Phase modifier auxiliary machine system multi-target optimization control method and device and storage medium

Abstract

The invention discloses a phase modifier auxiliary machine system multi-target optimization control method and device and a storage medium, and belongs to the technical field of phase modifier auxiliary machine system control, and the method comprises the steps: obtaining the current operation data of a phase modifier auxiliary machine system, solving a constructed multi-target optimization model based on the current operation data, and obtaining a preliminary solution; the multi-objective optimization model is constructed based on a plurality of optimization objectives, and the plurality of optimization objectives are determined based on dynamic characteristics of the phase modifier auxiliary engine system; calculating the fitness of the initial solution; and determining an algorithm for solving the multi-objective optimization model according to the fitness of the initial solution, the determined optimization objectives and the dynamic characteristics of the phase modifier auxiliary machine system, and solving the multi-objective optimization model through the algorithm to obtain an optimal solution so as to realize multi-objective optimization control of the phase modifier auxiliary machine system. By analyzing the dynamic characteristics of the phase modifier auxiliary machine system, the optimization target is accurately positioned, and the problems that the optimization target is fuzzy and the optimization effect is limited are solved.

Description

Technical Field

[0001] The present invention relates to a multi-objective optimization control method, device and storage medium for a phase modulator auxiliary machine system, and belongs to the technical field of phase modulator auxiliary machine system control. Background Art

[0002] Phase-shifting auxiliary systems are critical equipment for ensuring stable power system operation. During startup, the external cooling water pumps of these systems are prone to generating large current surges, and the fans may experience resonance during operation. These issues can negatively impact equipment lifespan and stability. Furthermore, various factors such as system voltage fluctuations, motor losses, and temperature rise can also impact performance. These factors can lead to low efficiency and reliability in the phase-shifting auxiliary system. Existing technologies rely primarily on the accumulated experience and expertise of operations and maintenance personnel, leading to subjective decisions about control strategies. This results in ambiguous optimization objectives and limited effectiveness. Summary of the Invention

[0003] The purpose of the present invention is to provide a multi-objective optimization control method, device and storage medium for a phase regulator auxiliary system to solve the problems of fuzzy optimization objectives and limited effects in the prior art.

[0004] To achieve the above objectives, the present invention is implemented by adopting the following technical solutions:

[0005] In a first aspect, the present invention provides a multi-objective optimization control method for a phase regulator auxiliary system, comprising:

[0006] After obtaining the current operating data of the phase condenser auxiliary system, a multi-objective optimization model is constructed based on the current operating data to obtain a preliminary solution. The multi-objective optimization model is constructed based on multiple optimization objectives, and the multiple optimization objectives are all determined based on the dynamic characteristics of the phase condenser auxiliary system;

[0007] Calculate the fitness of the preliminary solution;

[0008] An algorithm for solving the multi-objective optimization model is determined based on the fitness of the preliminary solution, the determined multiple optimization objectives and the dynamic characteristics of the phase condenser auxiliary system. The multi-objective optimization model is solved by the algorithm to obtain the optimal solution, thereby realizing multi-objective optimization control of the phase condenser auxiliary system.

[0009] Furthermore, the operating data includes operating data of multiple subsystems in the phase condenser auxiliary system, the subsystems including a brine treatment system, a lubricating oil system, an external cooling system, a stator cooling water system, and a rotor cooling water system;

[0010] The operating data includes the operating data of the subsystem during the start-up and shutdown process, the operating condition adjustment process and the equipment status switching process. The operating data includes operating parameters and operating status. The operating parameters include pressure, flow, cooling water supply, lubricating fluid supply, fan frequency and starting current. The operating status includes normal operating status, maintenance status, isolation status, constant speed operation and variable speed operation.

[0011] Furthermore, the dynamic characteristics are obtained from the operating data, and the dynamic characteristics include dynamic response characteristics, which are obtained by the following method:

[0012] The start-stop process includes a start-up phase and a stop-down phase, wherein the rate of change of the operating data in the start-up phase is greater than the rate of change of the operating data in the stop-down phase; during the start-up phase, the cooling water supply and the lubricating fluid supply are increased; during the stop-down phase, the cooling water supply and the lubricating fluid supply are reduced; and dynamic response characteristics corresponding to the operating data in the start-up phase and the stop-down phase are obtained from the collected operating data;

[0013] The operating condition adjustment process includes a load change process and a fault handling process. During the load change process, the cooling water supply and lubricating fluid supply vary accordingly with the load change to maintain stable operation of the phase regulator auxiliary system. At this time, other operating data also vary accordingly with the load change. During the fault handling process, the faulty part is isolated and maintained, and the operating data of other parts is not affected. The dynamic response characteristics corresponding to the operating data during the load change process and the fault handling process are obtained from the collected operating data.

[0014] The device state switching process includes a state switching process and a mode conversion process; in the state switching process, both the operating parameters and the operating state change with the state switching; in the mode conversion process, the operating parameters change with the mode conversion; the dynamic response characteristics corresponding to the operating data in the state switching process and the mode conversion process are obtained from the collected operating data;

[0015] The acquisition of dynamic response characteristics is completed through data analysis software.

[0016] Furthermore, the optimization objectives include a startup acceleration maximization objective, a response time minimization objective, a regulation speed maximization objective, a regulation accuracy maximization objective, a stability maximization objective, a reliability maximization objective, a voltage fluctuation minimization objective, a motor loss minimization objective, and a temperature rise minimization objective;

[0017] Among them, the voltage fluctuation in the voltage fluctuation minimization target is the product of the current change rate and the system impedance, the electrode loss in the motor loss minimization target is the sum of copper loss and iron loss, the copper loss is proportional to the square of the current, the iron loss is determined by the system impedance and the temperature rise coefficient, and the temperature rise in the temperature rise minimization target is obtained by dividing the motor loss by the heat dissipation efficiency.

[0018] Furthermore, the multi-objective optimization model is solved by using a weighted sum method or a Pareto optimal solution method;

[0019] Furthermore, when solving the multi-objective optimization model, the following constraints are met:

[0020] Physical constraints are constraints that take into account the physical characteristics and operating limitations of the phase-converter auxiliary system, including upper and lower limits on pressure, flow, and temperature, as well as nonlinear constraints on frequency;

[0021] Safety constraints are constraints that ensure that the auxiliary system of the phase regulator does not exceed the safety range during operation;

[0022] Economic constraints are constraints that take into account the operating costs and maintenance costs of the phase regulator auxiliary system.

[0023] Furthermore, the multi-objective optimization model is expressed as:

[0024] ;

[0025] in, and represents the weight, Indicates energy consumption, Indicates the adjustment time.

[0026] Furthermore, the fitness is calculated by the following formula:

[0027] ;

[0028] Among them, F represents fitness, 、 and is the weight, represents the energy efficiency target fitness, Indicates temperature stability adaptability, represents the dynamic performance fitness;

[0029] The energy efficiency target fitness is calculated by the following formula:

[0030] ;

[0031] in, Indicates the rated total power, Indicates total power;

[0032] The temperature stability adaptability is calculated by the following formula:

[0033] ;

[0034] in, is the allowable temperature deviation, R is the current temperature deviation, is the set temperature reference deviation, max means taking the maximum value, || means taking the absolute value;

[0035] ;

[0036] in, Indicates the actual time it takes for the system to complete the dynamic process, Indicates the ideal time for a set system to complete a dynamic process.

[0037] Furthermore, the algorithms include genetic algorithm, particle swarm algorithm, differential evolution algorithm and simulated annealing algorithm.

[0038] In a second aspect, the present invention provides a multi-objective optimization control device for a phase regulator auxiliary system, comprising:

[0039] The multi-objective optimization model preliminary solution module is configured to: obtain current operating data of the phase condenser auxiliary system, and then solve the constructed multi-objective optimization model based on the current operating data to obtain a preliminary solution, wherein the multi-objective optimization model is constructed based on multiple optimization objectives, and the multiple optimization objectives are all determined based on the dynamic characteristics of the phase condenser auxiliary system;

[0040] The fitness calculation module is configured to: calculate the fitness of the preliminary solution;

[0041] The multi-objective optimization solution and control module is configured to: determine the algorithm for solving the multi-objective optimization model based on the fitness of the preliminary solution, the determined multiple optimization objectives and the dynamic characteristics of the phase-shifting auxiliary system, and solve the multi-objective optimization model through the algorithm to obtain the optimal solution to achieve multi-objective optimization control of the phase-shifting auxiliary system.

[0042] In a third aspect, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the multi-objective optimization control method of the phase regulator auxiliary system described in any one of the first aspects are implemented.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention provides a multi-objective optimization control method, device and storage medium for a phase regulator auxiliary system. By analyzing the dynamic characteristics of the phase regulator auxiliary system, the optimization target is accurately located, the correctness and pertinence of the optimization direction are ensured, and the problems of fuzzy optimization targets and limited optimization effects in the prior art are solved. The multi-objective optimization model comprehensively considers the optimization targets of multiple dimensions, achieves all-round performance improvement, and avoids the phenomenon of neglecting one thing while focusing on another that may be caused by single-objective optimization. The calculation of fitness provides a basis for the selection of solution algorithms and parameter adjustment, ensuring the precise controllability of the optimization process.

[0045] It not only solves the problem of unclear optimization targets and insignificant optimization effects of the phase-shifting auxiliary system in existing technologies, but also achieves an overall leap in system performance through refined modeling, scientific algorithm selection and parameter adjustment, and rigorous simulation verification, providing strong guarantees for the safe and efficient operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of a multi-objective optimization control method for a phase regulator auxiliary system provided by Example 2 of the present invention;

[0047] Figure 2 This is a schematic diagram of the external cooling water pump startup current impact simulated using the method provided in Example 2;

[0048] Figure 3 2 is a schematic diagram of the frequency response of the fan resonance phenomenon simulated using the method provided in Example 2;

[0049] Figure 4 This is a schematic diagram of the changes in voltage fluctuation, motor loss and temperature rise over time after optimization simulated using the method provided in Example 2;

[0050] Figure 5 This is a flow chart of a multi-objective optimization control method for a phase regulator auxiliary system provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0052] Example 1

[0053] like Figure 5 As shown, this embodiment provides a multi-objective optimization control method for a phase-modulating auxiliary system, including:

[0054] After obtaining the current operating data of the phase condenser auxiliary system, a multi-objective optimization model is constructed based on the current operating data to obtain a preliminary solution. The multi-objective optimization model is constructed based on multiple optimization objectives, and the multiple optimization objectives are all determined based on the dynamic characteristics of the phase condenser auxiliary system;

[0055] Calculate the fitness of the preliminary solution;

[0056] An algorithm for solving the multi-objective optimization model is determined based on the fitness of the preliminary solution, the determined multiple optimization objectives and the dynamic characteristics of the phase condenser auxiliary system. The multi-objective optimization model is solved by the algorithm to obtain the optimal solution, thereby realizing multi-objective optimization control of the phase condenser auxiliary system.

[0057] The present invention analyzes the dynamic characteristics of the phase-shifting auxiliary system and accurately locates the optimization target, thereby ensuring the correctness and pertinence of the optimization direction and solving the problems of fuzzy optimization targets and limited optimization effects in the prior art. The multi-objective optimization model comprehensively considers the optimization targets of multiple dimensions, achieves all-round performance improvement, and avoids the phenomenon of neglecting one thing while focusing on another that may be caused by single-objective optimization. The calculation of fitness provides a basis for the selection of solution algorithms and parameter adjustment, ensuring the precise controllability of the optimization process.

[0058] Example 2

[0059] like Figure 1 As shown, this embodiment provides a multi-objective optimization control method for a phase-modulating auxiliary system, comprising the following steps:

[0060] Step S1: Analyze the dynamic characteristics of the phase regulator auxiliary system and determine multiple optimization targets of the phase regulator auxiliary system.

[0061] In this embodiment, an electronic device (e.g., a server or terminal device) operating on the phase-shifting auxiliary system can receive a multi-objective optimization control request from the phase-shifting auxiliary system via a wired or wireless connection. It should be noted that the wireless connection methods mentioned above may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX (World Interoperability for Microwave Access) connections, Zigbee connections, UWB (Ultra Wide Band) connections, and other currently known or future developed wireless connection methods.

[0062] The condenser auxiliary system is a critical component for ensuring the long-term, efficient, and stable operation of thermal power units. It primarily consists of multiple subsystems, including the brine treatment system, lubricating oil system, external cooling system, and stator and rotor cooling water systems. These subsystems each perform their respective functions and collectively maintain the normal operation of the condenser.

[0063] The brine treatment system effectively prevents scaling and corrosion, ensuring the cleanliness and safety of the unit. The lubricating oil system ensures lubrication of all unit components, reducing wear and extending service life. The external cooling system is responsible for reducing unit temperature and improving overall operating efficiency. The stator and rotor cooling water systems ensure the stability and reliability of the unit under high-load operation. The phase-shifting auxiliary system not only significantly improves the automation level and steady-state reliability of the thermal power unit, but also provides necessary heat exchange support during transient operations.

[0064] In this embodiment, the specific implementation of step S1 includes the following steps:

[0065] Step S11: obtaining the dynamic response characteristics of the phase regulator auxiliary system.

[0066] The brine treatment system, lubricating oil system, external cooling system, and stator and rotor cooling water systems involved in the phase regulator auxiliary system have dynamic characteristics during the start-up and shutdown, operating condition adjustment and equipment status switching of the phase regulator.

[0067] The dynamic characteristics during the start-stop process involve the dynamic characteristics of the startup phase and the shutdown phase.

[0068] Startup Phase: During the initial startup phase of a condenser, auxiliary systems must respond quickly to provide the necessary cooling and lubrication to ensure a smooth startup. During this phase, system parameters such as pressure and flow rate experience rapid changes.

[0069] Shutdown Phase: When the condenser is shut down, the auxiliary systems need to gradually reduce the supply of cooling and lubrication to avoid thermal shock or mechanical damage to the condenser. During this phase, system parameters change relatively slowly, but a certain level of monitoring and adjustment capabilities must still be maintained.

[0070] The dynamic characteristics during the operating condition adjustment process involve load changes and fault handling dynamic characteristics.

[0071] Load changes: When the load on the condenser changes, the auxiliary system needs to adjust the cooling and lubrication supply accordingly to maintain stable operation of the condenser. During this process, the system parameters will fluctuate with the increase or decrease of load.

[0072] Troubleshooting: When an auxiliary system malfunctions, prompt action is required to isolate and repair it to prevent further damage to the condenser. System reliability and rapid response are crucial during this process.

[0073] The dynamic characteristics of the device state switching process involve state switching and mode conversion.

[0074] State switching: When switching between different operating states of the condenser auxiliary system, such as switching from normal operation to maintenance, the system parameters and operating state will change significantly. During this process, it is necessary to ensure smooth and safe switching.

[0075] Mode conversion: In some cases, the phase regulator auxiliary system needs to switch between different operating modes, such as switching from fixed speed operation to variable speed operation. During this process, the system control strategy and parameter settings need to be adjusted accordingly.

[0076] Dynamic characteristics primarily refer to the temporal variations in the operating parameters and state of a phase-converter auxiliary system when subjected to external excitation or disturbance. These characteristics are typically described through time-domain and frequency-domain analysis. In time-domain analysis, the primary focus is on the phase-converter auxiliary system's response time, overshoot, and stability. These indicators reflect the system's ability to respond quickly and maintain stability to external excitation. Frequency-domain analysis, on the other hand, focuses on the phase-converter auxiliary system's response to signals of varying frequencies, including both amplitude-frequency and phase-frequency characteristics. This helps understand the system's filtering and transfer performance at different frequencies.

[0077] Sensors can be deployed to rapidly respond and accurately capture the dynamic characteristics of the phase regulator's startup and shutdown, operating condition adjustments, and equipment state transitions. For example, during the startup phase, the dramatic fluctuations in pressure and flow at the moment of startup can be captured in real time. A data acquisition system can then record these parameter changes in real time to ensure data accuracy and integrity. Simultaneously, the collected data is processed and analyzed using analytical software to reveal the dynamic response patterns of the phase regulator's auxiliary systems during startup. Furthermore, multiple repeated tests are conducted to verify the reliability and consistency of the results.

[0078] Dynamic response characteristics focus on describing the specific response behavior of a phase-converter auxiliary system to specific excitations or disturbances. This response behavior is often closely related to the system's internal structure and operating parameters, such as rotor inertia and damping coefficient. When subjected to external excitation, the phase-converter auxiliary system's dynamic response characteristics determine how quickly and smoothly it reaches a new steady-state. For example, in the event of a power system fault, the phase-converter auxiliary system must respond quickly, providing the necessary reactive power support to maintain system voltage stability. This speed and stability of response are crucial to preventing phase-converter auxiliary system failure and safeguarding power supply.

[0079] In specific implementation, this embodiment conducts dynamic characteristic simulation analysis on the starting current impact of the internal and external cooling water pumps and the fan resonance phenomenon in the phase-shifting auxiliary system.

[0080] External cooling water pump starting current surge: The starting current typically reaches a peak at startup and then gradually stabilizes over time. Here, we assume a rated current of 100A for the external cooling water pump and use a soft start factor of 2.5. The simulation results show that the starting current rapidly climbs to a peak of 250A before rapidly decaying back to the rated current. This indicates that the soft start strategy effectively suppresses the starting current surge, reducing the impact on the power grid.

[0081] Fan resonance: Fans may experience resonance at specific frequencies, which can increase equipment wear and affect their service life. In this example, assuming a fan resonance frequency of 30 Hz and a damping ratio of 0.05, the response characteristics within the frequency range of 0 Hz to 60 Hz were analyzed. The simulation results show that the frequency response increases significantly near 30 Hz, indicating that the system is prone to large-scale vibration near the resonance frequency. Therefore, during system operation, it is necessary to avoid approaching the resonance frequency to improve system stability.

[0082] Step S12: Analyze the dynamic behavior of the phase modulator auxiliary system according to the dynamic response characteristics of the phase modulator auxiliary system.

[0083] During the startup and shutdown phases, the condenser auxiliary system must respond quickly to commands and smoothly transition to operation or safe shutdown. During this process, the system's dynamic response characteristics are primarily reflected in startup acceleration, response time, and stability. Real-time monitoring and analysis of startup acceleration, response time, and stability parameters can accurately assess the startup efficiency and stability of the condenser auxiliary system, thereby optimizing startup strategies and reducing energy consumption and wear during startup.

[0084] When adjusting operating conditions, the phase-converter auxiliary system must rapidly adjust output power based on grid load fluctuations to ensure a stable power supply. Its dynamic response characteristics are primarily reflected in adjustment speed, adjustment accuracy, and adaptability. By precisely controlling the operating parameters of the phase-converter auxiliary system, its adaptability to grid load fluctuations can be significantly improved, ensuring the continuity and stability of the power supply.

[0085] During state switching, the phase-converter auxiliary system must smoothly transition between different operating conditions to avoid excessive shock and vibration. The system's dynamic response characteristics are primarily reflected in the smoothness, stability, and fault handling capabilities of the transition process. By optimizing the state switching strategy, energy consumption and losses during the transition can be reduced, improving the overall system efficiency.

[0086] S13, determining multiple optimization objectives of the phase condenser auxiliary system according to the dynamic behavior of the phase condenser auxiliary system.

[0087] Starting acceleration and response time directly impact the agility of the phase-converter auxiliary system. Optimization goals should be to significantly improve starting acceleration and shorten response time, ensuring the system can quickly respond to changes in grid demand and adjust reactive power output in a timely manner, thereby enhancing power system stability.

[0088] Regulation speed and accuracy are key performance indicators for a phase-converter auxiliary system. Optimization goals should focus on improving regulation speed to ensure the system can quickly adjust to the target state. Simultaneously, improving regulation accuracy and reducing reactive power output fluctuations to maintain grid voltage stability should also be considered. Furthermore, the system should have a wide adaptability range to accommodate reactive power demands under varying operating conditions.

[0089] During the transition process, smoothness and stability are crucial. The optimization goal should be to ensure a smooth transition for the phase-converter auxiliary system, avoiding dramatic fluctuations in voltage and reactive power, thereby protecting grid equipment from damage. At the same time, the overall system stability should be improved to ensure normal operation under various disturbances.

[0090] Fault handling capability is a key criterion for measuring the reliability of a phase-converter auxiliary system. The optimization goal should be to enhance the system's fault detection and handling capabilities, ensuring that when a fault occurs, it can be quickly located and action taken to minimize the impact.

[0091] During implementation, optimization objectives can also be defined as improving stability, reliability, and economic efficiency. Stability ensures the stable operation of the phase-converter auxiliary system under various operating conditions, preventing system instability caused by excessive parameter fluctuations. Reliability improves the reliability and fault handling capabilities of the auxiliary system, ensuring prompt isolation and repair measures when a fault occurs. Economic efficiency optimizes the operating costs of the auxiliary system, including energy consumption and maintenance costs, to improve the overall economic efficiency of the system.

[0092] It should be noted that the system's redundancy design can also be strengthened to improve fault tolerance and ensure that the system can still maintain basic functions when key components fail.

[0093] Step S2: establishing a multi-objective optimization model for the phase regulator auxiliary system according to multiple optimization objectives of the phase regulator auxiliary system.

[0094] In this embodiment, the specific implementation of step S2 includes the following steps:

[0095] Step S21: setting constraints according to multiple optimization objectives of the phase regulator auxiliary system.

[0096] Physical, safety, and economic constraints can be set based on actual conditions. Physical constraints consider the physical characteristics and operating limitations of the auxiliary system, such as upper and lower limits for parameters like pressure, flow, and temperature. Safety constraints ensure that the auxiliary system does not exceed safe operating ranges to avoid damage to personnel and equipment. Economic constraints consider economic factors such as system operating costs and maintenance expenses to ensure the optimization solution is economically feasible.

[0097] Step S22: Based on the set constraints, a multi-objective optimization model of the phase regulator auxiliary system is established to construct an objective function.

[0098] Depending on the actual situation, you can construct objective functions such as stability, reliability, and economy, or construct objective functions for startup acceleration, response time, regulation speed, and regulation accuracy. A stability objective function can be defined as a statistic such as the standard deviation or variance of system parameter fluctuations to quantify system stability. A reliability objective function can be defined as an indicator such as the system's failure rate or repair time to quantify system reliability. An economy objective function can be defined as an economic indicator such as the system's operating cost or maintenance expense to quantify the system's economic efficiency.

[0099] Step S23: solving the multi-objective optimization model.

[0100] In practice, multi-objective optimization models can be solved using methods such as weighted sum methods or Pareto optimality. The weighted sum method linearly combines multiple objective functions using weighted coefficients to transform them into a single objective function for solution. However, careful attention must be paid to the proper selection and adjustment of weights. The Pareto optimality method uses methods such as graphs and computer software to identify the Pareto optimal set of solutions to a multi-objective optimization problem, then selects the optimal solution based on actual needs. This approach comprehensively considers the trade-offs between various objectives.

[0101] In specific implementation, this embodiment sets voltage fluctuation, motor loss, and temperature rise as the three optimization objectives in the multi-objective optimization problem, then constructs a multi-objective optimization model and sets nonlinear constraints to avoid fan resonance. The optimization variables include current change rate, system impedance, and temperature rise coefficient. The objective function is defined as follows:

[0102] Voltage fluctuation: determined by the product of the rate of change of current and the system impedance.

[0103] Motor loss: It is the sum of copper loss (proportional to the square of the current) and iron loss (related to the system impedance and temperature rise coefficient).

[0104] Temperature rise: Calculated by dividing motor loss by heat dissipation efficiency, it reflects the system's heat dissipation performance.

[0105] The upper and lower limits of the optimization variables were set between 0 and 10 to ensure the optimal solution was found. To avoid resonance, frequency-dependent nonlinear constraints were set to keep the optimization parameters away from the resonant frequency region. A multi-objective genetic algorithm was used to solve the multi-objective optimization model, searching for the optimal solution through multiple iterations.

[0106] During implementation, dynamic characteristics analysis is performed, mathematical models of auxiliary systems (such as cooling water pumps, oil pumps, and fans) are established, and their dynamic response characteristics (such as inertia, delay, and nonlinearity) are analyzed. For example, the relationship between the input (control signal) and output (flow rate, pressure, temperature, etc.) of the phase-converter auxiliary system can be described using transfer functions or state-space equations.

[0107] The optimization objectives include: maximizing energy efficiency - minimizing the total power consumption of the auxiliary system; optimizing stability - ensuring that the operating parameters of the phase regulator (such as temperature and oil pressure) are within the allowable range; and responding speed - shortening the adjustment time of the auxiliary system to load changes.

[0108] Quantify the goal into a mathematical expression (such as an objective function or constraints).

[0109] The weighted sum method, constraint method or Pareto optimal solution can be used to deal with multi-objective conflicts and perform multi-objective optimization modeling. For example: construct a comprehensive objective function :

[0110] ;in and represents the weight, Indicates energy consumption, Indicates the adjustment time.

[0111] Assume that the dynamic characteristics of the phase-converter auxiliary system (such as the cooling water pump) can be simplified to a second-order system:

[0112] ;

[0113] in, represents the dynamic characteristics of the signal s, Indicates the output of the phase regulator auxiliary system, Indicates the input of the phase regulator auxiliary system, represents the system gain, represents the time constant, Indicates flow, pressure or motor speed control signal, represents the damping ratio, Indicates signal The corresponding time constant.

[0114] If the system needs to consider multi-variable coupling (such as the linkage between the oil pump and the cooling system):

[0115] ;

[0116] in, represents the state variables at time t (such as temperature, pressure, flow), represents the control input at time t (such as valve opening, motor power), represents the first multivariate coupling value at time t, represents the second multivariate coupling value at time t, and A, B, C, and D represent system parameter matrices.

[0117] The energy efficiency objective function, i.e. minimizing the total power consumption, is expressed as:

[0118] ;

[0119] in, represents the instantaneous power of the water pump at time t, represents the instantaneous power of the fan at time t, Represents the time constant.

[0120] The stability constraint ensures that the oil pressure p(t) is within the allowable range:

[0121] ;

[0122] Among them, p(t) represents the oil pressure at time t, Indicates the lower limit of oil pressure at time t, Indicates the upper limit of oil pressure. Represents the time constant.

[0123] Step S3: construct a fitness function based on the multi-objective optimization model of the phase regulator auxiliary system.

[0124] In this embodiment, the specific implementation of step S3 includes the following steps:

[0125] Step S31: Determine the evaluation index of the fitness function according to the multi-objective optimization model of the phase regulator auxiliary system.

[0126] The primary consideration should be the stability of the condenser auxiliary system. Any fluctuation in the condenser auxiliary system could impact the power grid. Therefore, stability indicators should cover key parameters such as voltage fluctuation range and frequency deviation to ensure that the condenser auxiliary system maintains stable operation under various operating conditions.

[0127] Economic performance is also essential. Operating costs, maintenance expenses, and energy efficiency are all crucial factors in determining the economic viability of a phase-converter auxiliary system. Incorporating these factors into the fitness function can help the optimization algorithm prioritize cost-effectiveness when searching for the optimal solution, improving the overall economic efficiency of the system.

[0128] Energy efficiency is a key factor in measuring system performance. Efficient energy utilization not only reduces energy consumption but also reduces environmental pollution. Therefore, factors such as energy efficiency ratio and energy loss rate should be used as key indicators in the fitness function to guide optimization algorithms in improving system energy efficiency.

[0129] Based on the optimization goal, the evaluation indicators of the fitness function are determined, such as the system stability index, economic index, energy efficiency index, etc. These indicators are used to measure the performance of the solution generated by the optimization algorithm and provide a standard for evaluating the quality of the solution.

[0130] Step S32: Construct a fitness function based on the evaluation index.

[0131] The system's stability index is key to measuring the ability of a condenser auxiliary system to maintain normal operation under various operating conditions. This stability index can be quantified as a weighted sum of parameters such as the condenser auxiliary system's response time and overshoot under varying load fluctuations. This stability index, as part of the fitness function, ensures that the optimized condenser auxiliary system can quickly recover stability in the face of external disturbances.

[0132] Economic performance measures the operating and maintenance costs of a phase-converter's auxiliary systems. By converting equipment energy consumption, maintenance cycles, and spare parts costs into a single monetary unit and calculating their annual total cost, this metric can be incorporated into the fitness function, encouraging the optimization algorithm to favor more cost-effective designs.

[0133] Energy efficiency is a key indicator of the energy efficiency of a phase-converter auxiliary system. By calculating the overall energy efficiency ratio (EER) of a phase-converter auxiliary system—the ratio of output power to input power—and using it as a key component of the fitness function, we can ensure that the optimized phase-converter auxiliary system maximizes energy utilization while improving performance.

[0134] Based on the determined evaluation indicators, a fitness function is constructed. The fitness function should be able to accurately reflect the performance of the solution and provide a clear search direction for the optimization algorithm.

[0135] In specific implementation, the water pump and fan of the phase-converter cooling system are optimized with the following optimization objectives:

[0136] Minimize total power consumption: ;

[0137] in, Indicates the total power, Indicates the power of the water pump, Indicates the power of the water pump.

[0138] Control the winding temperature within the set upper and lower limits to reduce the temperature adjustment time.

[0139] The design of fitness function can involve several aspects such as normalization target and weighted summation method.

[0140] In the normalized target, the energy efficiency target fitness function is:

[0141] ;

[0142] in, represents the energy efficiency target fitness, Indicates the rated total power, Indicates total power.

[0143] In the normalized objective, the temperature stability fitness function is:

[0144] ;

[0145] in, Indicates temperature stability adaptability, is the allowable temperature deviation, R is the current temperature deviation, is the set temperature reference deviation, max means taking the maximum value, || means taking the absolute value.

[0146] In the normalized objective, the dynamic performance fitness function is:

[0147] ;

[0148] in, represents the dynamic performance fitness, It represents the actual time for the system to complete a dynamic process, such as the transition time in a control system (the response time of the system from the initial state to the stable state), which is dynamically adjusted with environmental changes, load changes, or algorithm iterations; It represents the ideal time for the system to complete the dynamic process, such as the theoretical optimal transition time (such as the expected response time of the control system design). It is used as a normalized benchmark value to convert the actual time Mapped to a dimensionless fitness value.

[0149] The fitness function designed by the weighted summation method is:

[0150] ;

[0151] Among them, F represents fitness, 、 and is the weight, which can be taken =0.5 (energy efficiency first), =0.3 (stability first), =0.2 (response speed priority).

[0152] Step S4: Select an algorithm for solving the multi-objective optimization model of the phase modulation auxiliary system according to the fitness function.

[0153] In this embodiment, the specific implementation of step S4 includes the following steps:

[0154] Step S41 , analyzing the characteristics of genetic algorithm, particle swarm algorithm, differential evolution algorithm and simulated annealing algorithm in solving multi-objective optimization model.

[0155] The genetic algorithm, particle swarm algorithm, differential evolution algorithm and simulated annealing algorithm can be analyzed from multiple dimensions, including search mechanism, global and local search capabilities, convergence speed and sensitivity to parameter settings when solving multi-objective optimization models.

[0156] Genetic algorithms simulate biological evolution, continuously optimizing populations through selection, crossover, and mutation. They have strong global search capabilities, are suitable for multi-objective optimization problems, are highly robust, and are insensitive to the initial population and parameter settings. However, genetic algorithms are computationally intensive and require complex parameter settings.

[0157] The particle swarm algorithm, based on swarm intelligence, seeks the optimal solution through collaboration and information sharing among particles. It has fast convergence and is simple to implement, making it particularly suitable for optimization problems in multidimensional spaces. However, the particle swarm algorithm is prone to getting stuck in local optimal solutions and is sensitive to parameter settings.

[0158] Differential evolution algorithms continuously update population individuals through differential operations, have strong global search capabilities, and converge quickly. However, differential evolution algorithms are also sensitive to parameter settings and are easily affected by noise.

[0159] The simulated annealing algorithm draws inspiration from the physical annealing process, controlling the search process through temperature parameters to effectively avoid being trapped in local optima. It offers strong global search capabilities, high adaptability, and minimal dependence on the initial solution. However, the simulated annealing algorithm suffers from relatively low search efficiency and is sensitive to parameter settings.

[0160] In other words, genetic algorithms have strong global search capabilities and high robustness; particle swarm optimization algorithms converge quickly but are prone to local optima; differential evolution algorithms also converge quickly but are sensitive to parameters; and simulated annealing algorithms can effectively avoid local optima, but their search efficiency needs to be improved. In practical applications, the appropriate algorithm must be selected based on the specific characteristics of the problem.

[0161] Through step S41, the advantages and applicable scopes of different optimization algorithms can be understood, providing a basis for subsequent algorithm selection.

[0162] Step S42: Select one of the genetic algorithm, particle swarm algorithm, differential evolution algorithm and simulated annealing algorithm according to the fitness function, the characteristics of the phase-shifting auxiliary system and the optimization target.

[0163] Select an appropriate optimization algorithm by comprehensively considering factors such as the complexity of the phase-shifting auxiliary system, the diversity of optimization objectives, and the algorithm's computational efficiency and convergence. For example, the particle swarm optimization algorithm (PSO) offers potential advantages in solving multi-objective optimization problems for phase-shifting auxiliary systems due to its simplicity and fast convergence. Therefore, this embodiment selects the PSO as the algorithm for solving the multi-objective optimization model.

[0164] Step S5: solving the multi-objective optimization model of the phase modulator auxiliary system by using a particle swarm algorithm, and adjusting the parameters involved in the multi-objective optimization model of the phase modulator auxiliary system.

[0165] In this embodiment, the specific implementation of step S5 includes the following steps:

[0166] Step S51: Initialize the parameters of the particle swarm algorithm.

[0167] Based on the selected optimization algorithm, the algorithm parameters are initialized, such as population size, number of iterations, crossover probability, mutation probability, etc. These parameters will directly affect the performance and convergence speed of the algorithm. Through step S51, the initial conditions can be provided for the algorithm to ensure its normal operation.

[0168] Step S52: Execute the particle swarm algorithm to solve the multi-objective optimization model according to the initialized algorithm parameters.

[0169] Based on the initialized algorithm parameters, the PSO algorithm is used to solve the multi-objective optimization model. During the iteration process, the PSO algorithm continuously adjusts the solution parameters to optimize the value of the fitness function. Through multiple iterations, the PSO algorithm will generate a set of solutions that meet the optimization objectives.

[0170] In step S52, a solution set that meets the optimization goal is generated through iterative search.

[0171] Step S53: Adjust the particle swarm algorithm parameters and optimize the solution set.

[0172] Based on the PSO's performance and the performance of the solution set, adjust the algorithm parameters, such as increasing the number of iterations, adjusting the crossover probability, and adjusting the mutation probability. By adjusting these parameters, the solution set is further optimized, improving the algorithm's performance and convergence speed. At the same time, the solution set is screened and sorted, retaining the optimal solution as the final control strategy.

[0173] Through step S53, the performance of the particle swarm algorithm and the quality of the solution set can be improved.

[0174] Step S6: Verify the optimization results through simulation, and adjust and improve the optimization strategy.

[0175] In this embodiment, the specific implementation of step S6 includes the following steps:

[0176] Step S61: Evaluate the impact of the optimization strategy on system stability and economy by simulating the actual operating conditions of the phase-shifting auxiliary system.

[0177] Use simulation software or an experimental platform to verify the optimized control strategy. By simulating the actual operating conditions of the phase-shifting auxiliary system, evaluate the impact of the optimization strategy on system stability and economic efficiency. Simultaneously, observe the system's dynamic response characteristics during equipment startup and shutdown, operating condition adjustments, and equipment state transitions. Step S61 verifies the effectiveness and feasibility of the optimization results.

[0178] Step S62: Analyze the simulation results to evaluate the performance and advantages of the optimization strategy.

[0179] Based on the simulation results, analyze the impact of the optimization strategy on system stability, economy, energy efficiency, and other indicators. By comparing the performance differences between traditional control strategies and optimization strategies, evaluate the advantages and limitations of the optimization strategy. Also, analyze the adaptability and robustness of the optimization strategy under different operating conditions. In step S62, conduct an in-depth analysis of the simulation results to evaluate the performance and advantages of the optimization strategy.

[0180] Step S63: Adjust and improve the optimization strategy based on the simulation results and analysis conclusions.

[0181] Based on the simulation results and analysis conclusions, the optimization strategy is adjusted and improved. For example, to address system stability issues, the robustness of the control strategy can be increased; to address poor economic efficiency, control parameters can be optimized to reduce operating costs. Through continuous iteration and optimization, a more comprehensive control strategy is formed. In step S63, the control strategy is further optimized based on the simulation results and analysis conclusions.

[0182] This example uses MATLAB (matrix & laboratory, a mathematical software) to simulate dynamic characteristics such as external cooling water pump startup current surge and fan resonance. A multi-objective optimization model is constructed, and a genetic algorithm is used to determine optimal parameters. This effectively controls voltage fluctuations, motor losses, and temperature rise. Simulation results show that the optimized parameter combination significantly improves system reliability and stability.

[0183] Voltage fluctuation changes over time: The voltage fluctuation is large in the initial stage, but gradually decreases and stabilizes over time, which indicates that the system voltage control effect is good.

[0184] Changes in motor loss over time: After optimization, motor loss shows a gradual downward trend, which helps reduce motor heating and extend system service life.

[0185] Temperature rise changes over time: The system temperature rise gradually decreases, and the optimized cooling efficiency is higher, ensuring that the system operates within a stable temperature range and further improving reliability.

[0186] Figure 2 This is a schematic diagram of the external cooling water pump startup current impact simulated using the multi-objective optimization control method for the phase-shifting auxiliary system provided in this embodiment. Figure 2 As shown in the figure, the current quickly reaches its peak at the initial startup, then gradually decays to a steady state over time. This curve is consistent with the soft start characteristic model, indicating that soft start measures effectively control the startup current surge and reduce the impact on the power grid. This characteristic is crucial to the stability of the phase-converter auxiliary system, ensuring a smooth startup process for the external cooling water pump and reducing electrical stress on the equipment.

[0187] Figure 3 FIG. 1 is a schematic diagram of the frequency response of the fan resonance phenomenon simulated by the multi-objective optimization control method for the phase-shifting auxiliary system provided in this embodiment. Figure 3 The figure shows the resonant response curves of the fan at different frequencies. The frequency response amplitude reaches a peak near 30Hz, exhibiting a significant resonance phenomenon. This phenomenon indicates that the fan will produce significant vibration at this frequency. This resonance is very likely to cause increased wear on the equipment, negatively impacting its service life. Therefore, during the design and operation of the phase-shifting auxiliary system, operating frequencies close to the resonant frequency should be avoided as much as possible to ensure system reliability and stability.

[0188] Figure 4 This is a schematic diagram of the changes in voltage fluctuation, motor loss and temperature rise over time after optimization, simulated using the multi-objective optimization control method for the phase-shifting auxiliary system provided in this embodiment. Figure 4 As shown in the figure, the trend of voltage fluctuation, motor loss and temperature rise over time under the optimized parameter conditions is presented. This figure contains three sub-graphs, which respectively show the time variation curves of these three indicators:

[0189] Voltage fluctuations gradually decreased over time and eventually stabilized. This phenomenon shows that, thanks to the optimized parameter configuration, although the system voltage fluctuated significantly in the initial stage, it was able to stabilize quickly, which fully demonstrates the significant effect of optimizing parameters on improving system voltage stability.

[0190] The motor loss curve shows a decreasing trend over time. This means that after optimization, the motor's losses will gradually decrease during operation. This characteristic helps reduce energy consumption and heat generation, effectively improving system efficiency.

[0191] The temperature rise decreases over time, demonstrating the high cooling efficiency after optimization. Good cooling helps effectively control the motor temperature, allowing the entire system to operate stably within a safe temperature range.

[0192] These simulation results show that the method provided in this embodiment effectively controls voltage fluctuations, motor losses, and temperature rise in the phase-converter auxiliary system. The optimized system exhibits a higher level of stability and reliability during operation.

[0193] Example 3

[0194] Based on the same technical concept as Example 1, this embodiment provides a multi-objective optimization control device for a phase-shifting auxiliary system, comprising:

[0195] The multi-objective optimization model preliminary solution module is configured to: obtain current operating data of the phase condenser auxiliary system, and then solve the constructed multi-objective optimization model based on the current operating data to obtain a preliminary solution, wherein the multi-objective optimization model is constructed based on multiple optimization objectives, and the multiple optimization objectives are all determined based on the dynamic characteristics of the phase condenser auxiliary system;

[0196] The fitness calculation module is configured to: calculate the fitness of the preliminary solution;

[0197] The multi-objective optimization solution and control module is configured to: determine the algorithm for solving the multi-objective optimization model based on the fitness of the preliminary solution, the determined multiple optimization objectives and the dynamic characteristics of the phase-shifting auxiliary system, and solve the multi-objective optimization model through the algorithm to obtain the optimal solution to achieve multi-objective optimization control of the phase-shifting auxiliary system.

[0198] Example 4

[0199] Based on the same technical concept as Example 1, this embodiment provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the multi-objective optimization control method for the phase-modulating auxiliary system provided in Example 1 are implemented:

[0200] After obtaining the current operating data of the phase condenser auxiliary system, a multi-objective optimization model is constructed based on the current operating data to obtain a preliminary solution. The multi-objective optimization model is constructed based on multiple optimization objectives, and the multiple optimization objectives are all determined based on the dynamic characteristics of the phase condenser auxiliary system;

[0201] Calculate the fitness of the preliminary solution;

[0202] An algorithm for solving the multi-objective optimization model is determined based on the fitness of the preliminary solution, the determined multiple optimization objectives and the dynamic characteristics of the phase condenser auxiliary system. The multi-objective optimization model is solved by the algorithm to obtain the optimal solution, thereby realizing multi-objective optimization control of the phase condenser auxiliary system.

[0203] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0204] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0205] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0206] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0207] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A multi-objective optimization control method for a phase-shifting auxiliary system, characterized in that: include: After obtaining the current operating data of the phase condenser auxiliary system, a multi-objective optimization model is constructed based on the current operating data to obtain a preliminary solution. The multi-objective optimization model is constructed based on multiple optimization objectives, and the multiple optimization objectives are all determined based on the dynamic characteristics of the phase condenser auxiliary system; Calculate the fitness of the preliminary solution; An algorithm for solving the multi-objective optimization model is determined based on the fitness of the preliminary solution, the determined multiple optimization objectives and the dynamic characteristics of the phase condenser auxiliary system. The multi-objective optimization model is solved by the algorithm to obtain the optimal solution, thereby realizing multi-objective optimization control of the phase condenser auxiliary system.

2. The multi-objective optimization control method for the phase condenser auxiliary system according to claim 1 is characterized in that: The operating data includes operating data of multiple subsystems in the phase condenser auxiliary system, the subsystems including a brine treatment system, a lubricating oil system, an external cooling system, a stator cooling water system, and a rotor cooling water system; The operating data includes the operating data of the subsystem during the start-up and shutdown process, the operating condition adjustment process and the equipment status switching process. The operating data includes operating parameters and operating status. The operating parameters include pressure, flow, cooling water supply, lubricating fluid supply, fan frequency and starting current. The operating status includes normal operating status, maintenance status, isolation status, constant speed operation and variable speed operation.

3. The multi-objective optimization control method for the phase condenser auxiliary system according to claim 2 is characterized in that: The dynamic characteristics are obtained from the operating data, and the dynamic characteristics include dynamic response characteristics, which are obtained by the following method: The start-stop process includes a start-up phase and a stop phase, wherein the rate of change of the operating data in the start-up phase is greater than the rate of change of the operating data in the stop phase; during the start-up phase, the cooling water supply and the lubricating fluid supply are increased; During the shutdown phase, the cooling water supply and lubricating fluid supply are reduced; the dynamic response characteristics corresponding to the operating data during the startup phase and the shutdown phase are obtained from the collected operating data; The operating condition adjustment process includes a load change process and a fault handling process; During load changes, the cooling water supply and lubricating fluid supply vary accordingly to maintain stable operation of the phase-shifting auxiliary system. Other operating data also vary accordingly. During fault handling, the faulty part is isolated and maintained, leaving the operating data of other parts unaffected. The dynamic response characteristics corresponding to the load change and fault handling processes are obtained from the collected operating data. The device state switching process includes a state switching process and a mode conversion process; in the state switching process, the operating parameters and the operating state change with the state switching; During the mode conversion process, the operating parameters change with the mode conversion; the dynamic response characteristics corresponding to the operating data during the state switching process and the mode conversion process are obtained from the collected operating data; The acquisition of dynamic response characteristics is completed through data analysis software.

4. The multi-objective optimization control method for the phase condenser auxiliary system according to claim 1 is characterized in that: The optimization objectives include a startup acceleration maximization objective, a response time minimization objective, a regulation speed maximization objective, a regulation accuracy maximization objective, a stability maximization objective, a reliability maximization objective, a voltage fluctuation minimization objective, a motor loss minimization objective, and a temperature rise minimization objective; Among them, the voltage fluctuation in the voltage fluctuation minimization target is the product of the current change rate and the system impedance, the electrode loss in the motor loss minimization target is the sum of copper loss and iron loss, the copper loss is proportional to the square of the current, the iron loss is determined by the system impedance and the temperature rise coefficient, and the temperature rise in the temperature rise minimization target is obtained by dividing the motor loss by the heat dissipation efficiency.

5. The multi-objective optimization control method for the phase condenser auxiliary system according to claim 1 is characterized in that: The multi-objective optimization model is solved by using a weighted sum method or a Pareto optimal solution method; Furthermore, when solving the multi-objective optimization model, the following constraints are met: Physical constraints are constraints that take into account the physical characteristics and operating limitations of the phase-converter auxiliary system, including upper and lower limits on pressure, flow, and temperature, as well as nonlinear constraints on frequency; Safety constraints are constraints that ensure that the auxiliary system of the phase regulator does not exceed the safety range during operation; Economic constraints are constraints that take into account the operating costs and maintenance costs of the phase regulator auxiliary system.

6. The multi-objective optimization control method for the phase condenser auxiliary system according to claim 1 is characterized in that: The multi-objective optimization model is expressed as: ； in, and represents the weight, Indicates energy consumption, Indicates the adjustment time.

7. The multi-objective optimization control method for the phase condenser auxiliary system according to claim 1 is characterized in that: The fitness is calculated by the following formula: ； Among them, F represents fitness, 、 and is the weight, represents the energy efficiency target fitness, Indicates temperature stability adaptability, represents the dynamic performance fitness; The energy efficiency target fitness is calculated by the following formula: ； in, Indicates the rated total power, Indicates total power; The temperature stability adaptability is calculated by the following formula: ； in, is the allowable temperature deviation, R is the current temperature deviation, is the set temperature reference deviation, max means taking the maximum value, || means taking the absolute value; ； in, Indicates the actual time it takes for the system to complete the dynamic process, Indicates the ideal time for a set system to complete a dynamic process.

8. The multi-objective optimization control method for the phase condenser auxiliary system according to claim 1 is characterized in that: The algorithms include genetic algorithm, particle swarm algorithm, differential evolution algorithm and simulated annealing algorithm.

9. A multi-objective optimization control device for a phase-shifting auxiliary system, characterized in that: include: The multi-objective optimization model preliminary solution module is configured to: obtain current operating data of the phase condenser auxiliary system, and then solve the constructed multi-objective optimization model based on the current operating data to obtain a preliminary solution, wherein the multi-objective optimization model is constructed based on multiple optimization objectives, and the multiple optimization objectives are all determined based on the dynamic characteristics of the phase condenser auxiliary system; The fitness calculation module is configured to: calculate the fitness of the preliminary solution; The multi-objective optimization solution and control module is configured to: determine the algorithm for solving the multi-objective optimization model based on the fitness of the preliminary solution, the determined multiple optimization objectives and the dynamic characteristics of the phase-shifting auxiliary system, and solve the multi-objective optimization model through the algorithm to obtain the optimal solution to achieve multi-objective optimization control of the phase-shifting auxiliary system.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of the multi-objective optimization control method for a phase regulator auxiliary system according to any one of claims 1 to 8 are implemented.

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