A method for optimizing parameters of power systems based on chaotic predator optimization

Abstract

The present invention relates to a power system parameter optimization method based on chaotic predator optimization, which calculates and generates random variable parameters, and calculates the power W corresponding to the variable parameter value. i , and find the optimal power value W max , update the random parameters through the chaotic mechanism, and use the dimensional learning hunting method DLH to generate a domain matrix composed of variable parameter values ​​within the radius of R, that is, a domain matrix composed of a new set of variable parameters, and update the new parameters generated thereby; if W new Greater than W max , then W new The value of W is assigned to max , iteratively completes the output of the optimal power value W of the DSPRO system max The present invention greatly improves the real-time and precision requirements of MPPT control on the DSPRO system, and has obvious improvements compared with the MPPT control method based on the HPO algorithm.

Description

Technical Field

[0001] The invention belongs to a maximum power tracking control algorithm and relates to a power system parameter optimization method based on chaos predator optimization. Background Art

[0002] The traditional methods of maximum power point tracking control (MPPT) are the incremental mass resistance method and the disturbance and observation method that take into account the dynamic changes. However, both methods will reach equilibrium after a certain oscillation and response time, which will result in a large power loss. There are few studies on the maximum power density tracking control of the dynamic renewable energy two-stage pressure reverse osmosis model (DSPRO), and the optimization accuracy of the setting of the optimal operating parameters is not enough. In order to improve the real-time performance of the DSPRO system and enable the system to make real-time adjustments under changing environmental conditions, current research focuses more on the development and improvement of the MPPT control algorithm. In engineering, metaheuristic algorithms have unique advantages in optimizing control methods, especially when dealing with large and complex derivative problems in unknown space. The optimization algorithm can effectively avoid the local optimum and steady-state oscillation problems of the classic MPPT algorithm. The real-time control method based on feedback control does not fully consider the complex dynamic operating environment of the DSPRO system. It is difficult to achieve maximum power point tracking through analytical solutions for complex dynamic systems, and engineering practice has proved that the use of metaheuristic algorithms can enable the system to obtain maximum power in a stable, fast and effective manner in different operating environments. Meta-heuristic algorithms commonly used in engineering include particle swarm optimization (PSO), gray wolf optimization algorithm (GWO), dragonfly algorithm (DA), whale optimization algorithm (WOA), locust optimization method (GOA), Henry gas solubility optimization (HGSO), and predator optimization algorithm (HPO). Among them, HPO has a relatively high convergence speed and good local optimization ability when finding the optimal solution, but it is not easy to balance the two stages of exploration and development, and it is easy to fall into the local optimal solution, resulting in poor convergence and poor global optimization ability, which is difficult to meet the real-time requirements of the DSPRO system. The proposed chaos mechanism (CLS) means the unpredictability of the behavior of complex systems, and mapping means using functions to associate the chaotic behavior in the algorithm with certain parameter mappings. Due to the ergodic and non-repetitive nature of chaos, it can perform global search at a higher speed compared to random search that essentially relies on probability, while Dimension Learning Hunting (DLH) has strong original search capabilities, can balance internal changes between the search and development phases, avoid falling into local optimal solutions, and can build a field of information sharing to improve the accuracy of the solution.

[0003] The characteristic of the predator optimization algorithm is that it simulates the process of animal hunting. By updating the position of the prey, the predator will also adjust its own position (where the position refers to the corresponding variable parameter value, and the solution is the power density). The safe position considered by the prey is the best position considered by the predator. In other metaheuristic algorithms, only one coefficient position is considered, so its local optimization ability is strong and it is easy to fall into the local optimal solution. The relatively strong global random search ability of CLS and DLH can accurately find the global optimal solution. It can be seen that the performance of the MPPT control method based on predator optimization still has room for improvement. Summary of the invention

[0004] Technical issues to be solved

[0005] In order to avoid the shortcomings of the prior art, the present invention proposes a power system parameter optimization method based on chaotic predator optimization to transform the predator algorithm. The performance of the algorithm is improved by combining the advantages of other improvement methods, so that the MPPT control method can obtain better dynamic performance in the application of the DSPRO system. The introduction of chaotic mechanism and dimensional learning hunting method can improve the operation accuracy of the algorithm.

[0006] Technical Solution

[0007] A method for optimizing parameters of a power system based on chaotic predator optimization, characterized by the following steps:

[0008] Step 1: Initialize the power system parameter variables, namely pressure and inlet flow; generate random variable parameters by calculating the following formula:

[0009] x i =lb i +rand×(ub i -lb i ); rand∈[0,1]

[0010] Among them, x i Refers to the parameter value, lb i is the lower bound of the parameter, ub i is the upper bound of the parameter, rand represents a random number ranging from 0 to 1;

[0011] Step 2: Calculate the power W corresponding to the variable parameter value i , and find the optimal power value W max :

[0012] W i =ΔP i ΔQ i

[0013] Where: ΔP i , ΔQ iare the pressure applied to the water, i.e. the reverse osmosis pressure ΔP i and water permeate flow rate ΔQ i ;

[0014] The equilibrium parameter C and the decreasing mechanism parameter K are updated using the following formula: best :

[0015] K best =round(C×N)

[0016]

[0017] Among them: round is the function name; t is the current iteration number, T max is the maximum number of iterations, and N is the number of parameter values;

[0018] Step 3: Update random parameters through chaos mechanism

[0019]

[0020]

[0021]

[0022]

[0023] Calculate the adaptation parameter Z using the chaotic mechanism:

[0024]

[0025] Among them, i+1 =4ζ i ×(1-ζ i );ζ i and are the values ​​of chaos and chaos vector in iteration; R1, R2, R3 and R5 is a random parameter in [0,1], α index is the state parameter;

[0026] When random number Less than β, update the variable parameter value:

[0027] x i,j(t+1) =x i,j (t)+0.5[(2CZP pos(j) -x i,j (t))+(2(1-C)Zμ (j) -x i,j (t))]

[0028] When random number Greater than β, update the variable parameter value:

[0029] x i,j(t+1) =T pos(j) +CZcos(2πR4)×(T pos(j) -x i,j (t))

[0030] Among them, P pos(j) is the parameter value corresponding to the local optimal power, μ is the average value of all parameters, R4 is a random value within [0.1], T pos(j) is the parameter value corresponding to the maximum power of the system;

[0031] Step 4: Use the dimensional learning hunting method DLH to generate a domain matrix composed of variable parameter values ​​within the radius of R, that is, a domain matrix composed of a new set of variable parameters, and update the new parameters generated thereby:

[0032] x i-DLH,j (t+1)=x i,d (t)+rand*(x n,d (t)-x r,d (t))

[0033] Where: x i-DLH,j (t+1) and x i,d (t) represent replaceable variable parameters and current parameter values, respectively, x n,d (t) represents the variable parameter value in the domain matrix, x r,d (t) refers to a randomly selected value within the variable parameter range

[0034] Step 5: Compare the power values ​​(x i,j(t+1) and x i-DLH,j (t+1)), with the highest power W new With W max For comparison, if W new Greater than W max , then W new The value of W is assigned to max Otherwise, return to step 2 and calculate the next X new The corresponding W new ;

[0035] Step 6: When all updated parameter values ​​have been calculated but the maximum number of iterations T has not been reached max If not satisfied, return to step 3 to update C and K best Next iteration; if the maximum number of iterations T is reached max , then the optimal power value W of the DSPRO system is output max .

[0036] The initial chaotic value ζ i is randomly generated in [0,1] and ζ i ≠0.25, 0.5, 0.75, 1.

[0037] The β=0.01.

[0038] Beneficial Effects

[0039] The present invention proposes a power system parameter optimization method based on chaotic predator optimization, which calculates and generates random variable parameters, and calculates the power W corresponding to the variable parameter value. i , and find the optimal power value W max , update the random parameters through the chaotic mechanism, and use the dimensional learning hunting method DLH to generate a domain matrix composed of variable parameter values ​​within the radius of R, that is, a domain matrix composed of a new set of variable parameters, and update the new parameters generated thereby; if W new Greater than W max , then W new The value of W is assigned to max , iteratively completes the output of the optimal power value W of the DSPRO system max The present invention greatly improves the real-time and precision requirements of MPPT control on the DSPRO system, and has obvious improvements compared with the MPPT control method based on the HPO algorithm.

[0040] In the DSPRO system, for the existing technology, the MPPT control technology based on the classic HPO algorithm is prone to fall into the local optimum for the optimization of the optimal power of the DSPRO system, and there is a certain room for improvement. The proposed method balances the detection process and development process of the HPO algorithm by strengthening the global random search capability, improves the accuracy and global search capability of the algorithm, greatly improves the solution quality, and is more effective than the original algorithm.

[0041] The specific contributions of the present invention are as follows:

[0042] (1) Based on the predator optimization algorithm and other excellent improvement methods, an improved predator optimization algorithm (CHPO) is proposed to enhance the dynamic performance of the MPPT control method in the DSPRO system.

[0043] (2) We optimized HPO to obtain the maximum DSPRO power and compared and analyzed the optimization results with the DSPRO process before optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 :Improving the HPO algorithm process DETAILED DESCRIPTION

[0045] The present invention will now be further described with reference to the embodiments and the accompanying drawings:

[0046] For the DSPRO system, the traditional HPO method is not very accurate and often falls into the local optimal solution. The new improved HPO method can effectively improve the global search capability and avoid the generation of local optimal solutions. The CHPO method process improved by the traditional HPO method is as follows Figure 1 As shown (i refers to the number of variable parameters, t is the current iteration number, T max is the maximum number of iterations):

[0047] (1) Input parameters; Initialize variables, namely pressure and inlet flow. Generate random variable parameters by calculation:

[0048] x i =lb i +rand×(ub i -lb i ); rand∈[0,1] (1)

[0049] Among them, x i Refers to the parameter value, lb i is the lower bound of the parameter, ub i is the upper bound of the parameter, and rand represents a random number ranging from 0 to 1.

[0050] (2) Calculate the power W corresponding to the variable parameter value i Find the optimal power value W max , and update the equilibrium parameter C and the decreasing mechanism parameter K using the following formula best

[0051] W i =ΔP i ΔQ i (2)

[0052]

[0053] K best =round(C×N) (4)

[0054] Where ΔP i , ΔQ i They are the pressure applied to the water supply, namely the reverse osmosis pressure and the permeation flow rate of the water. round is the name of the function.

[0055] (3) Update random parameters through chaos mechanism And use the chaotic mechanism to calculate the adaptation parameter Z:

[0056]

[0057]

[0058]

[0059]

[0060]

[0061] Among them, i+1 =4ζ i ×(1-ζ i );ζ i and is the value of chaos and chaos vector in iteration. In addition, the initial chaos value ζ i is randomly generated in [0,1] and ζ i ≠0.25, 0.5, 0.75, 1. R1, R2, R3 and R5 is a random parameter in [0,1]. index is a state parameter.

[0062] By random number Is it less than β (β = 0.01), update the variable parameters in two cases:

[0063] If it is less than β, update the variable parameter value.

[0064] x i,j(t+1) =x i,j (t)+0.5[(2CZP pos(j) -x i,j (t))+(2(1-C)Zμ (j) -x i,j (t)] (10)

[0065] If it is greater than β, update the variable parameter value.

[0066] x i,j(t+1) =T pos(j) +CZcos(2πR4)×(T pos(j) -x i,j (t)) (11)

[0067] Among them, P pos(j) is the parameter value corresponding to the local optimal power, μ is the average value of all parameters, R4 is a random value within [0.1], T pos(j) It is the parameter value corresponding to the maximum power of the system.

[0068] (4) Generate a domain matrix consisting of variable parameter values ​​within a certain radius based on the dimensional learning hunting method (DLH), and update the resulting new parameters:

[0069] x i-DLH,j(t+1)=x i,d (t)+rand*(x n,d (t)-x r,d (t)) (12)

[0070] Among them, x i-DLH,j (t+1) and x i,d (t) represent replaceable variable parameters and current parameter values, respectively, x n,d (t) represents the variable parameter value in the domain matrix, x r,d (t) refers to a randomly selected value within the parameter range of the variable.

[0071] (5) By comparing the power values ​​(x) corresponding to the parameter values ​​updated in (3) and (4) i,j(t+1) and x i-DLH,j (t+1)), select the higher power W new , W new and W max For comparison, if W new Greater than W max , then W new The value of W is assigned to max , otherwise return to the next calculation.

[0072] (6) If all updated parameter values ​​have been calculated, it is necessary to determine whether the maximum number of iterations T has been reached. max If not satisfied, return to the next iteration; if satisfied, output the optimal power value W of the DSPRO system max .

[0073] In a specific embodiment:

[0074] Parameter settings for each algorithm: To ensure the fairness of the experiment, we first need to determine the best parameter settings for each algorithm for the problem.

[0075] Table 1 lists the average power density of the three methods under optimized extraction liquid flow and pressure difference. First, OP1 optimizes the extraction liquid under fixed hydraulic pressure. It can be seen from the power value that the improved HPO method obtains a larger power density, and its performance is better than the traditional method and the HPO method before optimization; OP2 optimizes the hydraulic pressure under fixed extraction liquid flow. It can be seen that the system power density under the optimal hydraulic pressure is significantly improved compared with other methods, and the optimal pressure is also more accurate; OP3 optimizes the hydraulic pressure and extraction liquid flow at the same time. By comparing OP3 with OP1 and OP2, it can be seen that the system power density under the optimal parameters is significantly improved compared with the power density under a single optimized parameter.

[0076] Table 1. Average power density extracted by the MPPT control method based on the CHPO algorithm under optimized extraction liquid flow rate and pressure difference

[0077]

[0078] The maximum power tracking control method proposed in this paper has been extensively tested under various working conditions of the DSPRO system, and the operating results have been compared and analyzed with the MPPT control method of the traditional algorithm and HPO before optimization to verify its reliability. The maximum average power density of the DSPRO system was obtained in all three cases, which is improved to a certain extent compared with the MPPT control method of the traditional algorithm. The experimental results show that the MPPT based on the CHPO algorithm can achieve optimal performance under all environmental conditions and converge to the optimal parameter value, which is better than other algorithms, making the MPPT more real-time and accurate in tracking the maximum power of the DSPRO renewable energy system. It enables the DSPRO system to make real-time adjustments under changing environmental conditions. The results show that CHPO has global optimization capabilities, high optimization accuracy and global search capabilities, which greatly improves the real-time and precision requirements of MPPT for DSPRO system control, and has obvious improvements compared with the MPPT control method based on the HPO algorithm.

Claims

1. A method for optimizing power system parameters based on chaotic predator optimization, characterized in that Here are the steps: Step 1: Initialize the power system parameter variables, namely pressure and inlet flow; generate random variable parameters by calculating the following formula: x i =lb i +rand×(ub i -lb i );rand∈[0,1] Among them, x i Refers to the parameter value, lb i is the lower bound of the parameter, ub i is the upper bound of the parameter, rand represents a random number ranging from 0 to 1; Step 2: Calculate the power W corresponding to the variable parameter value i , and find the optimal power value W max : W i =ΔP i ΔQ i Where: ΔP i , ΔQ i are the pressure applied to the water, i.e. the reverse osmosis pressure ΔP i and water permeate flow rate ΔQ i ; The equilibrium parameter C and the decreasing mechanism parameter K are updated using the following formula: best : K best =round(C×N) Among them: round is the function name; t is the current iteration number, T max is the maximum number of iterations, and N is the number of parameter values; Step 3: Update random parameters through chaos mechanism Calculate the adaptation parameter Z using the chaos mechanism: Among them, i+1 =4ζ i ×(1-ζ i );ζ i and are the values ​​of chaos and chaos vector in iteration; R1, R2, R3 and R5 is a random parameter in [0,1], α index is the state parameter; When random number Less than β, update the variable parameter value: x i,j(t+1) =x i,j (t)+0.5[(2CZP pos(j) -x i,j (t))+(2(1-C)Zμ (j) -x i,j (t))] When random number Greater than β, update the variable parameter value: x i,j(t+1) =T pos(j) +CZcos(2πR4)×(T pos(j) -x i,j (t)) Among them, P pos(j) is the parameter value corresponding to the local optimal power, μ is the average value of all parameters, R4 is a random value within [0.1], T pos(j) is the parameter value corresponding to the maximum power of the system; Step 4: Use the dimensional learning hunting method DLH to generate a domain matrix composed of variable parameter values ​​within the radius of R, that is, a domain matrix composed of a new set of variable parameters, and update the new parameters generated thereby: x i-DLH,j (t+1)=x i,d (t)+rand*(x n,d (t)-x r,d (t)) Where: x i-DLH,j (t+1) and x i,d (t) represent replaceable variable parameters and current parameter values, respectively, x n,d (t) represents the variable parameter value in the domain matrix, x r,d (t) refers to a randomly selected value within the variable parameter range Step 5: Compare the power values ​​(x i,j(t+1) and x i-DLH,j (t+1)), with the highest power W new With W max For comparison, if W new Greater than W max , then W new The value of W is assigned to max Otherwise, return to step 2 and calculate the next X new The corresponding W new ; Step 6: When all updated parameter values ​​have been calculated but the maximum number of iterations T has not been reached max If not satisfied, return to step 3 to update C and K best Next iteration; if the maximum number of iterations T is reached max , then the optimal power value W of the DSPRO system is output max .

2. The method for optimizing power system parameters based on chaotic predator optimization according to claim 1, characterized in that: Initial chaos value ζ i is randomly generated in [0,1] and ζ i ≠0.25, 0.5, 0.75, 1.

3. The method for optimizing power system parameters based on chaotic predator optimization according to claim 1, characterized in that: The β=0.01.

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