Thermoelectric power generation array reconstruction method suitable for NTD condition in polar region

Abstract

The invention relates to the technical field of thermoelectric power generation, in particular to a thermoelectric power generation array reconstruction method suitable for the NTD condition of a polar region, and the method comprises the following steps: S1, constructing a dual-objective function as a fitness function of a particle swarm algorithm; s2, randomly selecting N arrangement sequences as the positions of N particles; s3, predicting output power and inter-column voltage standard deviation of the thermoelectric power generation array in N arrangement sequences; s4, the fitness of the N arrangement sequences is calculated, and the arrangement sequence with the highest fitness is selected and stored in a solution set; s5, judging whether the number of iterations reaches the maximum number of iterations T or not; s6, updating and selecting N arrangement sequences as positions of N particles after mutation; and S7, selecting an arrangement sequence with the highest fitness from the solution set as a global optimal arrangement sequence. The system solves the problem that a centralized thermoelectric power generation system cannot operate efficiently and stably when applied to the polar region, and is suitable for the polar region.

Description

Technical Field

[0001] This invention relates to the field of thermoelectric power generation technology, specifically a method for reconfiguring thermoelectric power generation arrays suitable for NTD conditions in polar regions. Background Technology

[0002] A centralized thermoelectric power generation system is a system that utilizes the temperature difference between a high-temperature heat source and a low-temperature cold source to drive a thermoelectric power generation array (composed of multiple thermoelectric power generation modules, such as...). Figure 1 As shown, large-scale power generation systems have demonstrated broad application potential in many fields. However, when centralized thermoelectric power generation systems are applied in polar regions, the non-uniform distribution of temperature differences (originating from geothermal energy) causes the thermoelectric power generation array to operate under NTD (non-uniform temperature difference) conditions for extended periods. This results in lower output power and greater inter-array voltage imbalance, hindering efficient and stable system operation. Therefore, it is necessary to develop a thermoelectric power generation array reconfiguration method suitable for NTD conditions in polar regions to address the problem of inefficient and unstable operation of centralized thermoelectric power generation systems in polar areas. Summary of the Invention

[0003] To address the problem of centralized thermoelectric power generation systems failing to operate efficiently and stably in polar regions, this invention provides a thermoelectric power generation array reconfiguration method suitable for NTD conditions in polar regions.

[0004] This invention is achieved using the following technical solution:

[0005] A reconfiguration method for thermoelectric power generation arrays suitable for NTD conditions in polar regions, comprising the following steps:

[0006] Step S1: Construct a dual objective function with the objectives of maximizing the output power of the thermoelectric power generation array and minimizing the standard deviation of the inter-row voltage, and use the dual objective function as the fitness function of the particle swarm algorithm;

[0007] Step S2: Initialize the particle swarm algorithm, i.e. set the number of particles N and the maximum number of iterations T, and then use the particle swarm algorithm to randomly select N arrangement sequences from all arrangement sequences of the thermoelectric power generation array, and use the N arrangement sequences as the positions of N particles.

[0008] Step S3: Collect voltage and current data of the thermoelectric power generation array under N different arrangement sequences, and send the collected results into a pre-trained recurrent neural network. The recurrent neural network predicts the output power and inter-row voltage standard deviation of the thermoelectric power generation array under N different arrangement sequences based on the collected results.

[0009] Step S4: Substitute the prediction results into the fitness function of the particle swarm optimization algorithm to calculate the fitness of N arrangement orders. Then select the arrangement order with the highest fitness as the optimal arrangement order for the current iteration and store it in the solution set.

[0010] Step S5: Determine if the number of iterations has reached the maximum number of iterations T;

[0011] If the number of iterations has not reached the maximum number of iterations T, then proceed to step S6;

[0012] If the number of iterations has reached the maximum number of iterations T, then proceed to step S7;

[0013] Step S6: Perform a mutation operation on N particles, then use the particle swarm optimization algorithm to update and select N arrangement orders from all arrangement orders of the thermoelectric power generation array, and use the N arrangement orders as the positions of the N particles after mutation, and then return to step S3.

[0014] Step S7: Select the arrangement order with the highest fitness from the T optimal arrangement orders contained in the solution set as the global optimal arrangement order. Then, according to the global optimal arrangement order, use the switching matrix to rearrange several thermoelectric power generation modules of the thermoelectric power generation array under the reconstruction constraint, thereby realizing the reconstruction of the thermoelectric power generation array.

[0015] Furthermore, the dual objective function is expressed as:

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] In the formula: This indicates the output power of the thermoelectric generator array; This represents the standard deviation of the inter-row voltage of the thermoelectric array; This indicates the total number of rows in the thermoelectric power generation array; This indicates the total number of columns in the thermoelectric power generation array; Indicates the first Line number The voltage generated by the corresponding module is listed below; Indicates the first Line number List the internal resistance of the corresponding module; Indicates the first Column voltage; This represents the average value of the voltages in each column.

[0021] Furthermore, the recurrent neural network includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer, and an output layer connected in sequence; the first hidden layer has 64 neurons and uses the ReLU function as the activation function; the second hidden layer has 32 neurons and uses L2 regularization for weight decay; the third hidden layer has 16 neurons.

[0022] Furthermore, the ReLU function normalizes and integrates the input data to [0,1] to accelerate convergence, and is expressed as:

[0023] ;

[0024] In the formula: This indicates the input data.

[0025] Furthermore, L2 regularization is expressed as:

[0026] ;

[0027] In the formula: This represents the total loss of the recurrent neural network; This represents the mean squared error loss of a recurrent neural network. This represents the normalization coefficient, set to 0.0001; Represents the first recurrent neural network. Each connection weight parameter; This represents the total number of connection weight parameters in a recurrent neural network.

[0028] Further, in step S6, the mutation operation process is as follows: the N particles are divided into three parts: the first part of the particles do not undergo mutation; the second part of the particles undergo uniform mutation, that is, the position of this part of the particles is updated and changed, and its position is regenerated in the search space; the third part of the particles undergo non-uniform mutation, and its mutation degree gradually decreases as the number of iterations increases.

[0029] Furthermore, the formula for calculating the degree of mutation is as follows:

[0030] ;

[0031] In the formula: Indicates the degree of mutation; This represents the initial mutation probability, set to 0.8; This represents the probability of terminating the mutation, set to 0.4; Indicates the current iteration number; This indicates the maximum number of iterations.

[0032] Furthermore, in step S7, the reconstruction constraint is: when performing the rearrangement operation, the modules in the first row will maintain their original arrangement order, and each module in the remaining rows will only exchange positions with another module in the same row.

[0033] This invention addresses the NTD conditions in polar regions by constructing a dual-objective function and combining particle swarm optimization and recurrent neural networks to reconstruct the thermoelectric power generation array. This effectively increases the output power of the thermoelectric power generation array and reduces the inter-row voltage imbalance, thereby ensuring the efficient and stable operation of the centralized thermoelectric power generation system when applied in polar regions.

[0034] To verify the beneficial effects of this invention, a thermoelectric power generation array under four common NTD conditions (column NTD, row NTD, internal NTD, and external NTD) in polar regions was simulated and reconstructed using Matlab / Simulink software. The resulting temperature difference distribution maps of the thermoelectric power generation array before and after reconstruction were obtained (e.g.,...). Figure 3 , Figure 4 , Figure 7 , Figure 8 , Figure 11 , Figure 12 , Figure 15 , Figure 16 (as shown), voltage-power curve comparison chart (as shown) Figure 5 , Figure 9 , Figure 13 , Figure 17 As shown), voltage-current curve comparison chart (as shown) Figure 6 , Figure 10 , Figure 14 , Figure 18 The table shows the comparison of maximum output power (as shown in Table 1) and minimum inter-column voltage standard deviation (as shown in Table 2).

[0035] Table 1 is as follows:

[0036] .

[0037] Table 2 is as follows:

[0038] .

[0039] pass Figure 3 , Figure 4 , Figure 7 , Figure 8 , Figure 11 , Figure 12 , Figure 15 , Figure 16It can be seen that the thermoelectric power generation array was reconfigured by rearranging several thermoelectric power generation modules (in the figure, the reference temperature difference is set to 70℃, the white modules represent thermoelectric power generation modules that have not deviated from the reference temperature difference, and the gray and black modules represent thermoelectric power generation modules that have deviated from the reference temperature difference).

[0040] pass Figure 5 , Figure 9 , Figure 13 , Figure 17 As can be seen from Tables 1 and 2, the maximum output power of the reconstructed thermoelectric generator array is significantly improved, the minimum inter-column voltage standard deviation is significantly reduced, and the voltage-power curve is a single-peak curve (the original multi-peak phenomenon is eliminated).

[0041] pass Figure 6 , Figure 10 , Figure 14 , Figure 18 It can be seen that the voltage-current curve of the reconstructed thermoelectric generator array is a linear curve (the original multiple inflection points have disappeared).

[0042] This invention effectively solves the problem of centralized thermoelectric power generation systems failing to operate efficiently and stably in polar regions, and is suitable for polar regions. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the thermoelectric power generation array.

[0044] Figure 2 This is a flowchart illustrating the present invention.

[0045] Figure 3 This is a temperature difference distribution diagram before reconfiguration of the thermoelectric power generation array under NTD conditions.

[0046] Figure 4 This is a temperature difference distribution diagram after the thermoelectric power generation array is reconstructed under NTD conditions.

[0047] Figure 5 This is a comparison chart of voltage-power curves before and after reconfiguration of the thermoelectric power generation array under NTD conditions.

[0048] Figure 6 This is a comparison chart of voltage-current curves before and after reconfiguration of the thermoelectric generator array under NTD conditions.

[0049] Figure 7 This is a temperature difference distribution diagram before reconfiguration of the thermoelectric power generation array under NTD conditions.

[0050] Figure 8 This is a temperature difference distribution diagram after the thermoelectric power generation array is reconstructed under NTD conditions.

[0051] Figure 9 This is a comparison chart of voltage-power curves before and after reconfiguration of the thermoelectric generator array under NTD conditions.

[0052] Figure 10 This is a comparison of the voltage-current curves before and after the reconstruction of the thermoelectric generator array under NTD conditions.

[0053] Figure 11 This is a temperature difference distribution diagram before the thermoelectric power generation array is reconfigured under external NTD conditions.

[0054] Figure 12 This is a temperature difference distribution diagram after the thermoelectric power generation array is reconstructed under external NTD conditions.

[0055] Figure 13 This is a comparison chart of voltage-power curves before and after reconfiguration of the thermoelectric power generation array under external NTD conditions.

[0056] Figure 14 This is a comparison chart of voltage-current curves before and after reconfiguration of the thermoelectric generator array under external NTD conditions.

[0057] Figure 15 This is a temperature distribution diagram before the thermoelectric power generation array is reconfigured under internal NTD conditions.

[0058] Figure 16 This is a temperature difference distribution diagram after the thermoelectric power generation array is reconstructed under internal NTD conditions.

[0059] Figure 17 This is a comparison chart of voltage-power curves before and after reconfiguration of the thermoelectric power generation array under internal NTD conditions.

[0060] Figure 18 This is a comparison chart of voltage-current curves before and after reconfiguration of the thermoelectric power generation array under internal NTD conditions. Detailed Implementation

[0061] A reconfiguration method for thermoelectric power generation arrays suitable for NTD conditions in polar regions, comprising the following steps:

[0062] Step S1: Construct a dual objective function with the objectives of maximizing the output power of the thermoelectric power generation array and minimizing the standard deviation of the inter-row voltage, and use the dual objective function as the fitness function of the particle swarm algorithm;

[0063] Step S2: Initialize the particle swarm algorithm, i.e. set the number of particles N and the maximum number of iterations T, and then use the particle swarm algorithm to randomly select N arrangement sequences from all arrangement sequences of the thermoelectric power generation array, and use the N arrangement sequences as the positions of N particles.

[0064] Step S3: Collect voltage and current data of the thermoelectric power generation array under N different arrangement sequences, and send the collected results into a pre-trained recurrent neural network. The recurrent neural network predicts the output power and inter-row voltage standard deviation of the thermoelectric power generation array under N different arrangement sequences based on the collected results.

[0065] Step S4: Substitute the prediction results into the fitness function of the particle swarm optimization algorithm to calculate the fitness of N arrangement orders. Then select the arrangement order with the highest fitness as the optimal arrangement order for the current iteration and store it in the solution set.

[0066] Step S5: Determine if the number of iterations has reached the maximum number of iterations T;

[0067] If the number of iterations has not reached the maximum number of iterations T, then proceed to step S6;

[0068] If the number of iterations has reached the maximum number of iterations T, then proceed to step S7;

[0069] Step S6: Perform a mutation operation on N particles, then use the particle swarm optimization algorithm to update and select N arrangement orders from all arrangement orders of the thermoelectric power generation array, and use the N arrangement orders as the positions of the N particles after mutation, and then return to step S3.

[0070] Step S7: Select the arrangement order with the highest fitness from the T optimal arrangement orders contained in the solution set as the global optimal arrangement order. Then, according to the global optimal arrangement order, use the switching matrix to rearrange several thermoelectric power generation modules of the thermoelectric power generation array under the reconstruction constraint, thereby realizing the reconstruction of the thermoelectric power generation array.

[0071] The biobjective function is expressed as:

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] In the formula: This indicates the output power of the thermoelectric generator array; This represents the standard deviation of the inter-row voltage of the thermoelectric array; This indicates the total number of rows in the thermoelectric power generation array; This indicates the total number of columns in the thermoelectric power generation array; Indicates the first Line number The voltage generated by the corresponding module is listed below; Indicates the first Line number List the internal resistance of the corresponding module; Indicates the first Column voltage; This represents the average value of the voltages in each column.

[0077] The recurrent neural network includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer, and an output layer connected in sequence; the first hidden layer has 64 neurons and uses the ReLU function as the activation function; the second hidden layer has 32 neurons and uses L2 regularization for weight decay; the third hidden layer has 16 neurons.

[0078] The ReLU function normalizes the input data to [0,1] to accelerate convergence, and it is expressed as:

[0079] ;

[0080] In the formula: This indicates the input data.

[0081] L2 regularization is expressed as:

[0082] ;

[0083] In the formula: This represents the total loss of the recurrent neural network; This represents the mean squared error loss of a recurrent neural network. This represents the normalization coefficient, set to 0.0001; Represents the first recurrent neural network. Each connection weight parameter; This represents the total number of connection weight parameters in a recurrent neural network.

[0084] In step S6, the mutation operation process is as follows: N particles are divided into three parts: the first part of the particles does not undergo mutation; the second part of the particles undergoes uniform mutation, that is, the position of this part of the particles is updated and changed, and its position is regenerated in the search space; the third part of the particles undergoes non-uniform mutation, and its mutation degree gradually decreases as the number of iterations increases.

[0085] The formula for calculating the degree of mutation is as follows:

[0086] ;

[0087] In the formula: Indicates the degree of mutation; This represents the initial mutation probability, set to 0.8; This represents the probability of terminating the mutation, set to 0.4; Indicates the current iteration number; This indicates the maximum number of iterations.

[0088] In step S7, the reconstruction constraint is: when performing the rearrangement operation, the modules in the first row will maintain their original arrangement order, and each module in the remaining rows will only exchange positions with another module in the same row.

[0089] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but all such changes and modifications fall within the scope of protection of the present invention.

Claims

1. A method for reconfiguring a thermoelectric power generation array suitable for NTD conditions in polar regions, characterized in that: The method includes the following steps: Step S1: Construct a dual objective function with the objectives of maximizing the output power of the thermoelectric power generation array and minimizing the standard deviation of the inter-row voltage, and use the dual objective function as the fitness function of the particle swarm algorithm; Step S2: Initialize the particle swarm algorithm, i.e. set the number of particles N and the maximum number of iterations T, and then use the particle swarm algorithm to randomly select N arrangement sequences from all arrangement sequences of the thermoelectric power generation array, and use the N arrangement sequences as the positions of N particles. Step S3: Collect voltage and current data of the thermoelectric power generation array under N different arrangement sequences, and send the collected results into a pre-trained recurrent neural network. The recurrent neural network predicts the output power and inter-row voltage standard deviation of the thermoelectric power generation array under N different arrangement sequences based on the collected results. Step S4: Substitute the prediction results into the fitness function of the particle swarm optimization algorithm to calculate the fitness of N arrangement orders. Then select the arrangement order with the highest fitness as the optimal arrangement order for the current iteration and store it in the solution set. Step S5: Determine if the number of iterations has reached the maximum number of iterations T; If the number of iterations has not reached the maximum number of iterations T, then proceed to step S6; If the number of iterations has reached the maximum number of iterations T, then proceed to step S7; Step S6: Perform a mutation operation on N particles, then use the particle swarm optimization algorithm to update and select N arrangement orders from all arrangement orders of the thermoelectric power generation array, and use the N arrangement orders as the positions of the N particles after mutation, and then return to step S3. Step S7: Select the arrangement order with the highest fitness from the T optimal arrangement orders contained in the solution set as the global optimal arrangement order. Then, according to the global optimal arrangement order, use the switching matrix to rearrange several thermoelectric power generation modules of the thermoelectric power generation array under the reconstruction constraint, thereby realizing the reconstruction of the thermoelectric power generation array.

2. The method for reconfiguring a thermoelectric power generation array suitable for NTD conditions in polar regions according to claim 1, characterized in that: The biobjective function is expressed as: ； ； ； ； In the formula: This indicates the output power of the thermoelectric generator array; This represents the standard deviation of the inter-row voltage of the thermoelectric array; This indicates the total number of rows in the thermoelectric power generation array; This indicates the total number of columns in the thermoelectric power generation array; Indicates the first Line number The voltage generated by the corresponding module is listed below; Indicates the first Line number List the internal resistance of the corresponding module; Indicates the first Column voltage; This represents the average value of the voltages in each column.

3. The method for reconfiguring a thermoelectric power generation array suitable for NTD conditions in polar regions according to claim 1, characterized in that: The recurrent neural network includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer, and an output layer connected in sequence; the first hidden layer has 64 neurons and uses the ReLU function as the activation function; the second hidden layer has 32 neurons and uses L2 regularization for weight decay; the third hidden layer has 16 neurons.

4. The method for reconfiguring a thermoelectric power generation array suitable for NTD conditions in polar regions according to claim 3, characterized in that: The ReLU function normalizes the input data to [0,1] to accelerate convergence, and it is expressed as: ； In the formula: This indicates the input data.

5. The method for reconfiguring a thermoelectric power generation array suitable for NTD conditions in polar regions according to claim 3, characterized in that: L2 regularization is expressed as: ； In the formula: This represents the total loss of the recurrent neural network; This represents the mean squared error loss of a recurrent neural network. This represents the normalization coefficient, set to 0.0001; Represents the first recurrent neural network. Each connection weight parameter; This represents the total number of connection weight parameters in a recurrent neural network.

6. The method for reconfiguring a thermoelectric power generation array suitable for NTD conditions in polar regions according to claim 1, characterized in that: In step S6, the mutation operation process is as follows: N particles are divided into three parts: the first part of the particles does not undergo mutation; the second part of the particles undergoes uniform mutation, that is, the position of this part of the particles is updated and changed, and its position is regenerated in the search space; the third part of the particles undergoes non-uniform mutation, and its mutation degree gradually decreases as the number of iterations increases.

7. A method for reconfiguring a thermoelectric power generation array suitable for NTD conditions in polar regions, as described in claim 6, characterized in that: The formula for calculating the degree of mutation is as follows: ； In the formula: Indicates the degree of mutation; This represents the initial mutation probability, set to 0.8; This represents the probability of terminating the mutation, set to 0.4; Indicates the current iteration number; This indicates the maximum number of iterations.

8. The method for reconfiguring a thermoelectric power generation array suitable for NTD conditions in polar regions according to claim 1, characterized in that: In step S7, the reconstruction constraint is: when performing the rearrangement operation, the modules in the first row will maintain their original arrangement order, and each module in the remaining rows will only exchange positions with another module in the same row.

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