Parallel pump set energy efficiency optimization method based on improved grey wolf-sparrow algorithm

Abstract

A parallel pump set energy efficiency optimization method based on an improved grey wolf-sparrow algorithm comprises the steps that target pipe network basic data are obtained through a centrifugal pump hydraulic performance test; establishing flow-lift and flow-power polynomial fitting equations of the centrifugal pump at different rotating speeds; constructing an objective function of the parallel pump set optimization model according to requirements; constructing and considering constraint conditions of the parallel pump set optimization model; and solving by using an improved grey wolf-sparrow algorithm to obtain a parallel pump set optimization model control scheme. According to the parallel pump set optimization control scheme, optimization solution is carried out through start-stop, frequency conversion and valve adjustment, the calculation efficiency and precision are improved through an improved algorithm, the total energy consumption of the system is reduced, and meanwhile the expected life of the pump system is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of pump group control and energy efficiency optimization, and in particular to a parallel pump group energy efficiency optimization method based on an improved grey wolf-sparrow algorithm, aiming to achieve efficient and energy-saving control of parallel pump groups through an improved intelligent optimization algorithm. Background Art

[0002] Pump systems are widely used in industries such as industry, agriculture, and construction. Their energy efficiency optimization is of great significance for reducing energy consumption and increasing equipment life. Traditional pump control methods mainly rely on fixed start and stop rules and simple frequency conversion control, which is difficult to adapt to complex and variable load requirements, resulting in high system energy consumption and severe equipment wear.

[0003] In recent years, the application of intelligent optimization algorithms in pump group control has gradually attracted attention. Grey Wolf Optimizer (GWO) and Sparrow Search Algorithm (SSA) are two emerging swarm intelligence optimization algorithms that simulate the hunting behavior of gray wolf groups and the foraging behavior of sparrow groups, respectively, and have good global and local search capabilities. However, when dealing with complex optimization problems, a single algorithm still has problems such as slow convergence and easy to fall into local optimality.

[0004] In the prior art, some researchers proposed a pump group optimization control method based on genetic algorithm in "A parallel pump group energy efficiency optimization control method" CN202310736916.2. This method uses the global search capability of genetic algorithm to optimize the operating parameters of the pump group. Although the genetic algorithm performs well in global search, its convergence speed is slow, and the mutation and crossover operations in the optimization process may lead to low computational efficiency, which is difficult to meet the real-time control requirements in practical applications. Some researchers proposed "Optimal efficiency operation in parallel pumping systems using machine learning" CN201980038791.8, which proposed an optimal efficiency operation method in parallel pumping systems using machine learning. This method models and optimizes the pump group system through a machine learning algorithm, and adjusts it through real-time data to achieve the optimal efficiency operation of the pump group. However, this method requires a large amount of historical data for training, and the data acquisition and processing costs are high. During the real-time adjustment process, the machine learning algorithm may face problems with response speed and computational complexity, and it is difficult to maintain efficient optimization effects in a highly dynamic environment.

[0005] The gray wolf algorithm has a strong global search capability by simulating the social hierarchy structure and collaborative hunting strategy of gray wolf groups, but it may have a slow convergence speed in the local search stage. The sparrow algorithm has a good local search capability by simulating the foraging behavior and anti-predation behavior of sparrow groups, but it may have a limited search range in the global search stage. Summary of the invention

[0006] In order to overcome the shortcomings of the existing technology, the present invention proposes a parallel pump group energy efficiency optimization method based on the improved Grey Wolf-Sparrow algorithm. By combining the advantages of the Grey Wolf algorithm and the Sparrow algorithm, the calculation efficiency and accuracy of the optimization algorithm are improved, and efficient and energy-saving control of the parallel pump group is achieved.

[0007] The technical solution adopted by the present invention to solve its technical problem is:

[0008] A method for optimizing energy efficiency of a parallel pump group based on an improved grey wolf-sparrow algorithm comprises the following steps;

[0009] Step 1: Through the centrifugal pump hydraulic performance test, obtain the centrifugal pump flow Q, head H, motor input power P, working speed frequency f test sample data at different speeds;

[0010] Step 2, establish the polynomial fitting equations of flow-head and flow-power of the centrifugal pump at different speeds;

[0011] Step 3: construct objective functions of different parallel pump group optimization models according to different requirements;

[0012] Step 4: Construct and consider the constraints of the parallel pump group optimization model;

[0013] Step 5: Use the improved grey wolf-sparrow algorithm to solve the parallel pump group optimization model and generate the parallel pump group optimization plan.

[0014] Furthermore, in step 1, a centrifugal pump characteristic test is carried out under laboratory conditions, and the measured values ​​of the centrifugal pump flow Q, head H, motor input power P, and operating speed frequency f at different speeds are obtained with the help of a centrifugal pump hydraulic performance test system, the test data are sorted, and the centrifugal pump flow-head curve and flow-power curve at different speeds are plotted.

[0015] Furthermore, in step 2, according to the measured values ​​of flow rate Q, head H, power P, and working speed frequency f at different speeds, a polynomial fitting equation is used to establish an approximate equation of centrifugal pump head, flow rate, and speed frequency, as shown in formula (1); an approximate equation of power, flow rate, and speed frequency, as shown in formula (2);

[0016] H=-1.3685+0.0058Q+0.0480f-0.3662Q2 +0.0185fQ+0.0114f 2 -0.0306Q 3 +0.0077fQ 2 -6.5617e-04Qf 2 +2.0554e-05f 3 (1)

[0017] P=142.0547-3.1786Q-4.5490f+1.1248Q 2 +0.0380fQ+0.0917f 2 +0.4095Q 3 -0.2759fQ 2 +0.0611Qf 2 +9.2438e-04f 3 (2)

[0018] In the formula, Q is the flow rate of the pump, P is the motor input power, f is the working speed frequency of the pump, H is the pump head, a 00 to a 30 is the coefficient of the lift fitting equation, b 00 To b 30 are the coefficients of the power fitting equation.

[0019] Furthermore, in step 3, the objective function is to minimize the power consumption of the parallel pump group system, and the relationship is derived from the motor efficiency, inverter efficiency, flow rate and head:

[0020]

[0021] In the formula, i is the pump number, P is the motor input power, m is the total number of pumps, η vfd is the inverter efficiency, η motor is the motor efficiency.

[0022] In step 4, the constraints include parallel head constraint, single pump flow constraint, parallel pump flow constraint, variable frequency speed ratio constraint, and pump start and stop constraint;

[0023]

[0024] The parallel pump head constraint stipulates the lower limit of the head under variable frequency speed regulation, and the relationship is:

[0025] H rq ≤H i (5) In the formula, i is the pump number, H rq To meet the system requirements.

[0026] The single pump flow constraint specifies the upper and lower limits of the single pump flow, and the relationship is:

[0027] Q min ≤Q i ≤Q max (6)

[0028] In the formula, Q min is the lower limit of single pump flow, Q max It is the upper limit of single pump flow rate;

[0029] The parallel pump flow constraint specifies the upper and lower limits of the total system flow, and the relationship is:

[0030]

[0031] In the formula, Q rq is the total flow required by the system, Q i is the flow rate of each pump;

[0032] The variable frequency speed ratio constraint specifies the speed ratio of the frequency converter, and the relationship is:

[0033] 0.5≤k i ≤1 (8)

[0034] In the formula, k i is the speed ratio of each pump inverter;

[0035] The pump start and stop constraint specifies the number of pump starts and stops, and the relationship is:

[0036] 0≤m≤M(m∈N,M∈N)(9)

[0037] In the formula, m is the number of pumps started, and M is the total number of parallel pumps;

[0038] The system stability constraint specifies the difference between the operating condition of the pump system and the design condition, and the relationship is as follows:

[0039] Q BEP,i,k =k i Q BEP,i (10)

[0040]

[0041] -20%≤θ≤20%(12) In the formula, Q BEP，i is the flow rate of pump i at its highest efficiency, Q BEPi，k Q is the flow rate corresponding to the highest efficiency of pump i when the speed ratio is k, i is the current flow rate, and θ is the operating condition deviation.

[0042] In step 5, the parallel pump group optimization model is a mixed integer nonlinear programming problem. The improved grey wolf-sparrow algorithm is used to solve this problem. The solution process is as follows:

[0043] S501: According to the hydraulic performance data of the centrifugal pump, the pipe network data and the constraints, the distribution flow, speed ratio and start and stop of each pump are initialized based on the improved Sine chaotic map, which is the initialization population of the improved sparrow-grey wolf algorithm. The improved Sine chaotic map is expressed as:

[0044]

[0045] S502: randomly generate the position of the initial population in the solution space, and calculate the fitness value according to the position of the individual, and the fitness value is the energy consumption cost of the parallel pump group;

[0046] S503: Setting the convergence factor a to control the algorithm to use the gray wolf algorithm for global search or the sparrow algorithm for local search. The calculation formula of a is as follows:

[0047]

[0048] Where Max iter is the set iteration limit. If |A|>1, the improved gray wolf algorithm is used for global search, otherwise the improved sparrow algorithm is used for local search; the improved gray wolf-sparrow algorithm introduces a nonlinear convergence factor on the basis of the original algorithm and improves the update formula. The formulas after the gray wolf algorithm update and the sparrow algorithm update are as follows:

[0049]

[0050] l=(k-1)×rand+1 (18)

[0051]

[0052] Among them, formula (15) and formula (16) are improvements of the Grey Wolf Algorithm update; formula (17), formula (18), formula (19) and formula (20) are improvements of the Sparrow Algorithm; ST′∈[0.8,1] represents the security value; rand is a random number between [0,1]; ω is a constant;

[0053] In order to improve the performance of the algorithm, a perturbation strategy is added after the global search or local search. The specific position update formula is as follows:

[0054]

[0055] In the formula, is the location of the individual after the disturbance; is the position of individual i at the tth iteration; t(iter) satisfies the t distribution with the number of iterations of the algorithm as the degree of freedom, Max iter is the maximum number of iterations; t is the current number of iterations; w1 determines the upper limit of the dynamic selection probability, and w2 determines the range of change of the dynamic selection probability. After experiments, w1=0.75 and w2=0.1 are taken;

[0056] S504: updating the population based on the iterative process until the model reaches the maximum number of iterations, and outputting the optimal solution obtained by the improved gray wolf-sparrow algorithm;

[0057] Preferably, in step S5, the optimization model parameters of the parallel pump group include the allocated flow of each water pump, the start and stop status of each water pump, the speed ratio of each water pump inverter and the total energy consumption of the parallel pump group system.

[0058] The beneficial effects of the present invention are mainly manifested in: by combining the advantages of the grey wolf algorithm and the sparrow algorithm, the calculation efficiency and accuracy of the optimization algorithm are improved, and efficient and energy-saving control of the parallel pump group is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is the flow chart of energy efficiency optimization of parallel pump groups based on the improved grey wolf-sparrow algorithm.

[0060] Figure 2 Schematic diagram of the flow-head performance curve of a centrifugal pump at different speeds and frequencies.

[0061] Figure 3 Schematic diagram of the flow-power performance curve of a centrifugal pump at different speeds and frequencies.

[0062] Figure 4 Flowchart for improving the Grey Wolf-Sparrow algorithm. DETAILED DESCRIPTION

[0063] The present invention will be further described below in conjunction with the accompanying drawings.

[0064] Reference Figure 1 , a parallel pump group energy efficiency optimization method based on the improved gray wolf-sparrow algorithm, comprising the following steps: step 1, through the centrifugal pump hydraulic performance test, the centrifugal pump flow Q, head H, motor input power P, working speed frequency f test sample data at different speeds are obtained; step 2, the centrifugal pump flow-head and flow-power polynomial fitting equations at different speeds are established; step 3, the objective function of the parallel pump group optimization model is constructed according to the demand; step 4, the constraint conditions of the parallel pump group optimization model are constructed and considered; step 5, the parallel pump group optimization model control scheme is obtained by solving the improved gray wolf-sparrow algorithm.

[0065] The neural network prediction method for centrifugal pump performance without flow sensor in this embodiment is specifically implemented as follows:

[0066] Step 1: Through the centrifugal pump hydraulic performance test, obtain the centrifugal pump flow Q, head H, motor input power P, working speed frequency f test sample data at different speeds; the process is as follows:

[0067] In this embodiment, the rated flow rate is Q n =6m 3 / h, rated head is H n =22m, rated speed n=2900r / min, rated power P=750W centrifugal pumps of the same model were taken as the test object to form a parallel pump group. The centrifugal pump hydraulic performance test system was used to obtain the centrifugal pump flow Q, head H, motor input power P, working speed frequency f at different speeds, and the test data were sorted out;

[0068] Step 2: Establish the polynomial fitting equations of flow-head and flow-power of centrifugal pumps at different speeds; the process is as follows:

[0069] According to the measured values ​​of flow rate Q, head H, power P, and working speed frequency f at different speeds, polynomial fitting equations are used to establish the approximate equations of centrifugal pump head, flow rate, and speed frequency, as well as the approximate equations of power, flow rate, and speed frequency. The fitting equations of head and power are shown in formulas (1) and (2);

[0070] H=-1.3685+0.0058Q+0.0480f-0.3662Q 2 +0.0185fQ+0.0114f 2 -0.0306Q 3 +0.0077fQ 2 -6.5617e-04Qf 2 +2.0554e-05f 3 (1)

[0071] P=142.0547-3.1786Q-4.5490f+1.1248Q 2 +0.0380fQ+0.0917f 2 +0.4095Q 3 -0.2759fQ 2 +0.0611Qf 2 +9.2438e-04f 3 (2) In the formula, Q is the flow rate of the pump, P is the motor input power, f is the operating speed frequency of the pump, and H is the pump head;

[0072] Step 3: Construct different objective functions of the parallel pump group optimization model according to different requirements. The process is as follows:

[0073] The objective function is to minimize the power consumption of the parallel pump system, and the relationship is derived from the motor efficiency, inverter efficiency, flow rate and head:

[0074]

[0075] In the formula, i is the pump number, P is the motor input power, m is the total number of pumps, η vfd is the inverter efficiency, η motor is the motor efficiency;

[0076] Step 4: Construct and consider the constraints of the parallel pump group optimization model. The process is as follows:

[0077] The constraints include parallel head constraint, single pump flow constraint, parallel pump flow constraint, variable frequency speed ratio constraint, and pump start and stop constraint;

[0078]

[0079] The parallel pump head constraint stipulates the lower limit of the head under variable frequency speed regulation, and the relationship is:

[0080] H rq ≤H i (5)

[0081] Where i is the pump number, H rq To lift the system as required;

[0082] The single pump flow constraint specifies the upper and lower limits of the single pump flow, and the relationship is:

[0083] Q min ≤Q i ≤Q max (6)

[0084] In the formula, Q min is the lower limit of single pump flow, Q max It is the upper limit of single pump flow rate;

[0085] The parallel pump flow constraint specifies the upper and lower limits of the total system flow, and the relationship is:

[0086]

[0087] In the formula, Q rq is the total flow required by the system, Q i is the flow rate of each pump;

[0088] The variable frequency speed ratio constraint specifies the speed ratio of the frequency converter, and the relationship is:

[0089] 0.5≤k i ≤1 (8)In the formula, k iis the speed ratio of each pump inverter;

[0090] The pump start and stop constraint specifies the number of pump starts and stops, and the relationship is:

[0091] 0≤m≤M(m∈N,M∈N) (9)

[0092] In the formula, m is the number of pumps started, and M is the total number of parallel pumps;

[0093] The system stability constraint specifies the difference between the operating condition of the pump system and the design condition, and the relationship is as follows:

[0094] Q BEP,i,k =k i Q BEP,i (10)

[0095]

[0096] -20%≤θ≤20% (12) In the formula, Q BEP,i is the flow rate of pump i at maximum efficiency, Q BEP,i,k is the flow rate of pump i at maximum efficiency when the speed ratio is k, Q i is the current flow rate, θ is the operating condition deviation;

[0097] Step 5: Use the improved grey wolf-sparrow algorithm to solve the parallel pump group optimization model and generate the parallel pump group optimization plan. The parallel pump group optimization model is a mixed integer nonlinear programming problem. The improved grey wolf-sparrow algorithm is used to solve this problem. The solution steps include:

[0098] S501: According to the hydraulic performance data of the centrifugal pump, the pipe network data and the constraints, the distribution flow, speed ratio and start and stop of each pump are initialized based on the improved Sine chaotic map, which is the initialization population of the improved sparrow-grey wolf algorithm. The improved Sine chaotic map is expressed as:

[0099]

[0100] S502: randomly generate the position of the initial population in the solution space, and calculate the fitness value according to the position of the individual, and the fitness value is the energy consumption cost of the parallel pump group;

[0101] S503: Setting the convergence factor a to control the algorithm to use the gray wolf algorithm for global search or the sparrow algorithm for local search. The calculation formula of a is as follows:

[0102]

[0103] Where Max iteris the set iteration limit. If |A|>1, the improved gray wolf algorithm is used for global search, otherwise the improved sparrow algorithm is used for local search; the improved gray wolf-sparrow algorithm introduces a nonlinear convergence factor on the basis of the original algorithm and improves the update formula. The formulas after the gray wolf algorithm update and the sparrow algorithm update are as follows:

[0104]

[0105] l=(k-1)×rand+1 (18)

[0106]

[0107] Among them, formula (15) and formula (16) are improvements of the Grey Wolf Algorithm update; formula (17), formula (18), formula (19) and formula (20) are improvements of the Sparrow Algorithm. ST′∈[0.8,1] represents the security value; rand is a random number between [0,1]; ω is a constant, which is 5.5 after experiments;

[0108] In order to improve the performance of the algorithm, a perturbation strategy is added after the global search or local search. The specific position update formula is as follows:

[0109]

[0110] In the formula, is the location of the individual after the disturbance; is the position of individual i at the tth iteration; t(iter) satisfies the t distribution with the number of iterations of the algorithm as the degree of freedom, Max iter is the maximum number of iterations; t is the current number of iterations; w1 determines the upper limit of the dynamic selection probability, and w2 determines the range of change of the dynamic selection probability. After experiments, w1=0.75 and w2=0.1 are taken.

[0111] S504: updating the population based on the iterative process until the model reaches the maximum number of iterations, and outputting the optimal solution obtained by the improved gray wolf-sparrow algorithm;

[0112] In step S5, the optimization model parameters of the parallel pump group include the allocated flow of each water pump, the start and stop status of each water pump, the speed ratio of each water pump inverter and the total energy consumption of the parallel pump group system.

[0113] In order to prove that the energy efficiency optimization method of parallel pump groups based on the improved gray wolf-sparrow algorithm can further reduce the total energy consumption of parallel pump groups and reduce the operating costs of pump stations, experimental data is taken as an example to compare and analyze the "one variable and multiple working" of conventional pump stations (that is, one pump in the system supports variable frequency control, and the remaining pumps only support industrial frequency operation). Under different loads, the energy consumption parameters of the pump system are shown in Table 1. From the data in the table, it can be seen that the use of the improved gray wolf-sparrow algorithm to find the best solution can significantly reduce the system power consumption. Compared with the traditional "one variable and multiple working", it can reduce the system power consumption by up to 53.68% (under 70% system load). Table 1 shows the real-time measurement data of the power limitless centrifugal pump.

[0114]

[0115] Table 1

[0116] The contents described in the embodiments of this specification are merely enumerations of implementation forms of the inventive concept and are for illustrative purposes only. The protection scope of the present invention should not be considered to be limited to the specific forms described in this embodiment, and the protection scope of the present invention also extends to equivalent technical means that can be thought of by ordinary technicians in this field based on the inventive concept.

Claims

1. A method for optimizing energy efficiency of parallel pump groups based on an improved grey wolf-sparrow algorithm, characterized in that: The method comprises the following steps: Step 1: Through the centrifugal pump hydraulic performance test, obtain the centrifugal pump flow Q, head H, motor input power P, working speed frequency f test sample data at different speeds; Step 2, establish the polynomial fitting equations of flow-head and flow-power of the centrifugal pump at different speeds; Step 3: construct objective functions of different parallel pump group optimization models according to different requirements; Step 4: Construct and consider the constraints of the parallel pump group optimization model; Step 5: Use the improved grey wolf-sparrow algorithm to solve the parallel pump group optimization model and generate the parallel pump group optimization plan.

2. A method for optimizing energy efficiency of parallel pump groups based on an improved grey wolf-sparrow algorithm as claimed in claim 1, characterized in that: In the step 1, a centrifugal pump characteristic test is carried out under laboratory conditions, and the centrifugal pump flow Q, head H, motor input power P, and operating speed frequency f measurement values ​​at different speeds are obtained with the help of a centrifugal pump hydraulic performance test system, and the test data are sorted out to draw the centrifugal pump flow-head curve and flow-power curve at different speeds.

3. A method for optimizing energy efficiency of parallel pump groups based on an improved grey wolf-sparrow algorithm as claimed in claim 1 or 2, characterized in that: In step 2, according to the measured values ​​of flow rate Q, head H, power P, and working speed frequency f at different speeds, a polynomial fitting equation is used to establish an approximate equation of centrifugal pump head, flow rate, and speed frequency, as shown in formula (1); an approximate equation of power, flow rate, and speed frequency, as shown in formula (2); H=-1.3685+0.0058Q+0.0480f-0.3662Q 2 +0.0185fQ+0.0114f 2 -0.0306Q 3 +0.0077fQ 2 -6.5617e-04Qf 2 +2.0554e-05f 3 (1) P=142.0547-3.1786Q-4.5490f+1.1248Q 2 +0.0380fQ+0.0917f 2 +0.4095Q 3 -0.2759fQ 2 +0.0611Qf 2 +9.2438e-04f 3 (2) In the formula, Q is the flow rate of the pump, P is the motor input power, f is the working speed frequency of the pump, H is the pump head, a 00 to a 30 is the coefficient of the lift fitting equation, b 00 To b 30 are the coefficients of the power fitting equation.

4. A method for optimizing energy efficiency of parallel pump groups based on an improved grey wolf-sparrow algorithm as claimed in claim 1 or 2, characterized in that: In step 3, the objective function is to minimize the power consumption of the parallel pump group system, and the relationship is derived from the motor efficiency, inverter efficiency, flow rate and head: In the formula, i is the pump number, P is the motor input power, m is the total number of pumps, η vfd is the inverter efficiency, η motor is the motor efficiency.

5. A method for optimizing energy efficiency of parallel pump groups based on an improved grey wolf-sparrow algorithm as claimed in claim 1 or 2, characterized in that: In step 4, the constraints include parallel head constraint, single pump flow constraint, parallel pump flow constraint, variable frequency speed ratio constraint, and pump start and stop constraint; The parallel pump head constraint stipulates the lower limit of the head under variable frequency speed regulation, and the relationship is: H rq ≤H i (5) Where i is the pump number, H rq To lift the system as required; The single pump flow constraint specifies the upper and lower limits of the single pump flow, and the relationship is: Q min ≤Q i ≤Q max (6) In the formula, Q min is the lower limit of single pump flow, Q max It is the upper limit of single pump flow rate; The parallel pump flow constraint specifies the upper and lower limits of the total system flow, and the relationship is: In the formula, Q rq is the total flow required by the system, Q i is the flow rate of each pump; The variable frequency speed ratio constraint specifies the speed ratio of the frequency converter, and the relationship is: 0.5≤k i ≤1 (8) In the formula, k i is the speed ratio of each pump inverter; The pump start and stop constraint specifies the number of pump starts and stops, and the relationship is: 0≤m≤M(m∈N,M∈N) (9) In the formula, m is the number of pumps started, and M is the total number of parallel pumps; The system stability constraint specifies the difference between the operating condition of the pump system and the design condition, and the relationship is as follows: Q BEP,i,k =k i ·Q BEP,i (10) -20%≤θ≤20% (12) In the formula, Q BEP,i is the flow rate of pump i at its highest efficiency, Q BEP,i,k Q is the flow rate corresponding to the highest efficiency of pump i when the speed ratio is k, i is the current flow rate, and θ is the operating condition deviation.

6. A method for optimizing energy efficiency of parallel pump groups based on an improved grey wolf-sparrow algorithm as claimed in claim 1 or 2, characterized in that: In step 5, the parallel pump group optimization model is a mixed integer nonlinear programming problem. The improved grey wolf-sparrow algorithm is used to solve this problem. The solution process is as follows: S501: According to the hydraulic performance data of the centrifugal pump, the pipe network data and the constraints, the distribution flow, speed ratio and start and stop of each pump are initialized based on the improved Sine chaotic map, which is the initialization population of the improved sparrow-grey wolf algorithm. The improved Sine chaotic map is expressed as: S502: randomly generate the position of the initial population in the solution space, and calculate the fitness value according to the position of the individual, and the fitness value is the energy consumption cost of the parallel pump group; S503: Setting the convergence factor a to control the algorithm to use the gray wolf algorithm for global search or the sparrow algorithm for local search. The calculation formula of a is as follows: Where Max iter is the set iteration limit. If |A|>1, the improved gray wolf algorithm is used for global search, otherwise the improved sparrow algorithm is used for local search; the improved gray wolf-sparrow algorithm introduces a nonlinear convergence factor on the basis of the original algorithm and improves the update formula. The formulas after the gray wolf algorithm update and the sparrow algorithm update are as follows: l=(k-1)×rand+1 (18) Among them, formula (15) and formula (16) are improvements of the Grey Wolf Algorithm update; formula (17), formula (18), formula (19) and formula (20) are improvements of the Sparrow Algorithm; ST′∈[0.8,1] represents the security value; rand is a random number between [0,1]; ω is a constant; In order to improve the performance of the algorithm, a perturbation strategy is added after the global search or local search. The specific position update formula is as follows: In the formula, is the location of the individual after the disturbance; is the position of individual i at the tth iteration; t(iter) satisfies the t distribution with the number of iterations of the algorithm as the degree of freedom, Max iter is the maximum number of iterations; t is the current number of iterations; w1 determines the upper limit of the dynamic selection probability, and w2 determines the range of change of the dynamic selection probability. After experiments, w1=0.75 and w2=0.1 are taken; S504: Update the population based on the iterative process until the model reaches the maximum number of iterations, and output the optimal solution obtained by the improved gray wolf-sparrow algorithm.

7. A method for optimizing energy efficiency of parallel pump groups based on an improved grey wolf-sparrow algorithm as claimed in claim 6, characterized in that: In step S5, the optimization model parameters of the parallel pump group include the allocated flow of each water pump, the start and stop status of each water pump, the speed ratio of each water pump inverter and the total energy consumption of the parallel pump group system.

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