Supercritical carbon dioxide direct circulation nuclear power plant decoupling control system and method

Abstract

The invention discloses a decoupling control system and method for a supercritical carbon dioxide direct circulation nuclear power plant. A compressor inlet temperature constant control strategy and a compressor inlet pressure constant control strategy are adopted. A compressor inlet temperature control system adjusts the opening degree of a cooler cold side valve, and a compressor inlet pressure control system adjusts the working medium filling and discharging of a capacity box. In order to guarantee the power supply quality, a transmission shaft rotating speed constant control strategy is adopted, and transmission shaft auxiliary torque is adjusted through a transmission shaft rotating speed control system. In order to ensure the system efficiency, a reactor core outlet temperature constant control strategy is adopted, and a reactor core outlet temperature control system is used for adjusting the opening degree of a control valve; in order to achieve the reactor following operation target, a reactor core power matching demand power control strategy is adopted, the control rod speed is adjusted through a power control system, and based on the neuron PID decoupling control technology, the control target that each output parameter in the system is only influenced by a certain specific input parameter is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of supercritical carbon dioxide direct cycle nuclear power plant, and in particular to a decoupling control system and method for a supercritical carbon dioxide direct cycle nuclear power plant. Background Art

[0002] The supercritical carbon dioxide direct cycle nuclear power system is a system that uses carbon dioxide as a working fluid, obtains nuclear heat directly from a nuclear reactor, and then converts nuclear energy into electrical energy or mechanical energy through a supercritical carbon dioxide thermodynamic cycle. Due to the physical property mutation of supercritical carbon dioxide at the critical point (7.38MPa, 31.2℃), the compressor inlet operating condition in the supercritical carbon dioxide direct cycle nuclear power system is set near the critical point, and the low compressibility of supercritical carbon dioxide under this operating condition and the high density difference between the compressor inlet and outlet caused by the physical property mutation can be used to greatly reduce the power consumption of the supercritical carbon dioxide compressor. At the same time, as a primary coolant, carbon dioxide is safe, non-toxic, non-flammable, abundant in reserves and naturally exists. It has excellent physical and chemical stability and exhibits inert gas characteristics within the reactor design temperature range. Compared with other traditional gas working fluids, supercritical carbon dioxide is easier to achieve a larger density difference, which is beneficial to discharge the core waste heat through natural circulation and greatly improve the reliability of the reactor.

[0003] The supercritical carbon dioxide direct cycle nuclear power system is a very promising new power system with the advantages of simplified system, compact structure, strong mobility and high thermal efficiency. Its large-scale promotion and application is conducive to the clean and efficient application of my country's nuclear resources, which can significantly reduce greenhouse gas emissions and is of great significance to the realization of the "dual carbon" goal. The above advantages of the supercritical carbon dioxide direct cycle nuclear power system not only meet the development requirements of the next generation of advanced energy conversion technology, but also conform to the future development trend of nuclear power plants, such as described in the article "The Rise and Development of Supercritical Carbon Dioxide Nuclear Power System" published by Huang Yanping et al. in "Atomic Energy Science and Technology, Vol. 57, No. 9, September 2023".

[0004] However, since the physical properties of supercritical carbon dioxide working fluid change dramatically near the critical point, and the supercritical carbon dioxide direct cycle nuclear power system has the characteristics of strong coupling of multiple devices, the control actions of each control system will have a strong mutual influence, making the regulation and control of its variable load, startup, shutdown and other processes more complicated. For example, the patent application with publication number CN112071457B discloses a load tracking method for a supercritical carbon dioxide direct cooling reactor system. When operating at full load, the CO2 working fluid flows into the gas turbine to expand and do work, releases part of the heat in the regenerator, then enters the precooler for cooling, enters the compressor for pressurization, flows into the regenerator to absorb the energy of the turbine exhaust gas, and returns to the reactor to complete the entire cycle. When operating under partial load conditions, the generator power is matched with the grid demand through the generator power control system. The compressor inlet temperature control system is used to control the compressor inlet temperature during load operation. The compressor blocking protection system is used to ensure that the compressor is away from the blocking operation area. The power of the reactor is automatically adjusted by the core power control system relying on reactivity feedback, so that the reactor power is automatically adjusted following the change of the generator power, thereby realizing the load tracking capability of the supercritical carbon dioxide direct cooling reactor system within the full load range. However, this patent application has the defect that each control action affects each other. Therefore, it is necessary to select a suitable decoupling control method to reduce or eliminate the mutual influence of various control actions as much as possible, so that the fluctuation time and amplitude of the main parameters of the system can be controlled within a reasonable range under various loads and disturbances, avoiding overheating, overspeed, surge, etc., and maximizing the unit response speed, so that the system can quickly restore stability and ensure safe and stable operation of the system, which has important research significance and application value. Summary of the invention

[0005] In order to overcome the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a decoupling control system and method for a supercritical carbon dioxide direct cycle nuclear power plant. According to the multi-variable strong coupling characteristics of the supercritical carbon dioxide direct cycle nuclear power system, under its variable load, startup, shutdown and other operating conditions, the mutual influence between different control channels is eliminated, so that each output variable in the controlled system is only affected by a specific input variable, thereby achieving the decoupling control goal of the multi-variable strong coupling system and ensuring the safe and stable operation of the system.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A decoupling control system for a supercritical carbon dioxide direct cycle nuclear power plant, comprising a reactor 6, wherein the outlet end of a carbon dioxide pipeline of the reactor 6 is connected to the inlet end of a carbon dioxide of a turbine 10, and the outlet end of the carbon dioxide pipeline of the turbine 10 sequentially passes through a cold side of a regenerator 7, a secondary side of a cooler 8, a compressor 9, and a hot side of the regenerator 7 and returns to the inlet end of the carbon dioxide of the reactor 6; the turbine shaft of the turbine 10 is connected to the rotating shaft of a generator 11;

[0008] A first temperature sensor and a compressor inlet temperature control system 1 are provided on the pipeline between the secondary side carbon dioxide outlet end of the cooler 8 and the carbon dioxide inlet end of the compressor 9. The signal output end of the first temperature sensor is connected to the signal input end of the compressor inlet temperature control system 1; the compressor inlet temperature control system 1 adjusts the opening of the first control valve 13 arranged at the cold water inlet end of the primary side of the cooler.

[0009] A first pressure sensor and a compressor inlet pressure control system 2 are provided on the pipeline between the secondary side carbon dioxide outlet of the cooler 8 and the carbon dioxide inlet of the compressor 9, the signal output end of the first pressure sensor is connected to the signal input end of the compressor inlet pressure control system 2, and the signal output end of the compressor inlet pressure control system 2 is connected to the signal end of the second control valve 14 at the outlet end of the capacity box 12;

[0010] A second temperature sensor is provided on the pipeline between the outlet end of the carbon dioxide pipeline of the reactor 6 and the inlet end of the carbon dioxide pipeline of the turbine 10, and the signal end of the second temperature sensor is connected to the signal end of the core outlet temperature control system 3, and the core outlet temperature control system 3 adjusts the opening of the third control valve 15 arranged between the cold side inlet end and the hot side outlet end of the regenerator 7;

[0011] The signal output of the first power sensor on the generator 11 passes through the signal terminal of the power control system 4, the signal terminal of the control drum 16 and the signal terminal of the reactor 6 in sequence, and the power control system 4 drives the control drum 16 to adjust the reactivity;

[0012] A first speed sensor is provided on the transmission shaft connecting the compressor 9 and the turbine 10, and a signal end of the first speed sensor is connected to a signal end of a transmission shaft speed control system 5, and the transmission shaft speed control system 5 adjusts the auxiliary torque of the transmission shaft;

[0013] The compressor inlet temperature control system 1, the compressor inlet pressure control system 2, the core outlet temperature control system 3, the power control system 4 and the transmission shaft speed control system 5 are all built-in PID neural network decoupling controllers, which are constructed into a 10×15×5 forward network structure to achieve the control goal that each output parameter of the controlled system is only affected by a specific input parameter.

[0014] The decoupling control method based on the above-mentioned supercritical carbon dioxide direct cycle nuclear power plant decoupling control system comprises the following steps:

[0015] Step 1: Building a control system for a supercritical carbon dioxide direct cycle nuclear power plant: The compressor inlet temperature control system 1 maintains a constant compressor 9 inlet temperature by adjusting the flow rate on the cold side of the cooler 8; the compressor inlet pressure control system 2 maintains a constant compressor 9 inlet pressure by controlling the circulating working medium in the capacity box 12; the core outlet temperature control system 3 maintains a constant reactor 6 outlet temperature by adjusting the opening of the control valve 15; the power control system 4 achieves real-time matching of the reactor power and the load power by driving the reactivity introduced by the control drum 16; the compressor 9, the turbine 10 and the generator 11 rotate coaxially, and the transmission shaft speed control system 5 maintains a constant transmission shaft speed by adjusting the transmission shaft auxiliary torque;

[0016] Step 2: The compressor inlet temperature control system 1, the compressor inlet pressure control system 2, the core outlet temperature control system 3, the power control system 4 and the transmission shaft speed control system 5 in step 1 are constructed into a PID neural network decoupling control method with a 10×15×5 forward network structure;

[0017] Step 3: Based on the PID neural network decoupling control method constructed in step 2, the beetle swarm algorithm is used to globally search for the optimal initial weights of the five control systems: compressor inlet temperature control system 1, compressor inlet pressure control system 2, core outlet temperature control system 3, power control system 4 and transmission shaft speed control system 5;

[0018] Step 4: When the operating conditions of the supercritical carbon dioxide direct cycle nuclear power system change, in order to ensure the critical state of the working fluid at the compressor inlet, the PID neural network will take the difference between the output set value and the actual output value of the controlled system as input, and update the connection weights of the neurons in real time through the supervised Hebb learning rule, and adjust the strength of the proportional, integral, and differential effects in the system control process in real time, and finally obtain the input of the controlled system, complete the decoupling control of the supercritical carbon dioxide direct cycle nuclear power device, and transform the multivariable strong coupling system into multiple single-input and single-output systems, so as to achieve the control goal that each output parameter of the controlled system is only affected by a specific input parameter.

[0019] The step 2 is constructed into a 10×15×5 forward network structure, that is, the compressor inlet temperature control system 1, the compressor inlet pressure control system 2, the core outlet temperature control system 3, the power control system 4 and the transmission shaft speed control system 5 are 5 parallel sub-network structures, T c_ref , P c_ref 、T core_ref 、N r_ref 、vref They are the input set values ​​of the PID neural network decoupling controller; KV1, KV2, KV3, v r , T are the input values ​​of the controlled system respectively; T c , P c 、T core 、N r and v are the actual output values ​​of the controlled system; w si is the connection weight from the hidden layer to the output layer; s is the serial number of the parallel sub-network, s = 1, 2, 3, 4, 5; i is the serial number of the hidden layer neuron, i = 1, 2, 3.

[0020] The specific algorithm process of the beetle swarm algorithm in step 3 is as follows:

[0021] Initialize the parameters of the beetle population, set the number of beetles in the population, randomly assign the speed, position, and flight direction parameters of each beetle, and define the following fitness function:

[0022]

[0023] Where T is the maximum number of iterations of the beetle swarm algorithm, length is the number of PIDNN training times, n is the number of beetles in the beetle swarm, r is the target signal value, and y is the output of the system.

[0024] The fitness value of each position of the longicorn beetle in the longicorn beetle group is calculated according to the fitness function, and the optimal position of the individual longicorn beetle and the global optimal position are updated according to the fitness value of each position of the longicorn beetle;

[0025] Update the inertia weight:

[0026]

[0027] Where T is the maximum number of iterations, θ t is the inertia weight, θ max and θ min are the maximum and minimum values ​​of θ respectively.

[0028] Update the speed of each beetle:

[0029]

[0030] In the formula, represents the position of the i-th beetle in the t-th iteration of the q-th dimension, represents the speed of the ith beetle in the tth iteration in the qth dimension, is the individual extreme position, is the extreme position of the population, b1 and b2 are two learning factors, z3 and z4 are random numbers in [0,1];

[0031] Update the increment function:

[0032]

[0033] In the formula, represents the increment function, δ t represents the step size in the t-th iteration of the beetle, which decreases as the number of iterations increases and is updated according to the following formula:

[0034] δ t = z1δ t-1 +δ 0

[0035] In the formula, z1 is a positive constant, δ 0 is the initial value of the step length, and the positions of the left and right antennae of each longhorn beetle are updated as follows:

[0036]

[0037]

[0038] Where, d t represents the distance between the two whiskers in the tth iteration;

[0039] Update the position of each longhorn beetle:

[0040]

[0041] In the formula, β is a positive constant. After multiple iterations, the beetle swarm algorithm completes the global search and obtains the global optimal solution.

[0042] The PID neural network in step 4 takes the difference between the output set value and the actual output value of the controlled system as input, and the PID neural network input variables x1, x2, and x3 will be expressed in incremental form:

[0043]

[0044] According to the input variables x1, x2, x3 of the PID neural network, the input quantity of the controlled system will be calculated by the following formula:

[0045]

[0046] Where K is the neuron proportional coefficient, K>0; w1, w2, w3 are the neuron connection weights, which are approximately equal to the proportional, integral, and differential coefficients of the PID controller.

[0047] The supervised Hebb learning rule in step 4 is an algorithm rule that selects the supervised Hebb learning rule as the connection weight, specifically:

[0048]

[0049] Where η p is the proportional learning rate; η i is the integrated learning rate; η d is the differential learning rate;

[0050] The connection weights of the neural network are adjusted in real time through the supervised Hebb learning rule until the input error signal is close to zero. At this time, the input of the controlled system is the correct input. That is, the mutual influence between different control channels is eliminated by adjusting the strength of the proportional, integral and differential effects of the PID neural network.

[0051] In the step 4, the difference between the output set value and the actual output value of the controlled system is used as input. In the five control systems, namely, the compressor inlet temperature control system 1, the compressor inlet pressure control system 2, the core outlet temperature control system 3, the power control system 4 and the transmission shaft speed control system 5, the difference input and the PID neural network decoupling control method are specifically:

[0052] The compressor inlet temperature setting value of the compressor inlet temperature control system 1 is subtracted from the measurement value of the first temperature sensor, and the difference signal is sent to the PID neural network decoupling controller in the compressor inlet temperature control system 1; the decoupling controller sends its output signal to the first control valve 13;

[0053] The compressor inlet pressure setting value of the compressor inlet pressure control system 2 is subtracted from the measured value of the first pressure sensor, and the difference signal is sent to the PID neural network decoupling controller in the compressor inlet pressure control system 2; the decoupling controller sends its output signal to the second control valve 14 of the capacity box;

[0054] The core outlet temperature setting value of the core outlet temperature control system 3 is subtracted from the measured value of the second temperature sensor, and the difference signal is sent to the PID neural network decoupling controller in the core outlet temperature control system 3; the decoupling controller sends its output signal to the third control valve 15;

[0055] The power setting value of the power control system 4 is subtracted from the measured value of the first power sensor, and the difference signal is sent to the PID neural network decoupling controller in the power control system 4; the decoupling controller sends its output signal to the control drum 16;

[0056] The transmission shaft speed set value of the transmission shaft speed control system 5 is subtracted from the measured value of the first speed sensor, and the difference signal is sent to the PID neural network decoupling controller in the transmission shaft speed control system 5; the decoupling controller sends its output signal to the actuator to adjust the transmission shaft auxiliary torque.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. To ensure the critical state of the working fluid at the compressor inlet, the compressor inlet constant temperature and compressor inlet constant pressure control strategies are adopted, and the compressor inlet temperature control system is used to adjust the valve opening of the cold side of the cooler and the compressor inlet pressure control system is used to adjust the filling and discharge of the working fluid in the capacity box; to ensure the power supply quality, the drive shaft speed constant control strategy is adopted, and the drive shaft auxiliary torque is adjusted through the drive shaft speed control system; to ensure the system efficiency, the core outlet constant control strategy is adopted, and the core outlet temperature control system is used to adjust the valve opening; to achieve the operation target of the reactor following the machine, the core power matching demand power control strategy is adopted, and the control rod speed is adjusted through the power control system.

[0059] 2. The decoupling system of the supercritical carbon dioxide direct cycle nuclear power plant aims at the multi-parameter and strong coupling problems of the supercritical carbon dioxide direct cycle system. Based on the neuron PID decoupling control technology, the system is converted into multiple single-input and single-output controls to eliminate the mutual influence between different control channels and achieve the control goal that each output parameter in the system is only affected by a specific input parameter.

[0060] In summary, the decoupling system and method of the supercritical carbon dioxide direct cycle nuclear power plant proposed in the present invention are easy to implement in engineering. The five control systems transform the supercritical carbon dioxide direct cycle nuclear power plant from a multivariable strong coupling system into multiple single-input single-output control systems, thereby achieving the control target that each output variable in the multivariable coupling system is only affected by a specific input variable. And it is expected to have good economy and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a connection diagram of the entire system of the present invention.

[0062] Figure 2 This is the structure diagram of PID single neuron network control.

[0063] Figure 3 This is the structure diagram of PID neural network decoupling control. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0065] Reference Figure 1A decoupling control system for a supercritical carbon dioxide direct cycle nuclear power plant includes a reactor 6, a carbon dioxide pipeline outlet of the reactor 6 is connected to a carbon dioxide pipeline inlet of a turbine 10, and the carbon dioxide pipeline outlet of the turbine 10 sequentially passes through a cold side of a regenerator 7, a secondary side of a cooler 8, a compressor 9, and a hot side of the regenerator 7 to return to the carbon dioxide inlet of the reactor 6; a turbine shaft of the turbine 10 is connected to a rotating shaft of a generator 11;

[0066] A first temperature sensor and a compressor inlet temperature control system 1 are provided on the pipeline between the secondary side carbon dioxide outlet end of the cooler 8 and the carbon dioxide inlet end of the compressor 9. The signal output end of the first temperature sensor is connected to the signal input end of the compressor inlet temperature control system 1; the compressor inlet temperature control system 1 adjusts the opening of the first control valve 13 arranged at the cold side inlet end of the cooler 8.

[0067] A first pressure sensor and a compressor inlet pressure control system 2 are provided on the pipeline between the secondary side carbon dioxide outlet of the cooler 8 and the carbon dioxide inlet of the compressor 9, the signal output end of the first pressure sensor is connected to the signal input end of the compressor inlet pressure control system 2, and the signal output end of the compressor inlet pressure control system 2 is connected to the signal end of the second control valve 14 at the outlet end of the capacity box 12; the opening of the second control valve 14 is controlled;

[0068] A second temperature sensor is provided on the pipeline between the outlet end of the carbon dioxide pipeline of the reactor 6 and the inlet end of the carbon dioxide pipeline of the turbine 10, and the signal end of the second temperature sensor is connected to the signal end of the core outlet temperature control system 3, and the core outlet temperature control system 3 adjusts the opening of the third control valve 15 arranged between the cold side inlet end and the hot side outlet end of the regenerator 7;

[0069] The signal output of the first power sensor on the generator 11 passes through the signal terminal of the power control system 4, the signal terminal of the control drum 16 and the signal terminal of the reactor 6 in sequence, and the power control system 4 drives the control drum 16 to adjust the reactivity;

[0070] A first speed sensor is provided on the transmission shaft connecting the compressor 9 and the turbine 10, and a signal end of the first speed sensor is connected to a signal end of a transmission shaft speed control system 5, and the transmission shaft speed control system 5 adjusts the auxiliary torque of the transmission shaft;

[0071] The compressor inlet temperature control system 1, the compressor inlet pressure control system 2, the core outlet temperature control system 3, the power control system 4 and the transmission shaft speed control system 5 are all built-in PID neural network decoupling controllers, which are constructed into a 10×15×5 forward network structure to achieve the control goal that each output parameter of the controlled system is only affected by a specific input parameter.

[0072] The working principle of the above system is as follows: when operating at full load, the loop circulating medium is heated by the reactor 6, flows into the turbine 10 to expand and do work, and drives the generator 11 to rotate the rotating shaft; then, the loop circulating medium releases part of the heat through the regenerator 7, enters the cooler 8 to cool to the target temperature, enters the compressor 9 for pressurization, absorbs the heat of the hot side loop circulating medium through the regenerator 7, and returns to the reactor 6 to complete the entire cycle process;

[0073] When operating at partial load, the compressor inlet temperature control system 1 is used to control the cold side feed water flow of the cooler 8 to maintain the constant inlet temperature of the compressor 9; the compressor inlet pressure control system 2 is used to control the filling and discharging capacity of the capacity tank 12 to maintain the constant compressor inlet pressure; the core outlet temperature control system 3 is used to adjust the opening of the third control valve 15 to maintain the constant core outlet temperature; the power control system 4 is used to drive the control drum 16 to ensure that the core output power matches the required power; the drive shaft speed control system 5 is used to adjust the drive shaft auxiliary torque to maintain the drive shaft speed constant.

[0074] The decoupling control method based on the above-mentioned supercritical carbon dioxide direct cycle nuclear power plant decoupling control system comprises the following steps:

[0075] Step 1: Building a control system for a supercritical carbon dioxide direct cycle nuclear power plant: The compressor inlet temperature control system 1 maintains a constant compressor 9 inlet temperature by adjusting the flow rate on the cold side of the cooler 8; the compressor inlet pressure control system 2 maintains a constant compressor 9 inlet pressure by controlling the circulating working medium in the capacity box 12; the core outlet temperature control system 3 maintains a constant reactor 6 outlet temperature by adjusting the opening of the control valve 15; the power control system 4 achieves real-time matching of the reactor power and the load power by driving the reactivity introduced by the control drum 16; the compressor 9, the turbine 10 and the generator 11 rotate coaxially, and the transmission shaft speed control system 5 maintains a constant transmission shaft speed by adjusting the transmission shaft auxiliary torque;

[0076] Step 2: The compressor inlet temperature control system 1, the compressor inlet pressure control system 2, the core outlet temperature control system 3, the power control system 4 and the transmission shaft speed control system 5 in step 1 are constructed into a PID neural network decoupling control method with a 10×15×5 forward network structure;

[0077] Step 3: Based on the PID neural network decoupling control method constructed in step 2, the beetle swarm algorithm is used to globally search for the optimal initial weights of the five control systems: compressor inlet temperature control system 1, compressor inlet pressure control system 2, core outlet temperature control system 3, power control system 4 and transmission shaft speed control system 5;

[0078] Step 4: When the operating conditions of the supercritical carbon dioxide direct cycle nuclear power system change, in order to ensure the critical state of the working fluid at the compressor inlet, the PID neural network will take the difference between the output set value and the actual output value of the controlled system as input, and update the connection weights of the neurons in real time through the supervised Hebb learning rule, and adjust the strength of the proportional, integral, and differential effects in the system control process in real time, and finally obtain the input of the controlled system, complete the decoupling control of the supercritical carbon dioxide direct cycle nuclear power device, and transform the multivariable strong coupling system into multiple single-input and single-output systems, so as to achieve the control goal that each output parameter of the controlled system is only affected by a specific input parameter.

[0079] See also Figure 3 , the step 2 is constructed into a 10×15×5 forward network structure, that is, the compressor inlet temperature control system 1, the compressor inlet pressure control system 2, the core outlet temperature control system 3, the power control system 4 and the transmission shaft speed control system 5 are 5 parallel sub-network structures, which is a five-input and five-output coupled system. Therefore, the PID neural network decoupling controller is designed as a 10×15×5 three-layer forward network structure, T c_ref , P c_ref 、T core_ref 、N r_ref 、v ref They are the input set values ​​of the PID neural network decoupling controller; KV1, KV2, KV3, v r , T are the input values ​​of the controlled system respectively; T c , P c 、T core 、N r and v are the actual output values ​​of the controlled system; w si is the connection weight from the hidden layer to the output layer; s is the serial number of the parallel sub-network, s = 1, 2, 3, 4, 5; i is the serial number of the hidden layer neuron, i = 1, 2, 3.

[0080] The beetle swarm algorithm in step 3 combines the characteristics of the particle swarm algorithm and the beetle whisker algorithm, and searches for the global optimal solution by simulating the foraging mechanism of the beetle swarm. The specific algorithm process is as follows:

[0081] Initialize the parameters of the beetle population, set the number of beetles in the population, randomly assign the speed, position, and flight direction parameters of each beetle, and define the following fitness function:

[0082]

[0083] Where T is the maximum number of iterations of the beetle swarm algorithm, length is the number of PIDNN training times, n is the number of beetles in the beetle swarm, r is the target signal value, and y is the output of the system.

[0084] The fitness value of each position of the longicorn beetle in the longicorn beetle group is calculated according to the fitness function, and the optimal position of the individual longicorn beetle and the global optimal position are updated according to the fitness value of each position of the longicorn beetle;

[0085] Update the inertia weight:

[0086]

[0087] Where T is the maximum number of iterations, θ t is the inertia weight, θ max and θ min are the maximum and minimum values ​​of θ respectively.

[0088] Update the speed of each beetle:

[0089]

[0090] In the formula, represents the position of the i-th beetle in the t-th iteration of the q-th dimension, represents the speed of the ith beetle in the tth iteration in the qth dimension, is the individual extreme position, is the extreme position of the population, b1 and b2 are two learning factors, z3 and z4 are random numbers in [0,1];

[0091] Update the increment function:

[0092]

[0093] In the formula, represents the increment function, δ t represents the step size in the t-th iteration of the beetle, which decreases as the number of iterations increases and is updated according to the following formula:

[0094] δ t = z1δ t-1 +δ 0

[0095] In the formula, z1 is a positive constant, δ 0 is the initial value of the step length, and the positions of the left and right antennae of each longhorn beetle are updated as follows:

[0096]

[0097]

[0098] Where, d t represents the distance between the two whiskers in the tth iteration;

[0099] Update the position of each longhorn beetle:

[0100]

[0101] In the formula, β is a positive constant. After multiple iterations, the beetle swarm algorithm completes the global search and obtains the global optimal solution.

[0102] In step 4, the PID neural network takes the difference between the output set value and the actual output value of the controlled system as input, and the PID neural network input variables x1, x2, and x3 are expressed in incremental form:

[0103]

[0104] According to the input variables x1, x2, x3 of the PID neural network, the input quantity of the controlled system will be calculated by the following formula:

[0105]

[0106] Where K is the neuron proportional coefficient, K>0; w1, w2, w3 are the neuron connection weights, which are approximately equal to the proportional, integral, and differential coefficients of the PID controller.

[0107] See also Figure 2 In the present invention, the actual output value y of the controlled system will be fed back to the input end of the control system in real time, and the error value e will be obtained by comparing it with the input setting value r of the input end; the error value e is converted into x1, x2, x3 through state conversion, and the output value u is obtained through weighted calculation of w1, w2, w3. The PID neural network structure is continuously trained according to the error value e, and the network connection weight is adjusted to make the error value e continuously reduce until it approaches zero. At this time, the output value u of the PID neural network structure is the correct input that can make the controlled system reach the ideal setting value. The output value u of the PID neural network structure is input into the controller actuator. After a series of execution actions, all the output values ​​y of the controlled system will reach the target setting value r, and the controlled system will achieve the purpose of decoupling control.

[0108] The supervised Hebb learning rule is an algorithm rule that selects the supervised Hebb learning rule as the connection weight, specifically:

[0109]

[0110] Where η p is the proportional learning rate; η i is the integrated learning rate; η d is the differential learning rate;

[0111] The connection weights of the neural network are adjusted in real time through the supervised Hebb learning rule until the input error signal is close to zero. At this time, the input of the controlled system is the correct input. That is, the mutual influence between different control channels is eliminated by adjusting the strength of the proportional, integral and differential effects of the PID neural network. The control goal of achieving that each output parameter of the supercritical carbon dioxide direct cycle nuclear system is affected only by a specific input parameter is achieved.

[0112] When running at full load, the loop circulating medium is heated by the reactor 6, flows into the turbine 10 to expand and do work, and drives the generator 11 to rotate the rotating shaft; then, the loop circulating medium releases part of the heat through the regenerator 7, enters the cooler 8 to cool to the target temperature, enters the compressor 9 for pressurization, and then passes through the regenerator 7 to absorb the heat of the hot side loop circulating medium, and returns to the reactor 6, completing the whole cycle process;

[0113] When operating at partial load, the compressor inlet temperature control system 1 is used to control the cold side feed water flow of the cooler 8 to maintain the constant inlet temperature of the compressor 9; the compressor inlet pressure control system 2 is used to control the filling and discharging capacity of the capacity tank 12 to maintain the constant compressor inlet pressure; the core outlet temperature control system 3 is used to adjust the opening of the third control valve 15 to maintain the constant core outlet temperature; the power control system 4 is used to drive the control drum 16 to ensure that the core output power matches the required power; the drive shaft speed control system 5 is used to adjust the drive shaft auxiliary torque to maintain the drive shaft speed constant.

[0114] See also Figure 3 In order to ensure the critical state of the compressor inlet working fluid, it is necessary to ensure that the compressor inlet working fluid temperature is maintained at the critical temperature. The compressor inlet temperature control system 1 is designed. The compressor inlet temperature set value is subtracted from the measured value of the first temperature sensor, and the difference signal is sent to the PID neural network decoupling controller in the compressor inlet temperature control system 1; the decoupling controller sends its output signal to the first control valve 13.

[0115] In order to ensure the critical state of the working fluid at the compressor inlet, it is necessary to ensure that the working fluid pressure at the compressor inlet is maintained at the critical pressure. The compressor inlet pressure control system 2 is designed. The compressor inlet pressure set value is subtracted from the measured value of the first pressure sensor, and the difference signal is sent to the PID neural network decoupling controller in the compressor inlet pressure control system 2; the decoupling controller sends its output signal to the second control valve 14 of the capacity box.

[0116] In order to ensure that the loop circulation efficiency is as high as possible, it is necessary to ensure that the working medium temperature at the core outlet is kept constant, and a core outlet temperature control system 3 is designed. The core outlet temperature set value is subtracted from the measured value of the second temperature sensor, and the difference signal is sent to the PID neural network decoupling controller in the core outlet temperature control system 3; the decoupling controller sends its output signal to the third control valve 15.

[0117] In order to ensure the stack-following operation strategy, it is necessary to ensure that the reactor power matches the required power in real time. The power control system 4 is designed, and the power setting value is subtracted from the measurement value of the first power sensor. The difference signal is sent to the PID neural network decoupling controller in the power control system 4; the decoupling controller sends its output signal to the control drum 16.

[0118] In order to ensure the power supply quality, it is necessary to ensure that the transmission shaft speed remains constant. A transmission shaft speed control system 5 is designed. The transmission shaft speed set value is subtracted from the measured value of the first speed sensor, and the difference signal is sent to the PID neural network decoupling controller in the transmission shaft speed control system 5; the decoupling controller sends its output signal to the actuator to adjust the transmission shaft auxiliary torque.

[0119] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A decoupling control system for a supercritical carbon dioxide direct cycle nuclear power plant, comprising a reactor (6), wherein the outlet end of a carbon dioxide pipeline of the reactor (6) is connected to the inlet end of a carbon dioxide pipeline of a turbine (10), and the outlet end of the carbon dioxide pipeline of the turbine (10) is returned to the carbon dioxide inlet end of the reactor (6) through the cold side of a regenerator (7), the secondary side of a cooler (8), a compressor (9), and the hot side of the regenerator (7); the turbine shaft of the turbine (10) is connected to the rotating shaft of a generator (11); characterized in that: A first temperature sensor and a compressor inlet temperature control system (1) are provided on the pipeline between the secondary side carbon dioxide outlet of the cooler (8) and the carbon dioxide inlet of the compressor (9), and the signal output end of the first temperature sensor is connected to the signal input end of the compressor inlet temperature control system (1); The compressor inlet temperature control system (1) adjusts the opening of a first control valve (13) arranged at the cold water inlet end of the primary side of the cooler; A first pressure sensor and a compressor inlet pressure control system (2) are provided on the pipeline between the secondary side carbon dioxide outlet of the cooler (8) and the carbon dioxide inlet of the compressor (9); the signal output end of the first pressure sensor is connected to the signal input end of the compressor inlet pressure control system (2); and the signal output end of the compressor inlet pressure control system (2) is connected to the signal end of the second control valve (14) at the outlet end of the capacity box (12); A second temperature sensor is provided on the pipeline between the outlet end of the carbon dioxide pipeline of the reactor (6) and the inlet end of the carbon dioxide of the turbine (10), and the signal end of the second temperature sensor is connected to the signal end of the core outlet temperature control system (3). The core outlet temperature control system (3) adjusts the opening of a third control valve (15) arranged between the cold side inlet end and the hot side outlet end of the regenerator (7); The signal output of the first power sensor on the generator (11) passes through the signal end of the power control system (4), the signal end of the control drum (16) and the signal end of the reactor (6) in sequence, and the power control system (4) drives the control drum (16) to adjust the reactivity; A first speed sensor is provided on the transmission shaft connecting the compressor (9) and the turbine (10), and a signal end of the first speed sensor is connected to a signal end of a transmission shaft speed control system (5), and the transmission shaft speed control system (5) adjusts the auxiliary torque of the transmission shaft; The compressor inlet temperature control system (1), the compressor inlet pressure control system (2), the core outlet temperature control system (3), the power control system (4) and the transmission shaft speed control system (5) are all equipped with a PID neural network decoupling controller, forming a 10×15×5 forward network structure, so as to achieve the control target that each output parameter of the controlled system is only affected by a certain specific input parameter.

2. The decoupling control method of a decoupling control system of a supercritical carbon dioxide direct cycle nuclear power plant according to claim 1, characterized in that: The following steps are involved: Step 1: Building a control system for a supercritical carbon dioxide direct cycle nuclear power plant: a compressor inlet temperature control system (1) maintains a constant compressor (9) inlet temperature by adjusting the flow rate of the cooler (8) on the cold side; a compressor inlet pressure control system (2) maintains a constant compressor (9) inlet pressure by controlling the circulating working fluid of the capacity box (12) in the charging and discharging loop; a core outlet temperature control system (3) maintains a constant reactor (6) outlet temperature by adjusting the opening of a control valve (15); a power control system (4) achieves real-time matching of reactor power and load power by driving the reactivity introduced by the control drum (16); the compressor (9), the turbine (10) and the generator (11) rotate coaxially, and a transmission shaft speed control system (5) maintains a constant transmission shaft speed by adjusting the transmission shaft auxiliary torque; Step 2: The compressor inlet temperature control system (1), the compressor inlet pressure control system (2), the core outlet temperature control system (3), the power control system (4) and the transmission shaft speed control system (5) in step 1 are constructed into a PID neural network decoupling control method with a 10×15×5 forward network structure; Step 3: Based on the PID neural network decoupling control method constructed in step 2, the beetle swarm algorithm is used to globally search for the optimal initial weights of the five control systems: compressor inlet temperature control system (1), compressor inlet pressure control system (2), core outlet temperature control system (3), power control system (4) and transmission shaft speed control system (5); Step 4: When the operating conditions of the supercritical carbon dioxide direct cycle nuclear power system change, in order to ensure the critical state of the working fluid at the compressor inlet, the PID neural network will take the difference between the output set value and the actual output value of the controlled system as input, and update the connection weights of the neurons in real time through the supervised Hebb learning rule, and adjust the strength of the proportional, integral, and differential effects in the system control process in real time, and finally obtain the input of the controlled system, complete the decoupling control of the supercritical carbon dioxide direct cycle nuclear power device, and transform the multivariable strong coupling system into multiple single-input and single-output systems, so as to achieve the control goal that each output parameter of the controlled system is only affected by a specific input parameter.

3. The decoupling control method according to claim 2, characterized in that: The step 2 is constructed into a 10×15×5 forward network structure, that is, the compressor inlet temperature control system (1), the compressor inlet pressure control system (2), the core outlet temperature control system (3), the power control system (4) and the transmission shaft speed control system (5) are five parallel sub-network structures, T c_ref , P c_ref 、T core_ref 、N r_ref 、v ref They are the input set values ​​of the PID neural network decoupling controller; KV1, KV2, KV3, v r , T are the input values ​​of the controlled system respectively; T c , P c 、T core 、N r and v are the actual output values ​​of the controlled system respectively; w si is the connection weight from the hidden layer to the output layer; s is the serial number of the parallel sub-network, s = 1, 2, 3, 4, 5; i is the serial number of the hidden layer neuron, i = 1, 2, 3.

4. The decoupling control method according to claim 2, characterized in that: The specific algorithm process of the beetle swarm algorithm in step 3 is as follows: Initialize the parameters of the beetle population, set the number of beetles in the population, randomly assign the speed, position, and flight direction parameters of each beetle, and define the following fitness function: Where T is the maximum number of iterations of the beetle swarm algorithm, length is the number of PIDNN training times, n is the number of beetles in the beetle swarm, r is the target signal value, and y is the output of the system. The fitness value of each beetle's position in the beetle population is calculated according to the fitness function, and the optimal position of the individual beetle and the global optimal position are updated according to the fitness value of each beetle's position. Update the inertia weight: Where T is the maximum number of iterations, θ t is the inertia weight, θ max and θ min are the maximum and minimum values ​​of θ respectively. Update the speed of each beetle: In the formula, represents the position of the i-th beetle in the t-th iteration of the q-th dimension, represents the speed of the ith beetle in the tth iteration in the qth dimension, is the individual extreme position, is the extreme position of the population, b1 and b2 are two learning factors, z3 and z4 are random numbers in [0,1]; Update the increment function: In the formula, represents the increment function, δ t represents the step size in the t-th iteration of the beetle, which decreases as the number of iterations increases and is updated according to the following formula: d t =z1δ t-1 +d 0 In the formula, z1 is a positive constant, δ 0 is the initial value of the step length, and the positions of the left and right antennae of each longhorn beetle are updated as follows: Where, d t represents the distance between the two whiskers in the tth iteration; Update the position of each longhorn beetle: In the formula, β is a positive constant. After multiple iterations, the beetle swarm algorithm completes the global search and obtains the global optimal solution.

5. The decoupling control method according to claim 2, characterized in that: The PID neural network in step 4 takes the difference between the output set value and the actual output value of the controlled system as input, and the PID neural network input variables x1, x2, and x3 will be expressed in incremental form: According to the input variables x1, x2, x3 of the PID neural network, the input quantity of the controlled system will be calculated by the following formula: Where K is the neuron proportional coefficient, K>0; w1, w2, w3 are the neuron connection weights, which are approximately equal to the proportional, integral, and differential coefficients of the PID controller.

6. The decoupling control method according to claim 2, characterized in that: The supervised Hebb learning rule in step 4 is an algorithm rule that selects the supervised Hebb learning rule as the connection weight, specifically: Where η p is the proportional learning rate; η i is the integrated learning rate; η d is the differential learning rate; The connection weights of the neural network are adjusted in real time through the supervised Hebb learning rule until the input error signal is close to zero. At this time, the input of the controlled system is the correct input. That is, the mutual influence between different control channels is eliminated by adjusting the strength of the proportional, integral and differential effects of the PID neural network.

7. The decoupling control method according to claim 4, characterized in that: In the step 4, the difference between the output set value and the actual output value of the controlled system is used as input. In the five control systems, namely, the compressor inlet temperature control system (1), the compressor inlet pressure control system (2), the core outlet temperature control system (3), the power control system (4) and the transmission shaft speed control system (5), the difference input and the PID neural network decoupling control method are specifically as follows: The compressor inlet temperature setting value of the compressor inlet temperature control system (1) is subtracted from the measurement value of the first temperature sensor, and the difference signal is sent to the PID neural network decoupling controller in the compressor inlet temperature control system (1); the decoupling controller sends its output signal to the first control valve (13); the compressor inlet pressure setting value of the compressor inlet pressure control system (2) is subtracted from the measurement value of the first pressure sensor, and the difference signal is sent to the PID neural network decoupling controller in the compressor inlet pressure control system (2); the decoupling controller sends its output signal to the second control valve (14) of the capacity box; the core outlet temperature setting value of the core outlet temperature control system (3) is subtracted from the measurement value of the second temperature sensor, The difference signal is sent to the PID neural network decoupling controller in the core outlet temperature control system (3); the decoupling controller sends its output signal to the third control valve (15); the power setting value of the power control system (4) is subtracted from the measurement value of the first power sensor, and the difference signal is sent to the PID neural network decoupling controller in the power control system (4); the decoupling controller sends its output signal to the control drum (16); the transmission shaft speed setting value of the transmission shaft speed control system (5) is subtracted from the measurement value of the first speed sensor, and the difference signal is sent to the PID neural network decoupling controller in the transmission shaft speed control system (5); the decoupling controller sends its output signal to the actuator to adjust the auxiliary torque of the transmission shaft.

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