Variable frequency control method and system for noise of substation building exhaust system based on genetic algorithm

Abstract

This invention provides a variable frequency control method and system for noise control in substation ventilation systems based on genetic algorithms. It employs a distributed sensor network to collect real-time temperature and noise data from the substation, combines it with variable frequency speed control technology, and establishes a multi-objective optimization model using a genetic algorithm. Noise and temperature are used as constraints and optimization objectives in a joint solution. The genetic algorithm dynamically adjusts the operating frequency of the exhaust fans through operations such as population initialization, fitness evaluation, selection, crossover, and mutation, thereby balancing the needs of temperature control and noise minimization. This invention can adaptively adjust the speed of the exhaust fans. Compared with traditional single-unit fixed-speed or simple linkage control methods, this technology can significantly improve the overall energy efficiency of the system, reduce energy waste, and improve the operating environment of the substation. This technology is applicable to noise and temperature control needs in various types of indoor substations and similar scenarios, and has high applicability and promotional value.

Description

Technical Field

[0001] This invention relates to the field of silent ventilation technology for substations, and in particular to a frequency conversion control method and system for noise control of substation ventilation systems based on genetic algorithms. Background Technology

[0002] In modern power systems, substations, as a crucial link in power transmission and distribution, are essential for the reliability of the power grid by ensuring their efficient, stable, and safe operation. With the continuous increase in electricity demand, traditional open-air substations are gradually failing to meet the requirements of safety, environmental protection, and energy conservation, making indoor substations the mainstream choice. Although indoor substations reduce noise and environmental pollution compared to open-air substations, they still face problems such as noise pollution and heat dissipation. Noise pollution in substations mainly comes from transformers and auxiliary equipment, especially the noise from cooling fans and exhaust fans. Due to the limited space and concentrated operation of equipment indoors, the impact of noise is more significant in indoor substations. Therefore, solving noise problems and improving the energy efficiency of fan systems are urgent challenges that need to be addressed in the design and operation of substations. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a variable frequency control method and system for noise control of substation ventilation systems based on genetic algorithms. Combining low-noise exhaust fans and PID variable frequency control technology optimized by genetic algorithms, this method achieves automatic adjustment of fan operating frequency, reduction of substation room temperature and noise, and energy saving. This system not only effectively reduces noise pollution and maintains temperature in substations, but also enables energy efficiency optimization and remote monitoring.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a frequency conversion control method for noise in a substation ventilation system based on a genetic algorithm, comprising the following steps:

[0005] Step 1: Install multiple noise sensors and temperature sensors in the substation room; the noise sensors are used to monitor the noise level of each area in the substation room in real time, and the average value of the monitoring values ​​of all noise sensors is used as the comprehensive index of indoor noise and input to the PLC control module; the temperature sensors are used to monitor the temperature inside and outside the substation room, obtain the temperature difference between indoor and outdoor, and input the temperature difference data into the PLC control module as a reference for temperature and noise control.

[0006] Step 2: The PLC control module transmits the collected noise and temperature data to the host computer via the RS-485 communication protocol. The host computer processes the received data and uses a genetic algorithm to perform calculations and optimization analysis. The monitored temperature and noise data are displayed in real-time on the host computer's monitoring interface. Based on the current temperature threshold constraints and the changing trends of indoor noise and temperature, the genetic algorithm dynamically adjusts the fan's operating frequency, aiming to minimize the noise generated by the fan while ensuring that the indoor temperature does not exceed the set threshold.

[0007] Step 3: After calculation by the genetic algorithm, the optimal speed control strategy generated by the host computer will be sent to the PLC control module through the RS-485 communication protocol; the PLC control module will convert the speed control signal into a current frequency control signal and then transmit it to the frequency converter of each fan; the frequency converter will adjust the speed of each fan according to the current frequency control signal to achieve precise control of noise and temperature.

[0008] Step 4: As the ambient temperature and noise change in real time, the PLC control module continuously transmits the latest data to the host computer. When the temperature approaches the temperature threshold, the genetic algorithm will recalculate the optimization scheme based on the updated data. Through this dynamic adjustment method, the PLC control module and the genetic algorithm work together to ensure that the exhaust fan system always adjusts the fan speed according to the actual conditions inside and outside the station during the entire operation.

[0009] In a preferred embodiment, the optimization of temperature and noise data by the genetic algorithm specifically includes: the relationship between indoor temperature and exhaust fan speed is derived from heat balance, and is expressed as:

[0010]

[0011] There is an approximate logarithmic relationship between noise and exhaust fan speed, expressed as:

[0012] N v =A·lg(n)+B.

[0013] In a preferred embodiment, the optimal speed control strategy specifically includes:

[0014] (1) Initialize the population

[0015] Population representation: Each individual represents several wind turbines and the exhaust fan speed n of each wind turbine, which is represented as an n-dimensional array, where q is the number of wind turbines;

[0016] Population size: Select population size A to represent the current solution space, randomly initialize the n-dimensional array in the population, and ensure that the rotational speed of each wind turbine in the n-dimensional array is within a certain range;

[0017] (2) Fitness function

[0018] When calculating the room temperature under the action of several fans, the room temperature can be calculated using the following formula:

[0019]

[0020] Wherein: T int The room temperature is the result of all fans working together, q is the number of fans, and n is the number of fans. i i = 0, 1, 2, ..., q are the rotational speeds of each fan, k, ρ air v air A room c air Q heat All are empirical constants;

[0021] When calculating the noise level of several wind turbines, the noise level is calculated using the following formula:

[0022]

[0023] Where: N vt The sum of noise levels generated by all fans, where q is the number of fans. i = 0, 1, 2, ..., q represent the noise generated by each fan; combining the formula for noise and speed of a single fan, it can be further rewritten as follows:

[0024]

[0025] Temperature control constraints:

[0026] According to the heat balance model, there is a direct mathematical relationship between fan speed and indoor temperature, namely:

[0027]

[0028] Wherein: T int The room temperature is the result of all fans working together, q is the number of fans, and n is the number of fans. i i = 0, 1, 2, ..., q are the rotational speeds of each fan, k, ρ air v air A room c air Q heat All are empirical constants;

[0029] The fitness function first evaluates whether the indoor temperature exceeds a set threshold under a given fan configuration. If the temperature exceeds the threshold, the fitness value of the solution is zero, indicating that the configuration is invalid; otherwise, the fitness value will be adjusted according to the temperature deviation: the smaller the temperature deviation, the higher the fitness value.

[0030] Noise level is related to the number of fans started and the speed of each fan; the lower the noise level, the higher the adaptability.

[0031]

[0032] Wherein: F temp T represents the fitness value for temperature. int At room temperature, T th The set temperature threshold;

[0033] Noise minimization constraint:

[0034] The following relationship exists between noise and fan speed:

[0035]

[0036] Where: N vt The sum of noise levels generated by all fans, where q is the number of fans and n is the total noise level. i i =

[0037] 0, 1, 2, ..., q represent the rotational speeds of each fan, while A and B are empirical constants.

[0038] fitness function F noise It will adjust according to the noise level; the noise fitness function is as follows:

[0039]

[0040] Wherein: F noise N represents the fitness value for noise. vt The sum of noise levels for all fans is calculated, and α is an adjustment factor used to control the degree of impact of noise on fitness. If α is large, the noise penalty will be more significant, and the fan speed needs to be controlled as low as possible.

[0041] The fitness function is further designed as follows:

[0042]

[0043] Where: N max The set noise upper limit is when the noise N vt If this threshold is exceeded, the fitness value will drop rapidly, indicating that the noise of the configuration is seriously excessive. p is the number of wind turbines, and β is the penalty factor, which is used to adjust the impact of the number of wind turbines on the fitness value. If there are too many wind turbines, the β·p term will reduce the fitness value, thereby forcing the genetic algorithm to select an appropriate number of wind turbines.

[0044] (3) Select operation

[0045] The selection operation is used to select individuals with higher fitness from the current population as parent individuals; the selection method can be roulette wheel selection or tournament selection, which can select better individuals based on their fitness.

[0046] (4) Cross operation

[0047] Crossover is used to exchange the genes (i.e., the rotational speed values ​​of each wind turbine) of two parent individuals to generate new offspring individuals; common crossover methods include single-point crossover and uniform crossover.

[0048] (5) Mutation operation

[0049] The mutation operation is used to randomly fine-tune the rotation speed of some individuals in order to maintain the diversity of the population and prevent it from getting trapped in local optima; the mutation amplitude is set to a certain range.

[0050] (6) Update the population

[0051] After crossover and mutation operations, a new population is generated; the choice is made to either retain the parent and offspring by merging them, or to cull individuals with poor fitness.

[0052] (7) Termination Conditions

[0053] Set a maximum number of iterations, or stop the algorithm when the fitness reaches a preset threshold; the final output is the optimal solution, which is the optimal speed of each row of fans.

[0054] In a preferred embodiment, assuming steady-state conditions, the relationship between the station's temperature and the heat source and exhaust volume is derived from the following heat conservation equation:

[0055] Q heat +Q inflow =Q outflow

[0056] Q inflow It is the heat entering the station building, Q outflow The heat is discharged through the exhaust fan;

[0057] The relationship between air velocity and the temperature difference between indoors and outdoors when air enters a room is represented by heat transfer; the amount of heat Q entering the room... inflow It is mainly determined by the following factors:

[0058] Q inflow =m·c air ·ΔT

[0059] Where m is the air mass flow rate, in kg / s, calculated from the air velocity and room volume:

[0060] m = ρ air ·v air·A room

[0061] Where A room =L×W is the floor area of ​​the station building;

[0062] The exhaust volume of an exhaust fan is directly proportional to its rotational speed. Therefore, the exhaust volume of one exhaust fan in the station building is:

[0063] Q outflow =Q fan =k·n

[0064] The heat Q removed by the exhaust volume outflow Determined by air mass flow rate and temperature difference:

[0065] Q outflow =m·c air ·(T in -T out ).

[0066] In a preferred embodiment, the heat balance yields:

[0067] Q heat +m·c air ·ΔT=k·v fan ·c air ·(T in -T out )

[0068] Under steady-state conditions, the indoor temperature T in Determined by the following equation:

[0069] Q heat +ρ air ·v air ·A room ·c air ·ΔT

[0070] =k·v fan ·ρ air ·v air ·A room ·c air ·(T in -T out )

[0071] Simplifying the relevant constant terms, we can obtain the relationship between the exhaust fan speed and the indoor temperature:

[0072]

[0073] When the temperature in the substation room is too high, the PLC controls the fan speed to increase.

[0074] Q heat +Q inflow <Qoutflow

[0075] As the temperature of the exhaust gas increases, the indoor temperature decreases, causing ΔT to increase and Q to decrease. inflow Increase the value until both sides of the formula are equal, reaching equilibrium again.

[0076] The present invention also provides a frequency conversion control system for noise of a substation exhaust system based on a genetic algorithm, and a frequency conversion control method for noise of a substation exhaust system based on a genetic algorithm; including a noise monitoring module, a temperature monitoring module, a PLC control module, a rectifier module, an energy consumption module, and an inverter module.

[0077] In a preferred embodiment, the noise monitoring module specifically includes a noise sensor that monitors the noise level in the environment, in decibels (dB).

[0078] In a preferred embodiment, the temperature monitoring module specifically includes a temperature sensor that monitors the temperature of the environment or object and converts the temperature data into a digital signal.

[0079] In a preferred embodiment, the PLC control module consists of a digital input / output module, an analog input module, and an analog output module of a PLC programmable controller.

[0080] In a preferred embodiment, the rectifier module adopts a three-phase bridge rectifier circuit, the switching element is a diode, and the filter capacitor Cf is used to filter out the DC side voltage ripple of the rectifier circuit and stabilize the output voltage.

[0081] Compared with existing technologies, this invention has the following advantages: This patent proposes an intelligent substation room silent exhaust fan system, successfully integrating low-noise design, genetic algorithm optimization, and frequency conversion control technology, comprehensively solving the problems of noise pollution, heat dissipation, and energy efficiency optimization faced by indoor substations during operation. Through real-time monitoring of noise and temperature, precise control of fan speed, and implementation of dynamic optimization strategies, this system can significantly reduce indoor noise levels and energy consumption, improving the operational reliability and environmental performance of the substation. The modular and intelligent characteristics of the system design give it good scalability, making it suitable for indoor substations of different sizes and needs. Furthermore, by combining PLC and genetic algorithm for collaborative optimization, this system further optimizes noise levels while ensuring temperature control targets, demonstrating outstanding technical advantages and application value. Attached Figure Description

[0082] Figure 1 This is a system structure diagram of a preferred embodiment of the present invention;

[0083] Figure 2 This is a schematic diagram of the fan control process according to a preferred embodiment of the present invention;

[0084] Figure 3 This is a schematic diagram of the noise versus speed curves obtained from the literature.

[0085] Figure 4 This is a schematic diagram of the speed-temperature / noise curve of a preferred embodiment of the present invention;

[0086] Figure 5 This is a flowchart of the algorithm of a preferred embodiment of the present invention. Detailed Implementation

[0087] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0088] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0089] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations according to this application; as used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise; furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0090] A variable frequency control system for noise in a substation ventilation system based on a genetic algorithm, referenced. Figure 1 The automated frequency conversion control system of the intelligent substation silent exhaust fan system adopts a modular design, including a noise monitoring module, a temperature monitoring module, a PLC control module, a rectifier module, an energy consumption module, and an inverter module. Its basic structure is as follows: Figure 1 As shown. In system design, the study of frequency converters is crucial. Variable frequency speed control is generally divided into AC-AC conversion and AC-DC-AC conversion, with the latter being the most widely used in practice, offering advantages such as lower harmonic content and adjustable stator and rotor power factors. The following are detailed descriptions of each module:

[0091] 1. Noise monitoring module

[0092] The function of noise sensors is to monitor and measure the noise level in the environment, measured in decibels (dB). They can sense sound intensity, identify the source of noise pollution, and transmit this data to the control system or equipment for further analysis and processing. Noise sensors are installed at the four corners of the station building to monitor noise levels. The average value of the four sensor readings is taken as the indoor noise input to the PLC control module.

[0093] 2. Temperature monitoring module

[0094] Temperature sensors are used to monitor and measure the temperature of the environment or objects in real time, and the temperature data is converted into electrical or digital signals for further processing and analysis by the control system or equipment. Four temperature sensors are placed in the four corners of the station building to monitor the station building temperature, and one temperature sensor is placed outdoors to monitor the outdoor temperature. The average value of the four indoor temperature sensors is taken as the indoor temperature, and the reading of the outdoor temperature sensor is taken as the outdoor temperature. The difference between the indoor and outdoor temperatures is the temperature difference input to the PLC control module.

[0095] 3. PLC control module

[0096] Figure 1 The PLC control module is the core of the variable frequency automation control of the exhaust fan. The control system mainly consists of digital input / output modules and analog input / output modules of the PLC programmable controller. Its main functions include comparing and judging temperature and noise, fault diagnosis, and output control. It can also transmit information remotely for centralized monitoring.

[0097] 4. Rectifier Module

[0098] The rectifier module employs a three-phase bridge rectifier circuit, using diodes as switching elements. A filter capacitor Cf is used to filter out DC-side voltage ripple in the rectifier circuit and stabilize the output voltage. Furthermore, to prevent overcharging current from the filter capacitor during the instantaneous start-up and shutdown of the inverter, a current-limiting resistor Rm is connected in series on the DC side of the rectifier circuit. This also helps to reduce DC-side voltage fluctuations to some extent.

[0099] 5. Energy Consumption Module

[0100] Because the motor enters a generating state when the operating frequency decreases and feeds electrical energy back to the DC side of the rectifier module, the voltage Ud across the terminals increases, severely impacting the operation of electrical components and circuits. To avoid this, an energy dissipation module is added between the inverter and rectifier modules. This module uses internal resistors to dissipate the electrical energy fed back to the DC side, ensuring safe system operation and normal functioning of switching devices.

[0101] 6. Inverter Module

[0102] The inverter module of the frequency converter uses IGBTs as switching devices. Since the wind turbine contains inductive components, reactive power will be generated in the current. Therefore, diodes are connected in anti-parallel across each IGBT for reactive current feedback, and simultaneously provide a path for the wind turbine to feed power back to the DC side.

[0103] 7. Host computer module

[0104] The host computer (HPC) is a computer system in an automated control system that interacts with and controls the slave computer (such as a PLC, embedded device, or other controller). The HPC is responsible for processing the data received from the slave computer, using genetic algorithms to calculate the optimal operating frequency of the fan under specific temperature conditions to minimize noise. The HPC can send control commands to the slave computer based on user input or preset control logic, and also provides a suitable user interface for real-time display and monitoring of noise and temperature data, as well as receiving user input.

[0105] Programmable logic controllers (PLCs) are widely used in industrial production, power supply and distribution, and system monitoring, primarily to generate the necessary frequency pulses to control frequency converters. There are two main ways to control frequency converters with a PLC: one is through analog input / output modules, using the collected indoor / outdoor temperature difference as system feedback to form a closed-loop control; the other is through a serial communication interface, such as RS-485 serial communication, connecting the PLC and the frequency converter. This allows the PLC program to control the exhaust fan speed and the number of starts and stops, with the control results used as feedback to achieve zero steady-state error speed regulation. Therefore, this method achieves the most effective automation control and is the most widely used.

[0106] Based on the above analysis, by combining PLC and frequency converter, a genetic algorithm program is used to control the exhaust fan speed and the number of fans starting and stopping, and a corresponding PLC control strategy is formulated according to the actual site conditions.

[0107] The variable frequency automation system uses communication equipment with RS-485 as the data interface to connect the PCL, frequency converter, and exhaust fan. This transmission mode is compatible with both cables and optical fibers, offers high transmission baud rates, long transmission distances, and low distortion.

[0108] The temperature sensor measures the indoor (average) and outdoor temperatures of each substation room in real time, and the temperature difference is sent to the PLC through the analog input module.

[0109] The noise level in each substation room is measured in real time using noise sensors, and the noise readings are sent to the PLC via an analog input module.

[0110] The control loop of the cooling fan adopts closed-loop control. The PLC generates corresponding frequency trigger pulses as the control signal of the frequency converter based on the temperature difference, noise and genetic algorithm results. It also adjusts the fan speed and the number of fans switched on and off according to the difference between the input and feedback quantities, thereby reducing the temperature difference.

[0111] I. Derivation of the Relationship between Temperature and Exhaust Fan Speed

[0112] Assuming that an exhaust fan is installed in each of the four corners of the substation, the goal is to deduce the relationship between indoor temperature and exhaust fan speed based on factors such as the size of the substation, indoor and outdoor temperature difference, air velocity, exhaust fan volume, heat source, and the basic principles of thermodynamics and fluid mechanics. The relevant scheme and steps are briefly introduced below.

[0113] 1) Setting basic parameters

[0114] Station building dimensions: The station building is approximately considered as a cuboid, with dimensions L×W×H, which are the length, width, and height of the room, respectively, in meters.

[0115] Indoor and outdoor temperature difference: Let the indoor temperature be T. in The outdoor temperature is T out The temperature difference between the two is ΔT = T in -T out The unit is Celsius.

[0116] Air velocity: air velocity v air Assume it is a constant value, with the unit being m / s.

[0117] Air volume: The air volume of each exhaust fan is Q. fan The unit is m 3 / h. The exhaust volume of an exhaust fan is directly proportional to its rotational speed, specifically:

[0118] Q fan =k·n

[0119] Where k is a constant of the fan and n is the rotational speed of the exhaust fan, in revolutions per minute.

[0120] Heat sources: There may be multiple heat sources in the station building (such as electrical equipment, lighting equipment, etc.). Assume that the total heat load of these heat sources is Q. heat The unit is watt (W).

[0121] Specific heat capacity of air inside the station building: Let the specific heat capacity of air inside the station building be c. air The air density is ρ air .

[0122] (ii) Heat Balance Equation

[0123] Considering the heat balance of the room, assuming steady-state conditions, the relationship between the station building's temperature and the heat source and exhaust volume can be derived from the following heat conservation equation:

[0124] Q heat +Q inflow =Q outflow

[0125] Q inflow It is the heat entering the station building, Q outflowThe heat is discharged through the exhaust fan.

[0126] The relationship between air velocity and the temperature difference between indoors and outdoors when air enters a room can be represented by heat transfer. The heat Q entering the room... inflow It is mainly determined by the following factors:

[0127] Q inflow =m·c air ·ΔT

[0128] Where m is the air mass flow rate, measured in kg / s, which can be calculated from the air velocity and room volume.

[0129] m = ρ air ·v air ·A room

[0130] Where A room =L×W is the area occupied by the station building.

[0131] The exhaust volume of an exhaust fan is directly proportional to its rotational speed. Therefore, the exhaust volume of one exhaust fan in the station building is:

[0132] Q outflow =Q fan =k·n

[0133] The heat Q removed by the exhaust volume outflow Determined by air mass flow rate and temperature difference:

[0134] Q outflow =m·c air ·(T in -T out )

[0135] (iii) Relationship between exhaust fan speed and indoor temperature

[0136] From the heat balance, we can obtain:

[0137] Q heat +m·c air ·ΔT=k·v fan ·c air ·(T in -T out )

[0138] Under steady-state conditions, the indoor temperature T in Determined by the following equation:

[0139] Q heat +ρ air ·v air ·A room ·c air ·ΔT

[0140] =k·v fan ·ρ air ·v air ·A room ·c air ·(T in -T out )

[0141] Simplifying the relevant constant terms, we can obtain the relationship between the exhaust fan speed and the indoor temperature:

[0142]

[0143] When the temperature in the substation room is too high, the PLC controls the fan speed to increase.

[0144] Q heat +Q inflow outflow

[0145] As the temperature of the exhaust gas increases, the indoor temperature decreases, causing ΔT to increase and Q to decrease. inflow Increase the value until both sides of the formula are equal, reaching equilibrium again.

[0146] II. Relationship between exhaust fan speed and noise

[0147] The relationship between fan speed and noise is usually positively correlated; that is, the higher the fan speed, the greater the noise generated. This phenomenon stems from the interaction of various physical phenomena during the fan's operation. To explain this relationship in more detail, we can explore it from the following aspects:

[0148] 1) Sources of noise

[0149] The noise from exhaust fans mainly comes from the following sources:

[0150] (1) Airflow noise (aerodynamic noise)

[0151] Airflow noise is generated by the interaction between the fan impeller and the air during rotation. As the fan speed increases, the airflow velocity and kinetic energy increase, resulting in stronger turbulence and aerodynamic noise as the airflow passes through the fan blades and exhaust pipes. There is a positive correlation between airflow noise and fan speed.

[0152] (2) Mechanical noise (structural noise)

[0153] The mechanical noise of a fan originates from the vibration and friction between its internal components (such as blades, bearings, and rotor). At high speeds, the vibration frequency of these mechanical components increases, making the noise more pronounced. This noise is typically low-frequency, but at high speeds, both the frequency and intensity can increase due to vibration and impact.​

[0154] (3) Fluid dynamics noise (eddy current noise)

[0155] When the exhaust fan impeller rotates, the airflow generates vortices on the blade surface, leading to flow instability and vortex noise. At high speeds, the turbulence effect of the fluid intensifies, resulting in even more noise from the vortices.

[0156] (4) Exhaust fan vibration and resonance noise

[0157] During operation, wind turbines may vibrate due to imbalance, blade damage, installation problems, or other reasons. These vibrations can propagate through the wind turbine's support structure and cause resonance, thereby amplifying noise.

[0158] (ii) Relationship between rotational speed and noise

[0159] As the fan speed increases, the frequency composition of the noise also changes. At low speeds, the noise is mainly concentrated in the low-frequency range (such as mechanical noise and low-frequency vibration noise); while at high speeds, high-frequency components such as airflow noise and eddy current noise increase. High-frequency noise usually has a more significant impact on humans, especially in environments where low-frequency noise is well controlled.

[0160] Figure 3 The noise-speed relationship curve was obtained from the literature. The graph shows a logarithmic relationship between noise and speed, which can typically be expressed as:

[0161] N v =A·lg(n)+B

[0162] Where: N v Noise level is expressed in dB, n is rotational speed in r / min, and A and B are empirical constants.

[0163] Under this relationship, each increase in the exhaust fan speed leads to a logarithmic increase in noise. As the speed increases, the intensity of airflow turbulence, mechanical vibration, and hydrodynamic effects all increase accordingly, thus raising the noise level.

[0164] III. Genetic Algorithm

[0165] Genetic Algorithm (GA) is a computational model that simulates the biological evolutionary process based on natural selection and genetic mechanisms, as described in Darwin's theory of evolution. It is a randomized search method and can be viewed as a mathematical model of evolution. The algorithm is characterized by parallelizability, high efficiency, and global search capability. It requires virtually no auxiliary knowledge and is not limited by conditions such as differentiability or continuity, and possesses strong global search ability. Similar to natural genetic evolution, for each generation of a genetic algorithm, a fitness function is defined for a specific problem to select individuals. Individuals that meet the fitness function are retained, while those that do not are eliminated. After multiple generations, new individuals are obtained. In this way, mimicking the laws of natural inheritance, we obtain new individuals that are better adapted to the environmental requirements than the old individuals. This is the basic idea behind the implementation of a genetic algorithm.

[0166] This patent uses a genetic algorithm to optimize the operating parameters of multiple exhaust fans in a substation, minimizing noise generated by the fans while ensuring the indoor temperature does not exceed a set threshold. The relationship between indoor temperature and exhaust fan speed is derived from heat balance and is expressed as:

[0167]

[0168] There is an approximate logarithmic relationship between noise and exhaust fan speed, which can be expressed as:

[0169] N v =A·lg(n)+B

[0170] Figure 4 The graph shows the relationship between rotational speed, noise, and temperature. It can be seen from the graph that under the combined constraints of temperature threshold and noise threshold, there exists a rotational speed range that simultaneously meets the requirements for temperature and noise control.

[0171] Therefore, the optimization goal is to find the optimal combination of the rotational speed n and the number of wind turbines q using a genetic algorithm, in order to minimize noise while meeting temperature control requirements and achieve a high-efficiency, low-noise operation strategy. Simultaneously, temperature...

[0172] A certain margin should be reserved to prevent the control system from failing to respond in time due to a sudden increase in station temperature, which could cause the equipment to be in an unfavorable operating state.

[0173] 1) Algorithm Framework

[0174] To find the optimal control scheme, the following steps are needed to determine the optimal speed and number of wind turbines when multiple turbines are running.

[0175] (1) Initialize the population

[0176] Population representation: Each individual represents several wind turbines and the possible exhaust fan speed n of each wind turbine, which can be represented as an n-dimensional array, where q is the number of wind turbines.

[0177] Population size: Select population size A to represent the current solution space, randomly initialize the n-dimensional array in the population, and ensure that the rotational speed of each wind turbine in the n-dimensional array is within a certain range (e.g., 0 to 1000 rpm).

[0178] (2) Fitness function

[0179] The fitness function is used to evaluate the quality of an individual. In this problem, the fitness function consists of two parts: 1. Temperature constraint: The corresponding room temperature is calculated based on the exhaust fan speed. During the calculation, the ventilation volumes of several exhaust fans are directly added or subtracted, and then the room temperature value is calculated using the formula between exhaust fan speed and room temperature. If the temperature exceeds a set threshold T... th If the solution's fitness is 0, it is invalid. 2. Noise constraint: Calculate the noise level based on the rotational speed of each exhaust fan to minimize the noise while not exceeding the maximum temperature threshold.

[0180] When calculating the room temperature under the action of several fans, the room temperature can be calculated using the following formula:

[0181]

[0182] Wherein: T int The room temperature is the result of all fans working together, q is the number of fans, and n is the number of fans. i (i = 0, 1, 2, ..., q) represents the rotational speed of each fan, k, ρ air v air A room c air Q heat All of these are empirical constants.

[0183] When calculating the noise level of several wind turbines, the noise level can be calculated using the following formula:

[0184]

[0185] Where: N vt The sum of noise levels generated by all fans, where q is the number of fans. Let the noise generated by each fan be the noise level. Combining the formula for noise level versus speed of a single fan, it can be further rewritten as follows:

[0186]

[0187] Temperature control constraints:

[0188] According to the heat balance model described above, there is a direct mathematical relationship between fan speed and indoor temperature, namely:

[0189]

[0190] Wherein: T int The room temperature is the result of all fans working together, q is the number of fans, and n is the number of fans. i (i = 0, 1, 2, ..., q) represents the rotational speed of each fan, k, ρ air v air A room c air Q heat All of these are empirical constants.

[0191] Therefore, the fitness function first evaluates whether the indoor temperature exceeds a set threshold under a given fan configuration. This threshold also considers the margin for sudden temperature increases during station operation. If the temperature exceeds the threshold, the fitness value of the solution is zero, indicating that the configuration is invalid. Otherwise, the fitness value is adjusted according to the temperature deviation: the smaller the temperature deviation, the higher the fitness value.

[0192] Noise level is related to the number of fans started and the speed of each fan; the lower the noise, the higher the adaptability.

[0193]

[0194] Wherein: F temp T represents the fitness value for temperature. int At room temperature, T th The set temperature threshold.

[0195] Noise minimization constraint:

[0196] As mentioned earlier, there is the following relationship between noise and fan speed:

[0197]

[0198] Where: N vt The sum of noise levels generated by all fans, where q is the number of fans and n is the total noise level. i (i = 0, 1, 2, ..., q) represents the rotational speed of each fan, and A and B are empirical constants.

[0199] To minimize noise and optimize the fan speed and number in the genetic algorithm, the fitness function is designed in conjunction with noise constraints. During optimization, we aim to reduce noise while ensuring the temperature remains within the required range by controlling the fan speed; therefore, the fitness function F... noise It will adjust according to the noise level; the noise fitness function is as follows:

[0200]

[0201] Wherein: F noise N represents the fitness value for noise. vt The sum of noise levels for all fans is represented by α, which is an adjustment factor used to control the degree of noise impact on fitness. If α is large, the noise penalty will be more significant, and the fan speed needs to be kept as low as possible.

[0202] To further optimize noise control, a noise upper limit and a penalty factor are introduced into the fitness function. This causes the fitness value to drop sharply when the noise exceeds a certain threshold. Furthermore, when the system has multiple fans, the impact of the number of fans on noise is considered. The penalty factor increases the complexity of the number and speed of fans in the configuration, preventing excessive noise caused by an excessive number of fans or excessively high speeds. The fitness function is further designed as follows:

[0203]

[0204] Where: N max The set noise upper limit is when the noise N vt Exceeding this threshold will cause the fitness value to drop rapidly, indicating that the noise level of the configuration is severely excessive. p represents the number of wind turbines, and β is a penalty factor used to adjust the impact of the number of wind turbines on the fitness value. If the number of wind turbines is too high, the β·p term will lower the fitness value, thus forcing the genetic algorithm to select an appropriate number of wind turbines.

[0205] (3) Select operation

[0206] Selection is used to choose individuals with higher fitness from the current population as parents. Selection methods can include roulette wheel selection or tournament selection, which select the better individuals based on their fitness.

[0207] (4) Cross operation

[0208] Crossover is used to exchange the genes (i.e., the rotational speed values ​​of each wind turbine) of two parent individuals to generate new offspring individuals. Common crossover methods include single-point crossover and uniform crossover.

[0209] (5) Mutation operation

[0210] The mutation operation is used to randomly fine-tune the rotational speed of some individuals to maintain population diversity and prevent them from getting trapped in local optima. The mutation magnitude can be set to a certain range, such as making small changes around the current rotational speed.

[0211] (6) Update the population

[0212] After crossover and mutation operations, a new population is generated. You can choose to retain the parent and offspring by merging them, or you can choose to cull individuals with poor fitness.

[0213] (7) Termination Conditions

[0214] Set a maximum number of iterations, or stop the algorithm when the fitness reaches a preset threshold. The final output is the optimal solution (i.e., the optimal speed of each exhaust fan).

[0215] II) Algorithm Explanation

[0216] (1) Initialize the population: Randomly generate the exhaust fan speed n and the number of running fans q in the initial population. Fitness function: Calculate the corresponding room temperature and noise based on the speed, ensuring that the room temperature does not exceed the threshold and minimizing the noise.

[0217] (2) Selection operation: Use tournament selection to select the parent with higher fitness.

[0218] (3) Crossover operation: The offspring speed is generated by the average of the parent speed.

[0219] (4) Mutation operation: Increase the diversity of the population by randomly changing the rotation speed with a certain probability.

[0220] (5) Optimization result: Output the optimal speed of each exhaust fan.

[0221] (iii) Exhaust fan control strategy based on genetic algorithm

[0222] The PLC (Programmable Logic Controller) is responsible for real-time control of the optimized exhaust fan speed configuration to each exhaust fan. The PLC obtains feedback and adjusts the fan speed by monitoring indoor temperature and noise data in real time to achieve optimal control. The PLC uses sensors to monitor the temperature and noise in the substation room in real time and transmits the data to the control system. Based on the optimal speed configuration output by the genetic algorithm, the PLC adjusts the speed of each exhaust fan. As temperature and noise data change, the PLC and the genetic algorithm work together to dynamically adjust the exhaust fan speed, ensuring that the temperature does not exceed the set threshold while minimizing noise.

[0223] Algorithm Implementation

[0224] First, various environmental parameters of the room are set, such as room size, heat load, initial temperature, and exhaust fan performance. A genetic algorithm calculates the optimal exhaust fan speed configuration to ensure minimal noise while meeting temperature constraints. The PLC executes the optimized control strategy, adjusting the exhaust fan speed and the number of operating fans in real time, and continues to optimize based on feedback data. This achieves the goal of minimizing environmental noise while meeting temperature control requirements in the substation.

[0225] This invention proposes a variable frequency control technology for substation ventilation systems based on a genetic algorithm. This technology enables the joint optimization control of multiple exhaust fans while ensuring that the substation room temperature does not exceed a set threshold, thereby minimizing operating noise. The system employs a distributed sensor network to collect real-time temperature and noise data from the substation. Combined with variable frequency speed control technology, a multi-objective optimization model is established using a genetic algorithm, with noise and temperature as constraints and optimization objectives solved jointly. The genetic algorithm dynamically adjusts the operating frequency of the exhaust fans through population initialization, fitness evaluation, selection, crossover, and mutation operations, thereby balancing the needs of temperature control and noise minimization. This invention can adaptively adjust the exhaust fan speed according to different environmental conditions and equipment operating states, enabling the system to effectively reduce noise pollution while achieving efficient heat dissipation. Compared with traditional single-unit fixed-speed or simple linkage control methods, this technology can significantly improve the overall energy efficiency of the system, reduce energy waste, and improve the operating environment of the substation. This technology is applicable to noise and temperature control needs in various types of indoor substations and similar scenarios, possessing high applicability and promotional value.

Claims

1. A frequency conversion control method for noise in a substation ventilation system based on a genetic algorithm, characterized in that, Includes the following steps: Step 1: Install multiple noise sensors and temperature sensors in the substation room; the noise sensors are used to monitor the noise level of each area in the substation room in real time, and the average value of the monitoring values ​​of all noise sensors is used as the comprehensive index of indoor noise and input to the PLC control module; the temperature sensors are used to monitor the temperature inside and outside the substation room, obtain the temperature difference between indoor and outdoor, and input the temperature difference data into the PLC control module as a reference for temperature and noise control. Step 2: The PLC control module transmits the collected noise and temperature data to the host computer via the RS-485 communication protocol. The host computer processes the received data and uses a genetic algorithm to perform calculations and optimization analysis. The monitored temperature and noise data are displayed in real-time on the host computer's monitoring interface. Based on the current temperature threshold constraints and the changing trends of indoor noise and temperature, the genetic algorithm dynamically adjusts the fan's operating frequency, aiming to minimize the noise generated by the fan while ensuring that the indoor temperature does not exceed the set threshold. Step 3: After calculation by the genetic algorithm, the optimal speed control strategy generated by the host computer will be sent to the PLC control module through the RS-485 communication protocol; the PLC control module will convert the speed control signal into a current frequency control signal and then transmit it to the frequency converter of each fan; the frequency converter will adjust the speed of each fan according to the current frequency control signal to achieve precise control of noise and temperature. Step 4: As the ambient temperature and noise change in real time, the PLC control module continuously transmits the latest data to the host computer. When the temperature approaches the temperature threshold, the genetic algorithm will recalculate the optimization scheme based on the updated data. Through this dynamic adjustment method, the PLC control module and the genetic algorithm work together to ensure that the exhaust fan system always adjusts the fan speed according to the actual conditions inside and outside the station during the entire operation.

2. The frequency conversion control method for noise in a substation ventilation system based on a genetic algorithm according to claim 1, characterized in that, The optimization of temperature and noise data by the genetic algorithm specifically includes: the relationship between indoor temperature and exhaust fan speed is derived from heat balance and expressed as: T in Indicates indoor temperature; T out Indicates outdoor temperature; n represents exhaust fan speed; k, ρ air v air A room c air These are empirical constants; Let be the total heat load of the heat sources within the station building, and be an empirical constant. There is an approximate logarithmic relationship between noise and exhaust fan speed, expressed as: Where, N v This indicates the noise level produced by a single fan. , All of these represent empirical constants.

3. The frequency conversion control method for noise in a substation ventilation system based on a genetic algorithm according to claim 2, characterized in that, The speed control strategy specifically includes: (1) Initialize the population Population representation: Each individual represents several wind turbines and the exhaust fan speed n of each wind turbine, which is represented as an n-dimensional array, where q is the number of wind turbines; Population size: Select population size A to represent the current solution space, randomly initialize the n-dimensional array in the population, and ensure that the rotational speed of each wind turbine in the n-dimensional array is within a certain range; (2) Fitness function When calculating the room temperature under the action of several fans, the room temperature can be calculated using the following formula: in: The room temperature is the result of all the fans working together. Number of wind turbines The rotational speed of each fan. , , , , All are empirical constants; When calculating the noise level of several wind turbines, the noise level is calculated using the following formula: in: The sum of the noise levels generated by all the fans. Number of wind turbines The noise generated by each fan; Temperature control constraints: According to the heat balance model, there is a direct mathematical relationship between fan speed and indoor temperature; The fitness function first evaluates whether the indoor temperature exceeds a set threshold under a given fan configuration. If the temperature exceeds the threshold, the fitness value is zero, indicating that the configuration is invalid; otherwise, the fitness value will be adjusted according to the temperature deviation: the smaller the temperature deviation, the higher the fitness value. Noise level is related to the number of fans started and the speed of each fan; the lower the noise level, the higher the adaptability. in: This is the temperature fitness value. Indoor temperature, The set temperature threshold; Noise minimization constraint: The following relationship exists between noise and fan speed: in: The sum of the noise levels generated by all the fans. Number of wind turbines The rotational speed of each fan. , All are empirical constants; fitness function It will adjust according to the noise level; the noise fitness function is as follows: in: This represents the fitness value for noise. The sum of the noise levels generated by all the fans. It is an adjustment factor used to control the degree of influence of noise on fitness; if The larger the noise level, the more significant the penalty will be, and the fan speed needs to be kept as low as possible. The fitness function is further designed as follows: in: The set noise limit, when Exceeding this threshold will cause the fitness value to drop rapidly, indicating that the noise level of the configuration is severely excessive. Number of wind turbines This is a penalty factor used to adjust the impact of the number of wind turbines on the fitness value; if the number of wind turbines is too large, This will lower the fitness value, thus forcing the genetic algorithm to select the appropriate number of wind turbines; (3) Select operation The selection operation is used to select individuals with higher fitness from the current population as parent individuals; the selection method adopts roulette wheel selection or tournament selection, which can select better individuals based on their fitness. (4) Cross operation Crossover is used to exchange the genes (i.e., the rotational speed values ​​of each wind turbine) of two parent individuals to generate new offspring individuals; common crossover methods include single-point crossover and uniform crossover. (5) Mutation operation The mutation operation is used to randomly fine-tune the rotation speed of some individuals in order to maintain the diversity of the population and prevent it from getting trapped in local optima; the mutation amplitude is set to a certain range. (6) Update the population After crossover and mutation operations, a new population is generated; the choice is made between merging the parents and offspring, or eliminating individuals with poor fitness. (7) Termination conditions Set a maximum number of iterations, or stop the algorithm when the fitness reaches a preset threshold; the final output is the optimal solution, which is the optimal speed of each row of fans.

4. The frequency conversion control method for noise in a substation ventilation system based on a genetic algorithm according to claim 3, characterized in that, Assuming steady-state conditions, the relationship between the station building's temperature and the heat source and exhaust volume can be derived from the following heat conservation equation: in It is the heat entering the station building. The heat is discharged through the exhaust fan; The relationship between air velocity and the temperature difference between indoors and outdoors when air enters a room is represented by heat transfer; the amount of heat entering the room... It is mainly determined by the following factors: in It is the mass flow rate of air, measured in units of... The results were calculated using airflow velocity and room volume: in It refers to the floor area of ​​the station building; The exhaust volume of an exhaust fan is directly proportional to its rotational speed. Therefore, the exhaust volume of one exhaust fan in the station building is: Heat removed by the exhaust volume Determined by air mass flow rate and temperature difference: 。 5. The frequency conversion control method for noise in a substation ventilation system based on a genetic algorithm according to claim 4, characterized in that, From the heat balance, we get: Under steady-state conditions, indoor temperature Determined by the following equation: Simplifying the relevant constant terms, we can obtain the relationship between the exhaust fan speed and the indoor temperature: When the temperature in the substation room is too high, the PLC controls the fan speed to increase. When the exhaust temperature increases, the indoor temperature will decrease, causing... Get bigger Increase the value until both sides of the formula are equal, reaching equilibrium again.

6. A variable frequency control system for noise in a substation ventilation system based on a genetic algorithm, characterized in that... A frequency conversion control method for noise of a substation ventilation system based on a genetic algorithm, as described in any one of claims 1-5; It includes a noise monitoring module, a temperature monitoring module, a PLC control module, a rectifier module, an energy consumption module, and an inverter module.

7. A variable frequency control system for noise in a substation ventilation system based on a genetic algorithm, as described in claim 6, is characterized in that, The noise monitoring module specifically includes a noise sensor, which monitors the noise level in the environment, in decibels (dB).

8. A frequency conversion control system for noise in a substation ventilation system based on a genetic algorithm, as described in claim 6, is characterized in that... The temperature monitoring module specifically includes a temperature sensor, which monitors the temperature of the environment or object and converts the temperature data into a digital signal.

9. A frequency conversion control system for noise in a substation ventilation system based on a genetic algorithm, as described in claim 6, is characterized in that, The PLC control module consists of digital input / output modules, analog input modules, and analog output modules of the PLC programmable controller.

10. A variable frequency control system for noise in a substation ventilation system based on a genetic algorithm, as described in claim 6, is characterized in that... The rectifier module adopts a three-phase bridge rectifier circuit, and the switching element is a diode. The filter capacitor Cf is used to filter out the DC side voltage ripple of the rectifier circuit and stabilize the output voltage.

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