Hydropower station technology water supply pipe network auxiliary equipment hydraulic heat dissipation system optimization arrangement method based on improved non-dominated sorting whale algorithm

Abstract

The invention discloses a hydropower station technical water supply pipe network auxiliary equipment hydraulic heat dissipation system optimization arrangement method based on an improved non-dominated sorting whale algorithm, which comprises the following steps: firstly, constructing a hydropower station technical water supply pipe network structure, and designing and building a hydraulic system meeting water supply cooling requirements; establishing a running state decision model of the auxiliary pump, judging whether the pump needs to be started or not, and controlling the running efficiency of the pump in combination with variable-frequency speed regulation; establishing an optimization target model, including minimizing pipeline wear, maximizing cooling effect and maintaining system water pressure stability, and setting hydraulic constraint conditions at the same time; iteratively optimizing the system layout through an improved non-dominated sorting whale optimization algorithm, and screening out an optimal solution; the optimization effect is verified through analogue simulation, and elbow abrasion, flow velocity and water pressure are evaluated; the operation efficiency of a hydropower station technology water supply pipe network can be improved, pipeline abrasion is reduced, the layout of a hydraulic cooling system is optimized, and the system reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optimizing the layout of the technical water supply network of a hydropower station, and specifically to an optimization layout method for the hydraulic cooling system of auxiliary equipment of the technical water supply network of a hydropower station based on an improved non-dominated sorting whale algorithm. Background Art

[0002] In the water supply system of a hydropower station, the technical water supply network not only needs to ensure a stable water flow supply but also undertakes the cooling task of some equipment. However, the hydraulic cooling efficiency of the existing technical water supply network system of hydropower stations is relatively low. Especially in the auxiliary cooling system of electromechanical equipment, common cooling problems significantly affect the operation stability and service life of the equipment. The main problems include insufficient cooling effect, serious pipeline wear, and insufficient system pressure, etc. In view of these problems, it is urgent to systematically improve the layout method of the water supply network, the cooling system, and the optimization strategy.

[0003] Firstly, when the existing water supply system is working at high load, insufficient cooling often occurs due to the unreasonable layout of the cooling system. Secondly, the pipeline wear is proportional to the square of the flow velocity. Especially in the pipeline after the filter screen, the impact of impurities aggravates the wear, increases the maintenance cost, and reduces the cooling efficiency of the system. Finally, when the water pressure difference between the upper and lower parts of the system is too large, the efficiency of water flow transmission to the cooling system drops significantly, and the cooling effect is not good. The traditional direct pipeline design cannot effectively solve this problem. Summary of the Invention

[0004] The present invention provides an optimization layout method for the hydraulic cooling system of auxiliary equipment of the technical water supply network of a hydropower station based on an improved non-dominated sorting whale algorithm, which solves the problems that when the water system is working at high load, insufficient cooling often occurs due to the unreasonable layout of the cooling system, the impact of impurities aggravates the wear, increases the maintenance cost and reduces the cooling efficiency of the system, and when the water pressure difference between the upper and lower parts of the system is too large, the efficiency of water flow transmission to the cooling system drops significantly, and the cooling effect is not good.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: An optimization layout method for the hydraulic cooling system of auxiliary equipment of the technical water supply network of a hydropower station based on an improved non-dominated sorting whale algorithm, comprising the following steps: Step 1, construct the structure of the technical water supply network of the hydropower station: According to the specific hydraulic cooling requirements of the technical water supply network system of the hydropower station, design and construct the structure of the technical water supply network of the hydropower station, including parallel direct pipelines and pressurized pipelines, to ensure that the subsequent layout supports the best water distribution and cooling efficiency; Step 2, establish a decision-making model for the operating state of the auxiliary pump: According to the real-time hydraulic conditions of the technical water supply network of the hydropower station, evaluate whether it is necessary to start the auxiliary pump for operation; Step 3, establish the target optimization model and constraints: By combining the flow velocity equation, energy equation, and pressure drop equation, construct a mathematical model of the water supply system, set up an objective function optimization target of minimizing pipeline wear, maximizing cooling effect, and maintaining the minimum water pressure, and set system flow rate, water pressure, and pump energy consumption constraints to ensure that the optimization results meet the requirements of the actual system operating conditions; Step 4, obtain the optimal system layout plan: Update and iteratively optimize the system layout plan through the improved non-dominated sorting whale optimization algorithm, introduce an adaptive weight coefficient to balance the global search and local search of the algorithm, determine the Pareto front through non-dominated sorting, sort the solution set that meets the optimization target, and combine the crowding degree calculation to ensure the uniform distribution and diversity of the solutions, and screen out the optimal layout plan that meets the objective function and constraints; Step 5, verify and improve the optimization plan: Evaluate the optimized technical water supply network of the hydropower station through system simulation, analyze the key parameters of the optimized flow distribution, elbow wear, flow velocity, and water pressure to ensure that the system can operate efficiently in the long term and meet the requirements of engineering design.

[0006] Preferably, the auxiliary pump operation state decision model in Step 2 is: If the current pressure H actual can meet the requirements of the cooling system H required , the system can operate in direct current without starting the pump, and the system operates in direct current; If the current pressure H actual cannot meet the requirements of the cooling system H required when, the system needs to start the centrifugal pump and pressurize. After the centrifugal pump is started, the system will determine whether to increase the water flow according to the tail water temperature T outlet To meet the hot water cooling demand, when the temperature is too high, the pump speed needs to be increased by variable frequency speed regulation while keeping the head constant to n pump achieve the purpose of increasing the flow rate, and keep the current flow rate when the temperature is normal.

[0007] Preferably, the target optimization model and constraints in Step 3 are: Under energy consumption constraints, flow velocity constraints, and hydraulic balance constraints, minimize pipeline wear, maximize cooling effect, and maintain stable system water pressure; specifically, minimize pipeline wear by controlling the pipeline flow velocity, adjust the flow velocities of the upper guide bearing, thrust bearing, and air cooler branch to maximize the cooling effect, and at the same time ensure that the current water pressure meets the requirement of the minimum water pressure for the system to promote water circulation by adjusting the pump power, so as to achieve the optimization of hydraulic balance and system efficiency.

[0008] More preferably, in the target optimization model of step 3, the objective function for minimizing pipeline wear is: ; where v i is the flow velocity in the i th branch, D i is the pipeline diameter. Wear is proportional to the square of the flow velocity. A smaller flow velocity can reduce the impact and wear of impurities on the pipeline.

[0009] Furthermore, in the target optimization model of step 3, the objective function for maximizing the cooling effect is: ; where v 2, v 3, v 4 are the branch flow velocities of the upper guide bearing, thrust bearing, and air cooler respectively. The cooling effect is proportional to the flow velocity. The water flow velocity needs to be maximized in the cooling branch to ensure the best cooling effect for each cooling component.

[0010] Even further, in the target optimization model of step 3, the objective function for maintaining the minimum water pressure is: ; where P pump is the power of the pump, H required is the minimum water pressure requirement for the thrust bearing and air cooler, H actual is the current water pressure height; maintaining the system water pressure H actual meets the minimum water pressure requirements at the thrust bearing and air cooler H required , and by adjusting the power of the pump P pump ensures the balance between system efficiency and water pressure requirements.

[0011] Specifically, in the constraint conditions of step 3, the pump energy consumption constraint model is: ; where E pump is the total energy consumption of the pump, t is the operating time of the pump, E max is the energy consumption upper limit. The energy consumption of the pump cannot exceed the design limit of the system; the constraint model of flow velocity and flow rate is: Q i=A i · v i , where A i is the cross-sectional area of the i th branch, v i is the flow velocity; The final optimization objective function of step 3 is: ; where f 1 (v) is the objective function for minimizing pipeline wear, f 2 (v) is the objective function for maximizing the cooling effect, f 3 (P, H) is the objective function for maintaining the minimum water pressure.

[0012] Preferably, the position update mechanism of step 4 balances the algorithm between global search and local search by introducing an adaptive weight coefficient to improve the diversity of solutions and search efficiency during the optimization process. Its adaptive weight equation is: ; where w min is the minimum weight, with a value of 0.3, w max is the maximum weight, with a value of 0.9, t max is the total number of iterations, t is the current number of iterations.

[0013] More preferably, the initial position in step 4 is generated by the Sine-Tent-Cosine chaotic mapping function, and its formula is: ; Applying the composite chaotic mapping to the population initialization of the whale optimization algorithm can make the initial population distribution more uniform, thereby improving the algorithm performance.

[0014] A hydraulic cooling system for auxiliary equipment of a technical water supply network in a hydropower station based on an improved non-dominated sorting whale algorithm is used for the implementation of an optimization layout method for the hydraulic cooling system of auxiliary equipment of a technical water supply network in a hydropower station based on the improved non-dominated sorting whale algorithm, and a technical water supply network structure of the hydropower station is constructed according to the system, including a water intake pool. The water intake pool is connected in parallel with a pressurization pipeline and a filtration pipeline through a gravity pipeline. The pressurization pipeline includes at least two parallel variable-frequency pressurization pumps. The filtration pipeline includes at least two parallel water filters. After the water outlet of the filtration pipeline is branched, it is respectively introduced into a water guide bearing pipeline and a four-way reversing valve. The four-way reversing valve is respectively connected with a thrust bearing pipeline, an upper guide bearing pipeline and an air cooler. The water guide bearing pipeline, the thrust bearing pipeline, the upper guide bearing pipeline and the air cooler finally lead to a drainage pool.

[0015] Advantages of the present invention: To solve the problem of insufficient water pressure, the present invention introduces an operation state decision model for auxiliary pumps. By real-time monitoring the water pressure of the water supply network, it judges whether to start the auxiliary pump and adjusts the operation state of the pump according to the need, ensuring pressurization when the water pressure is insufficient and stopping the pump when the water pressure is sufficient, so as to maintain efficient operation. At the same time, the system layout is optimized by the improved non-dominated sorting whale optimization algorithm, further improving the overall operation efficiency of the technical water supply network in the hydropower station, reducing pipeline wear, and ensuring the stability and durability of the cooling system. The following improvements are also introduced: (1) Chaotic mapping mechanism: used for population initialization, enhancing the diversity and uniform distribution of the solution set, thereby improving the global exploration ability of the algorithm in the search space. (2) Adaptive weight control: used for dynamically balancing between global search and local exploitation, enabling the algorithm to maintain extensive search for solutions and quickly converge in different stages, improving the convergence efficiency. (3) Crowding degree calculation mechanism: effectively preventing the over-aggregation of the solution set, maintaining the sparsity and uniform distribution of the Pareto front solution set, and ensuring the diversity of the final optimization result. Through these improvements, the optimization method can significantly improve the operation efficiency of the technical water supply network system in the hydropower station, reduce pipeline wear, enhance the cooling effect, and ensure the stability of the system water pressure in each branch. Compared with the traditional water supply pipeline system, the present invention performs excellently in terms of flexibility and intelligent control, is suitable for the technical update and transformation of the technical water supply network in the hydropower station, and has broad engineering application prospects and economic benefits. Description of the drawings

[0016] Figure 1 is the main technical route of the method of the present invention; Figure 2It is a schematic diagram of the connection of the water supply pipe network structure system used in the method of the present invention for hydropower stations; Figure 3 It is a flowchart of the improved non-dominated sorting whale algorithm used in the method of the present invention; Figure 4 It is a comparison chart of pipeline wear under different flow rates after optimization of the method of the present invention. Specific implementation manners

[0017] As follows, the embodiments will be further described with reference to the accompanying drawings.

[0018] As Figure 1 shown, as a preferred Embodiment 1, an optimization layout method for the hydraulic cooling system of auxiliary equipment of the water supply pipe network of a hydropower station based on an improved non-dominated sorting whale algorithm includes the following steps: Step 1, construct the water supply pipe network structure of the hydropower station technology. According to the specific hydraulic cooling requirements of the water supply pipe network system of the hydropower station technology, design and construct the water supply pipe network structure of the hydropower station technology to ensure that the subsequent layout supports the best water distribution and cooling efficiency; the water supply pipe network structure of the hydropower station technology includes a water intake pool, a drainage pool, a water guide bearing, an upper guide bearing, a thrust bearing, an air cooler, a four-way reversing valve, a terminal control cabinet, a variable frequency booster pump, a water filter, and a valve.

[0019] The water supply pipe network structure of this system is as Figure 2 shown, which includes a water intake pool. The water intake pool is connected in parallel with a pressurization pipeline and a filtration pipeline through a gravity pipeline. The pressurization pipeline includes at least two parallel variable frequency booster pumps. The filtration pipeline includes at least two parallel water filters. The water outlet of the filtration pipeline is branched and respectively leads to the water guide bearing pipeline and the four-way reversing valve. The four-way reversing valve is respectively connected to the thrust bearing pipeline, the upper guide bearing pipeline, and the air cooler. The water guide bearing pipeline, the thrust bearing pipeline, the upper guide bearing pipeline, and the air cooler finally lead to the drainage pool.

[0020] The water supply pipe network system of the hydropower station technology extracts water from the water intake pool and passes through the first variable frequency booster pump and the second variable frequency booster pump (one is the main pump and the other is the standby pump). When the water flow pressure is sufficient, the water can flow freely through the pipeline without starting the booster pump; but if the water flow pressure is insufficient, the variable frequency booster pump starts to adjust the inlet pressure to ensure that the water can reach the cooling components smoothly. Then the water flows through the water filters in one-use-one-backup mode for impurity filtration to ensure that the water quality meets the standards and reduce the wear of the pipeline and equipment. Then, the water passes through the four-way reversing valve and is distributed to the water guide bearing, the thrust bearing, the upper guide bearing, and the air cooler as needed to complete the cooling task. Finally, the water flows together and enters the drainage pool to complete the entire heat dissipation.

[0021] Step 2: Establish a decision-making model for the operating state of the auxiliary pump. According to the real-time hydraulic conditions of the technical water supply network of the hydropower station, evaluate whether it is necessary to start the auxiliary pump, and combine the variable frequency speed control to control the operating efficiency of the pump.

[0022] (1) Pump pressurization judgment model The system first evaluates whether the current water pressure is sufficient. If the current pressure H actual can meet the requirements of the cooling system H required , the system can operate directly without starting the pump: When H actual H required is satisfied, the pump is not started and the system operates directly; When the system pressure is insufficient, the system needs to start the centrifugal pump and pressurize: When H actual <H required is satisfied, the centrifugal pump is started.

[0023] (2) Pump variable frequency speed control model After the centrifugal pump is started, the system will determine whether it is necessary to increase the water flow according to the tail water temperature T outlet . To meet the hot water cooling demand, when the head is kept constant, the water flow needs to be increased when the temperature is too high, and the current flow rate is maintained when the temperature is normal. Set a critical temperature T threshold . When the tail water temperature is higher than this value, the system needs to increase the flow rate Q required : If the tail water temperature exceeds the critical temperature threshold, that is T outlet > T threshold , when the head is kept constant, when increasing the flow rate, the pump speed needs to be increased by variable frequency speed control n pump : , increase the pump speed n pump The relationship between the pump speed and the flow rate can be obtained by the similarity law of the centrifugal pump: , therefore, the new pump speed n new can be calculated by the ratio of the desired flow rate Q required and the initial flow rate Q initial : ; If T outle ≤ T threshold , then there is no need to increase the flow rate, and the current pump speed is maintained: n new = n initial 。

[0024] Step 3, establish the target optimization model and constraint conditions. By combining the flow velocity equation, energy equation, pressure drop equation, etc., construct a mathematical model of the water supply system, set optimization goals such as minimizing pipeline wear, maximizing cooling effect, and maintaining the minimum water pressure, and set constraint conditions such as system flow rate, water pressure, and pump energy consumption to ensure that the optimization results meet the requirements of the actual working conditions of the system; In the target optimization model, the objective function for minimizing pipeline wear is: , where v i is the flow velocity in the i th branch, D i is the pipeline diameter. Wear is proportional to the square of the flow velocity. A smaller flow velocity can reduce the impact and wear of impurities on the pipeline; In the target optimization model, the objective function for maximizing the cooling effect is: , where v 2 、 v 3 、v 4 are the flow velocities of the upper guide bearing, thrust bearing, and air cooler branches respectively. The cooling effect is proportional to the flow velocity. The water flow velocity needs to be maximized in the cooling branches to ensure that each cooling component reaches the best cooling effect; In the target optimization model, the objective function for maintaining the minimum water pressure is: , where P pump is the power of the pump, H required is the minimum water pressure requirement for the thrust bearing and air cooler, H actual is the current water pressure height; when the system needs to increase the water pressure, the pump must provide sufficient power; when the water pressure is sufficient, the pump can reduce the output and operate efficiently. By adjusting the power of the pump, balance the difference between the water pressure demand and the actual water pressure in the system, so as to optimize the operation efficiency of the pump while ensuring the water pressure demand of the system.

[0025] In the hydraulic constraint conditions, the pump energy consumption constraint model is: , where E pump is the total energy consumption of the pump, t is the operation time of the pump, Emax It is the upper limit of energy consumption. The energy consumption of the pump cannot exceed the design limit of the system; The constraint model of flow rate and flow rate is: Q i =A i · v i ， in A i It is i The cross-sectional area of ​​the branch, v i is the flow rate.

[0026] The constraint model of hydraulic balance constraint is: ,in Q input is the total input flow, Q 1 、 Q 2 、Q 3 、Q 4 is the flow rate of the four branches, and the total flow rate of water remains unchanged.

[0027] The final optimization objective function of the system is: ,in f 1 (v), f 2 (v), f 3 (P, H) They are respectively the above-mentioned function of minimizing pipe wear, function of maximizing cooling effect, and function of maintaining minimum water pressure.

[0028] Step 4: Obtain the optimal system layout plan. Update and iteratively optimize the system layout plan through the improved non-dominated sorting whale optimization algorithm, combine non-dominated sorting with congestion calculation, and screen out the optimal layout plan that meets the objective function and constraints; The process of optimizing the system layout scheme by using the improved non-dominated sorting whale optimization algorithm includes: In the Whale Optimization Algorithm (WOA), each whale position represents a potential solution, and the goal is to approximate the global optimal solution by simulating the behavior of humpback whales hunting prey. The hunting behavior of whales can be divided into three main steps: surrounding the prey, attacking the prey with a bubble net, and searching for the prey.

[0029] (1) Surrounding the prey: During the iteration process, the whale will gradually shrink the encirclement to approach the prey (i.e. the optimal solution). The mathematical model is:

[0030] t represents the current iteration number, and Indicated in t The position of the best candidate solution before the generation, and Respectively t The solution for the first and next generation, A and C represents the coefficient, r 1 and r 2 is a random number in (0,1), T max is the maximum number of iterations.

[0031] (2) Bubble net predation: When hunting, humpback whales often use spiral motion and bubble net predation to approach prey. The mathematical model is:

[0032] b is a constant, l is a random number in (-1,1). D p Indicates the distance between the whale and its prey. In the process of predation, the spiral rise and the shrinking of the encirclement are carried out simultaneously, and the mathematical model is: ,in p is a random number between 0 and 1.

[0033] (3) Searching for prey: When the coefficient vector |A|>1, the whale conducts a global search and randomly selects a location to search. The mathematical model of this behavior is: ,in, X rand Represents the position of a random whale in the population.

[0034] As a preferred embodiment 2, although the traditional whale optimization algorithm (WOA) shows good performance in global search and local development, it still has some shortcomings. In order to further enhance the global search ability and solution diversity of the algorithm, multiple improvements such as adaptive weight mechanism, chaotic mapping, non-dominated sorting and congestion calculation are introduced. Figure 3 This is a flowchart of the improved non-dominated sorting whale algorithm used in the present invention. Next, these improvements will be introduced one by one: (1) Initialization of chaotic mapping: The initial position is generated by the Sine-Tent-Cosine chaotic mapping function, and its formula is:

[0035] Applying compound chaotic mapping to the population initialization of the whale optimization algorithm can make the initial population distribution more uniform, thereby improving the algorithm performance.

[0036] (2) Adaptive weight mechanism: By introducing an adaptive weight coefficient, the algorithm balances between global search and local search to improve the diversity of solutions and search efficiency during the optimization process. Its adaptive weight equation is: , where w min is the minimum weight, with a value of 0.3, w max is the maximum weight, with a value of 0.9, t max is the total number of iterations, t is the current iteration number. Therefore, the updated formula for the whale position after improvement is shown as follows:

[0037] (3) Non-dominated sorting: In multi-objective optimization, the quality of solutions is distinguished by hierarchical sorting. The basic steps include: calculating the number of individuals dominated by each individual and the set of individuals it dominates; forming the first-layer Pareto front set with all non-dominated individuals; updating the number of dominated individuals, and if an individual is no longer dominated, it is assigned to the next layer until all individuals are stratified. Through this method, the non-dominated sorting algorithm can eliminate individuals of lower ranks according to the dominance relationship and retain better solutions.

[0038] (4) Crowding degree calculation: In Pareto optimization, the selection of non-dominated solutions not only considers their dominance levels but also the distribution density of the solutions. The crowding degree reflects the sparsity of the distribution of solutions in the solution set. The steps for calculating the crowding degree are as follows: 1) Initialize the crowding distance of each individual to 0; 2) For the individuals in each Pareto front layer, sort them in ascending order according to the values of each objective function, and record the maximum and minimum values of each objective function; 3) Set the crowding distances of the individuals with the maximum and minimum objective function values in the Pareto front layer to infinity; 4) For the remaining individuals, calculate according to the differences in the objective function values of their adjacent individuals before and after. The crowding degree formula is: , where , A[i + 1].n and A[i - 1].n respectively represent the objective function values of the adjacent solutions before and after of individual i on the n th objective function, and are the maximum and minimum values of this objective function.

[0039] The goals that the above method can achieve include: on the premise of ensuring the minimum system energy consumption, achieving the optimal balance between pipeline wear and cooling effect. By optimizing the flow velocity of the pipeline, it is possible to ensure the minimum wear inside the pipeline, while adjusting the water flow velocity of the cooling branch to ensure the maximum cooling effect of the upper guide bearing, thrust bearing and air cooler. In addition, the system water pressure is stabilized by reasonably adjusting the power of the pump, realizing the efficient operation and hydraulic balance of the system. In short, through the comprehensive optimization of various goals, this method can achieve the system optimization effect of the best cooling effect and the minimum pipeline wear on the premise of meeting the energy consumption constraint and hydraulic balance.

[0040] Step 5, verify and improve the optimization plan. Evaluate the optimized technical water supply network of the hydropower station through system simulation, and analyze key parameters such as the optimized flow distribution, elbow wear, flow velocity and water pressure to ensure that the system can operate efficiently in the long term and meet the engineering design requirements.

[0041] As a preferred Example 3, Figure 4 It is a comparison chart of pipeline wear under different flows after optimization, as Figure 4 shown: when the flow rate is 160 kg / s and the rotational speed is 1500 rpm, the wear is mainly concentrated at the inlet of the elbow, the wear rate is relatively low, and the overall distribution is relatively uniform, but the flow rate is small; When the flow rate is 185 kg / s and the rotational speed is 1500 rpm, the wear density at the elbow slightly increases, but it is still within the acceptable range, and this flow rate can also ensure the cooling effect of the system; When the flow rate is 205 kg / s and the rotational speed is 1500 rpm, the wear increases significantly at this time, especially at the elbow, and the wear rate reaches the highest value. Long-term operation will lead to serious wear problems.

[0042] Based on the above analysis, a flow rate of 185 kg / s is selected as the optimization plan. This flow rate ensures the cooling effect of the system while having moderate wear, achieving the best balance between the cooling effect and pipeline wear. After further optimization by the non-dominated sorting whale optimization algorithm (NSWOA), the system water pressure is maintained within the minimum required range, and it is also verified that this flow rate can achieve the goals of maximizing system efficiency and minimizing pipeline wear. The method of the present invention successfully improves the overall operation efficiency of the technical water supply network of the hydropower station, ensuring the stable and efficient operation of the system and meeting the engineering design requirements.

Claims

1. An optimization layout method for the hydraulic heat dissipation system of auxiliary equipment in the technical water supply network of a hydropower station based on an improved non-dominated sorting whale algorithm, characterized in that, It includes the following steps: Step 1, construct the technical water supply pipeline network structure of the hydropower station: According to the specific hydraulic cooling requirements of the technical water supply pipeline network system of the hydropower station, design and construct the technical water supply pipeline network structure of the hydropower station, including parallel DC pipelines and pressurized pipelines, to ensure that the subsequent layout supports the best water distribution and cooling efficiency; Step 2, establish a decision-making model for the operating state of the auxiliary pump: According to the real-time hydraulic conditions of the technical water supply pipeline network of the hydropower station, evaluate whether it is necessary to start the operation of the auxiliary pump; Step 3, establish an objective optimization model and constraint conditions: By combining the flow velocity equation, energy equation, and pressure drop equation to construct a mathematical model of the water supply system, set up an objective function optimization goal of minimizing pipeline wear, maximizing cooling effect, and maintaining the minimum water pressure, and set system flow rate, water pressure, and pump energy consumption constraint conditions to ensure that the optimization results meet the requirements of the actual working conditions of the system; Step 4, obtain the optimal layout plan of the system: Update and iteratively optimize the system layout plan through the improved non-dominated sorting whale optimization algorithm, introduce an adaptive weight coefficient to balance the algorithm between global search and local search, determine the Pareto front through non-dominated sorting, sort the solution set that meets the optimization goal, and combine the crowding degree calculation to ensure the uniform distribution and diversity of the solutions, and screen out the optimal layout plan that meets the objective function and constraint conditions; Step 5, verify and improve the optimization plan: Evaluate the optimized technical water supply pipeline network of the hydropower station through system simulation, analyze the key parameters of the optimized flow distribution, elbow wear, flow velocity, and water pressure to ensure that the system can operate efficiently in the long term and meet the engineering design requirements.

2. An optimization layout method for the hydraulic heat dissipation system of auxiliary equipment in the technical water supply network of a hydropower station based on an improved non-dominated sorting whale algorithm, characterized in that, The decision-making model for the operating state of the auxiliary pump in Step 2 is: If the current pressure H actual can meet the requirements of the cooling system H required , the system can operate directly without starting the pump, and the system operates directly; If the current pressure H actual cannot meet the requirements of the cooling system H required then the system needs to start the centrifugal pump and pressurize it. After the centrifugal pump is started, the system will determine whether to increase the water flow according to the tail water temperature T outlet To meet the hot water cooling demand, when the temperature is too high, the pump speed needs to be increased by variable frequency speed regulation while keeping the head constant n pump to achieve the purpose of increasing the flow rate, and when the temperature is normal, the current flow rate is maintained.

3. The optimized layout method of the hydraulic heat dissipation system of the auxiliary equipment for the technical water supply network of a hydropower station based on an improved non-dominated sorting whale algorithm according to claim 1, characterized in that, The objective optimization model and constraint conditions in Step 3 are: Under the constraints of energy consumption, flow velocity, and hydraulic balance, minimize pipeline wear, maximize cooling effect, and maintain the stability of the system water pressure; specifically, minimize pipeline wear by controlling the pipeline flow velocity, adjust the flow velocities of the upper guide bearing, thrust bearing, and air cooler branch to maximize the cooling effect, and at the same time ensure that the current water pressure meets the requirement of the minimum water pressure for the system to promote water circulation by adjusting the pump power, so as to achieve the optimization of hydraulic balance and system efficiency.

4. A method for optimizing the layout of a hydraulic heat dissipation system for auxiliary equipment of a technical water supply network in a hydropower station based on an improved non-dominated sorting whale algorithm, characterized in that, In the objective optimization model of Step 3, the objective function for minimizing pipeline wear is: ； Among them v i is the flow velocity in the i th branch, D i is the pipe diameter. Wear is proportional to the square of the flow velocity. A smaller flow velocity can reduce the impact and wear of impurities on the pipe.

5. An optimization layout method for the hydraulic heat dissipation system of auxiliary equipment in the technical water supply network of a hydropower station based on an improved non-dominated sorting whale algorithm, characterized in that, In the objective optimization model of Step 3, the objective function for maximizing cooling effect is: ； Among them v 2、 v 3、 v 4 are the branch flow velocities of the upper guide bearing, thrust bearing, and air cooler respectively. The cooling effect is proportional to the flow velocity. It is necessary to maximize the water flow velocity in the cooling branch to ensure that each cooling component achieves the best cooling effect.

6. An optimization layout method for the hydraulic heat dissipation system of auxiliary equipment in the technical water supply network of a hydropower station based on an improved non-dominated sorting whale algorithm, characterized in that, In the objective optimization model of Step 3, the objective function for maintaining the minimum water pressure is: ； Among them P pump is the power of the pump, H required is the minimum water pressure requirement for the thrust bearing and the air cooler, H actual is the current water pressure height; maintaining the system water pressure H actual meets the minimum water pressure requirements at the thrust bearing and the air cooler H required , and by adjusting the power of the pump P pump ensures the balance between system efficiency and water pressure demand.

7. An optimization layout method for the hydraulic heat dissipation system of auxiliary equipment in the technical water supply network of a hydropower station based on an improved non-dominated sorting whale algorithm, characterized in that, In the constraint conditions of Step 3, the pump energy consumption constraint model is: ； Among them E pump is the total energy consumption of the pump, t is the operating time of the pump, E max is the upper limit of energy consumption, and the energy consumption of the pump cannot exceed the design limit of the system; the constraint models of flow velocity and flow rate are as follows: Q i =A i · v i where A i is the cross-sectional area of the i th branch, v i is the flow velocity; The final optimization objective function of Step 3 is: ； Among them f 1 (v) is the objective function for minimizing pipeline wear f 2 (v) is the objective function for maximizing the cooling effect f 3 (P,H) is the objective function for maintaining the minimum water pressure 8. An optimization layout method for the hydraulic heat dissipation system of auxiliary equipment in the technical water supply network of a hydropower station based on an improved non-dominated sorting whale algorithm, characterized in that, The position update mechanism in Step 4 balances the algorithm between global search and local search by introducing an adaptive weight coefficient to improve the diversity and search efficiency of the solutions during the optimization process. Its adaptive weight equation is: ； Among them w min is the minimum weight, with a value of 0.3, w max is the maximum weight, with a value of 0.9, t max is the total number of iterations, t is the current iteration number.

9. The optimization layout method of the hydraulic heat dissipation system of the auxiliary equipment of the technical water supply network of a hydropower station based on an improved non-dominated sorting whale algorithm according to claim 1, characterized in that, The initial position in Step 4 is generated by the Sine-Tent-Cosine chaotic mapping function, and its formula is: ； Applying the composite chaotic mapping to the population initialization of the whale optimization algorithm can make the initial population distribution more uniform, thereby improving the algorithm performance.

10. A hydraulic heat dissipation system for auxiliary equipment of a technical water supply network in a hydropower station based on an improved non-dominated sorting whale algorithm, characterized in that, For the implementation of an optimized layout method for the hydraulic cooling system of auxiliary equipment in the technical water supply network of a hydropower station based on an improved non-dominated sorting whale algorithm as described in any one of claims 1 to 9, and to construct the structure of the technical water supply network of the hydropower station according to the system, including a water intake pool, the water intake pool is connected in parallel with a pressurization pipeline and a filtration pipeline through a gravity flow pipeline. The pressurization pipeline includes at least two parallel variable-frequency pressurization pumps. The filtration pipeline includes at least two parallel water filters. After the water outlet of the filtration pipeline is branched, it is respectively introduced into a water guide bearing pipeline and a four-way reversing valve. The four-way reversing valve is respectively connected to a thrust bearing pipeline, an upper guide bearing pipeline, and an air cooler. The water guide bearing pipeline, the thrust bearing pipeline, the upper guide bearing pipeline, and the air cooler finally lead to a drainage pool.

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