters; S4, the intelligent algorithm module maps and executes the optimal parameters through the actuator; S5: The control center monitors the lowest fuel cost per kilowatt-hour in real time and makes dynamic adjustments through closed-loop feedback.

2. The method for optimizing fuel cost per kilowatt-hour based on intelligent algorithms according to claim 1, characterized in that: The parameters include the instantaneous fuel mass flow rate measured using a Coriolis mass flow meter. The system acquires the generated power P in real time via a power transmitter, measures the ambient temperature T using a platinum resistance temperature sensor, obtains the spindle speed n using a photoelectric encoder, and calculates the load factor by measuring the ratio of generated power to rated power. .

3. The method for optimizing fuel cost per kilowatt-hour based on intelligent algorithms according to claim 1, characterized in that: The parameter optimization method is as follows: using an improved particle swarm optimization algorithm, with the goal of minimizing C, to search for the optimal parameter combination under the constraint of safe equipment operation. The specific steps are as follows: SS1, setting the number of particles N=50, the maximum number of iterations M=100, and the particle position dimension D=3, respectively corresponding to ( The initial positions of the particles are randomly generated within the safe operating range of the equipment. , , initial velocity Setting it to 10% of the location range indicates In the rotational speed n dimension, [-0.04] 0.04 ], at the load factor The dimension is [-0.04, 0.04], and the dimension at ambient temperature T is [-4℃, 4℃]. SS2. Using the fuel cost per kilowatt-hour (C) as the fitness function, and after introducing combustion efficiency correction, the actual effective fuel flow rate participating in power generation is: The fitness function is specifically expressed as follows: ; SS3. In each iteration, the particle velocity is updated according to the following formula: The particle's position is updated according to the following formula: ;in, This represents the velocity of particle i in dimension d at the t-th iteration, where d corresponds to the rotational speed n and the load factor. The three dimensions are: ambient temperature (T). This represents the velocity of particle i in dimension d at the (t+1)th iteration. Indicates inertia weight, , Represents the learning factor. , Represents a random number between [0, 1]. This represents the optimal position of particle i in dimension d at the t-th iteration. Indicates the globally optimal position; This represents the position of particle i in dimension d at the t-th iteration. This represents the position of particle i in dimension d at the (t+1)th iteration; SS4. During the iteration process, the particle position must satisfy the device's physical constraints: Rotational speed constraint: n>0.7 And n <= 1.3 Load factor constraints: Ambient temperature constraint: T .

4. The method for optimizing fuel cost per kilowatt-hour based on intelligent algorithms according to claim 1, characterized in that: The mapping execution method is as follows: the optimal parameters output by the algorithm ( The lowest fuel cost per kilowatt-hour is obtained by importing the combustion efficiency correction coefficient and fitness function into the equipment. And map the optimal parameters to the equipment execution command: adjust the spindle speed to the specified value via the speed control system. Adjusting power generation through the load distribution system to Adjust the ambient temperature to [temperature] using the air conditioning system. .

5. The method for optimizing fuel cost per kilowatt-hour based on intelligent algorithms according to claim 1, characterized in that: The dynamic adjustment method is as follows: the data acquisition terminal uploads parameters to the control center in real time with a period of 100ms; the intelligent algorithm module performs an optimization iteration every 10 minutes; when the fuel calorific value fluctuation is detected to exceed 5%, an emergency re-optimization mechanism is triggered to recalculate the optimal parameters; at the same time, the control center establishes a minimum fuel cost threshold per kilowatt-hour. When the lowest fuel cost per kilowatt-hour Still greater than the minimum fuel cost per kilowatt-hour threshold At that time, an early warning signal will be issued to the relevant technical personnel.

6. A fuel cost optimization system based on intelligent algorithms, used to implement the fuel cost optimization method based on intelligent algorithms according to any one of claims 1 to 5, characterized in that, It includes a data acquisition terminal, an intelligent algorithm module, an actuator, and a control center. The data acquisition terminal, intelligent algorithm module, and actuator are all connected to the control center. The data acquisition terminal is used to collect key parameters during the power generation process in real time. The intelligent algorithm module performs modeling and optimization calculations based on the collected data. The actuator adjusts the equipment operating parameters according to the optimization results. Specifically, it includes: Data acquisition module: High-precision sensors in the data acquisition terminal acquire parameters in real time; Data Analysis Module: The intelligent algorithm module initially constructs a mathematical model of fuel cost per kilowatt-hour and introduces a correction coefficient for equipment combustion efficiency; Parameter optimization module: The intelligent algorithm module uses an improved particle swarm optimization algorithm to optimize parameters; Optimal Parameter Execution Module: The intelligent algorithm module maps and executes the optimal parameters through the execution mechanism; Closed-loop feedback module: The control center monitors the minimum fuel cost per kilowatt-hour in real time and makes dynamic adjustments through closed-loop feedback.

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