A method for regulating pump station outlet pressure in a water supply network

Abstract

The present invention belongs to the technical field of pressure control of water supply network, and in particular relates to a method for controlling the outlet pressure of a pump station for a water supply network. Compared with the mainstream method of constant supply pressure at the pump outlet, this method can effectively distinguish various time periods of water use by dividing water supply time periods into different time periods, and provide corresponding pump station outlet pressure values ​​for each time period. In other water supply time periods except the peak time period, the outlet pressure of the pump station is more accurately in line with actual needs, thereby effectively reducing energy waste. Compared with keeping the pressure value of the most unfavorable point constant, since historical data is used to accurately predict the outlet pressure of the pump station, there is no need to collect real-time pressure data at the most unfavorable point and transmit it to the booster pump station in real time to guide the operation of the pump station. There is no timeliness requirement for the feedback of data such as pressure measurement information, and there is no need for frequent regulation. This method can effectively reduce the energy waste of pump stations on the basis of reasonable cost control.

Description

Technical Field

[0001] The present invention belongs to the technical field of water supply network pressure regulation, and in particular relates to a method for regulating the outlet pressure of a pump station in a water supply network. Background Art

[0002] The water supply system is an important part of urban infrastructure, and water pumps are key to the system. The energy consumed by their operation accounts for a major part of the entire system. However, most pumping stations in my country still have the problem of energy waste. According to relevant industry statistics, the operating efficiency of urban water pump units is relatively low, currently generally ranging from 50% to 75%. As the main energy consumption link in urban water supply systems, improving the energy consumption of water pumps has a significant impact on achieving the industry's energy conservation goals. Therefore, studying water pump optimization operation strategies, improving pump station operating efficiency and reducing operating energy consumption will help achieve the city's green and low-carbon development goals.

[0003] The current mainstream approach to managing pump groups at water supply stations is to maintain a constant outlet pressure. Specifically, the outlet pressure at the water supply station is maintained at a constant value, determined to ensure stable water use even during peak demand periods at the most unfavorable point. However, this management approach presents a significant problem: the pressure at the most unfavorable point is often excessively elevated, resulting in significant energy waste.

[0004] To address this problem, some skilled in the art have proposed maintaining a constant pressure value at the most unfavorable point and, in combination with real-time data such as water flow rates from users in the network, reversely adjusting the power of the pumping station's pump groups. This approach effectively avoids energy waste at the pumping station, as the most unfavorable point at each time point is just sufficient for stable water use. However, adopting this technical solution requires installing real-time pressure transmission equipment at the most unfavorable point to transmit the collected pressure data in real time to the booster pump station to guide the pump station's operation (in extreme cases, frequent regulation is required). This places high demands on the pump station's real-time operational management capabilities. Furthermore, the transmission of the pump station's starting pressure to the most unfavorable point requires time, resulting in a certain lag in the feedback of the pressure measurement information, which results in a weak timeliness of the control strategy. Therefore, this technical solution is very costly, lacks real-time performance and operability, and is not ideal in actual use.

[0005] In summary, how to effectively reduce the energy waste of pumping stations on the basis of reasonable cost control has become an urgent problem to be solved. Summary of the Invention

[0006] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for regulating the outlet pressure of a pump station for a water supply network, which can effectively reduce energy waste in the pump station on the basis of reasonable cost control.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A method for regulating the outlet pressure of a pump station for a water supply network comprises the following steps:

[0009] S1. Construct a pump station outlet pressure prediction model; use it to predict the pump station outlet pressure in the next N hours based on the historical water supply flow sequence data, the pressure sequence data of the pressure measuring point, and the water supply pressure sequence data required by the pump station;

[0010] S2. Obtain historical training data and preprocess it to obtain model training data; the historical training data includes historical water supply flow sequence data, pressure sequence data of pressure measuring points, and water supply pressure sequence data required by the pump station;

[0011] S3, using the model training data obtained in S2, trains the pump station outlet pressure prediction model constructed in S1;

[0012] S4. Predict the pump station outlet pressure in the next N hours using the trained pump station outlet pressure prediction model;

[0013] S5. Based on the prediction result of S4, the pump station outlet pressure is divided into multiple water supply periods; when dividing the water supply periods, according to the fluctuation of the predicted pump station outlet pressure, the maximum continuous time interval in which the fluctuation value is less than the preset value is set as a water supply period;

[0014] S6. For each water supply period, the maximum pump station outlet pressure value during the water supply period is used as the constant outlet pressure of the water supply period; and the constant outlet pressures of each water supply period are integrated as a control scheme;

[0015] S7. According to the control scheme of S6, actual pump station outlet pressure control is performed to provide corresponding constant outlet pressure in different water supply periods.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] 1. This method constructs a pump station outlet pressure prediction model to predict the pump station outlet pressure over the next N hours. In practice, based on the model's prediction results, the pump station outlet pressure is divided into multiple water supply periods. Based on the predicted fluctuations in the pump station outlet pressure, the longest continuous time interval during which the fluctuation is less than a preset value is defined as a water supply period. For each water supply period, the maximum pump station outlet pressure value within the period is used as the constant outlet pressure for that water supply period. The constant outlet pressures for each water supply period are then integrated to form a control scheme. Actual control is then performed based on this control scheme.

[0018] Compared with the mainstream method of constant pump outlet pressure, this method can effectively distinguish various time periods of water use (such as the peak time period, sub-peak time period, regular time period, valley time period, etc.) by dividing the water supply period, and provide the corresponding pump station outlet pressure value for each period. In other water supply periods except the peak time period, the water outlet pressure of the pump station is more in line with actual needs, thereby effectively reducing energy waste. Compared with keeping the pressure value of the most unfavorable point constant, since the pump station outlet pressure is predicted using historical data (such as data from the past 10 days), there is no need to collect real-time pressure data at the most unfavorable point and transmit it to the booster pump station in real time to guide the operation of the pump station. There is no timeliness requirement for the feedback of data such as pressure measurement information, and there is no need for very frequent regulation. It is highly operational and the cost of implementation is low.

[0019] 2. The pump station outlet pressure prediction model constructed using this method is trained based on historical water flow rate data, pressure data at pressure measuring points, and the required water supply pressure data for the pump station. This model can more accurately predict the pump station outlet pressure over the next N hours. This predictive capability helps to understand the changing trend of the pump station outlet pressure in advance, providing a reliable basis for subsequent regulation.

[0020] 3. Based on the prediction results, this method divides the pump station outlet pressure into multiple different water supply periods. Based on the predicted fluctuations in the pump station outlet pressure, the longest continuous time interval during which the fluctuation value is less than a preset value is defined as a water supply period. This division method can more rationally arrange water supply periods and reduce the adverse effects of excessive pressure fluctuations on the water supply system.

[0021] In summary, this method can effectively reduce the energy waste of pumping stations on the basis of reasonable cost control.

[0022] Preferably, in S7, when a water supply period provides a corresponding constant outlet pressure, an optimal power control scheme of the water pump unit is solved by a preset power optimization model, and the water pump unit is controlled according to the obtained optimal power control scheme.

[0023] This setup, through a power optimization model, can accurately calculate the optimal power required by the pump unit under different water supply periods and constant outlet pressure. This helps prevent the pump unit from operating at excessive power when not necessary, further reducing energy consumption and operating costs.

[0024] Furthermore, prolonged operation at excessively high power can lead to increased wear and tear on the pump unit, shortening the equipment's service life. Power optimization control ensures the pump unit operates within a reasonable power range, reducing mechanical stress and wear caused by overload, thereby extending the equipment's service life.

[0025] Preferably, the objective function of the power optimization model is:

[0026]

[0027] Where c is the electricity price during the period; n is the number of speed regulating pumps; ω i To control the opening and closing coefficient of the i-th pump, the value is 0 or 1; Q i is the output flow of the i-th pump; S i is the speed ratio of the i-th pump; d 0i d 1i d 2i is the power fitting coefficient of the i-th pump.

[0028] Such a setting can ensure the optimal power control solution and reduce energy consumption as much as possible.

[0029] Preferably, the constraints of the power optimization model include single pump flow constraint, water pump head constraint, single pump shaft power constraint, number of started units constraint, and speed ratio constraint.

[0030] Such a setting can ensure the effectiveness of the obtained optimal power control solution.

[0031] Preferably, the single pump flow constraint is:

[0032] Q min <Q i <Q max ;

[0033] Where Q i is the flow rate of the i-th pump in the pumping station; Q min , Q max are the minimum flow rate and maximum flow rate of the i-th water pump respectively;

[0034] The pump head constraint is:

[0035] H min <H i <H max ;

[0036] Where H i is the head of the i-th water pump; H min 、H max are the minimum head and maximum head of the i-th water pump respectively.

[0037] Preferably, the single pump shaft power constraint is:

[0038] P min <P i <P max ;

[0039] Where, Pi is the shaft power of the i-th water pump; P min 、P max are the minimum and maximum shaft power of the i-th pump in the pumping station respectively;

[0040] The number of startup units is constrained as follows:

[0041] 0<n<n max ;

[0042] Where n is the number of pumps in operation in the pumping station, n max The preset maximum number of bootable units.

[0043] Preferably, the speed ratio constraint is:

[0044] S i ∈[S imin ,1];

[0045] Where S i is the speed ratio of the i-th pump; S imin is the minimum speed ratio of the i-th pump.

[0046] Preferably, in S7, when solving the optimal power control scheme of the water pump unit through the power optimization model, the solution is performed by an optimization method based on a hybrid improved cuckoo algorithm; the workflow of the optimization method based on the hybrid improved cuckoo algorithm includes:

[0047] Step 1: Randomly generate N bird nests as the initial population, record the initial optimal bird nest location, and perform parameter initialization settings;

[0048] Step 2: Use the Levy flight strategy to update the locations of other bird nests except the current best nest location, calculate the fitness value of each updated bird nest, and then use the nest location with the largest fitness value among all the bird nests as the new best nest location;

[0049] Step 3: Use the update strategy of the GWO algorithm to update the location of each bird's nest again;

[0050] Step 4: Randomly update the nest locations other than the optimal solution of the GWO algorithm based on the preset discovery probability Pa. The random update process includes: generating a random number r for a nest location to be randomly updated, and comparing r with the discovery probability Pa; if r>Pa, updating the nest location by random walking; otherwise, the nest location remains unchanged;

[0051] Step 5: Calculate the fitness values ​​of all bird nest positions again, and record the bird nest position corresponding to the best fitness value as the optimal solution of the current round;

[0052] Step 6: Determine whether the termination condition is met. If so, output the optimal solution of the current round as the final result; if not, return to step 2 and perform the next round of iteration.

[0053] This setup: 1. The hybrid improved cuckoo algorithm combines the randomness and long step length of the Lévy flight strategy, helping the algorithm quickly explore and escape from local optimal solutions in the search space, improving global search capabilities. By introducing the update strategy of the GWO algorithm and leveraging the social hierarchy and hunting behavior of gray wolf packs to guide search direction, the algorithm's local search capabilities are enhanced, allowing it to more accurately approach the global optimal solution.

[0054] 2. The Lévy flight strategy enables the algorithm to extensively explore the solution space in the early stages of the search, identifying regions of potential high-quality solutions. The introduction of the GWO algorithm strengthens this refined search for high-quality solutions in the later stages of the search, improving solution accuracy. This balance between exploration and exploitation makes the algorithm more efficient and stable when solving complex optimization problems.

[0055] 3. Random updates based on a preset discovery probability Pa increase the algorithm's randomness and diversity, helping to prevent premature convergence. Updating nest locations through random walks allows the algorithm to maintain a certain degree of flexibility during the search process, adapting to different search environments and problem characteristics.

[0056] 4. Optimal power control problems for pump units often involve multiple variables and complex constraints, making them difficult to solve effectively with traditional optimization methods. The hybrid improved Cuckoo algorithm combined with the GWO algorithm, through its powerful global and local search capabilities, can effectively address these complex optimization problems and find high-quality solutions.

[0057] In summary, through this method, the effectiveness of the optimal power control solution finally obtained can be guaranteed.

[0058] Preferably, in step 1, the initialization parameters include the abandonment probability Pa, the maximum number of iterations Tmax, and the parameters of the GWO algorithm;

[0059] In step 6, the termination condition is that the number of iterations reaches the maximum number of iterations Tmax.

[0060] Preferably, the pressure value in the power optimization model calculation optimization process solution meets the following conditions:

[0061] |H i -H j |≤0.2;

[0062] Where H i is the constant outlet pressure of the i-th water supply period in the control scheme; H jCalculate the pressure value in the optimization process solution for the corresponding water supply period.

[0063] In this way, the effectiveness of the optimal power control solution finally obtained can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to make the purpose, technical solutions and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0065] Figure 1 Flowchart of this method;

[0066] Figure 2 This is the flow chart of the hybrid improved cuckoo algorithm in this method;

[0067] Figure 3 Schematic diagram of the convergence of the optimization calculation of the Schwefel test function f1(x) in Example 2;

[0068] Figure 4 Schematic diagram comparing the optimization results of the f1(x) test function in Example 2;

[0069] Figure 5 Schematic diagram of the convergence of the optimization calculation of the Schwefel test function f2(x) in Example 2;

[0070] Figure 6 Schematic diagram of the convergence of the optimization calculation of the test function f2(x) in Example 2;

[0071] Figure 7 This is a schematic diagram of the analysis of the pressure value required for the pump station outlet in Example 3;

[0072] Figure 8 This is a statistical chart of pipe leakage and burst in the past three years in Example 3;

[0073] Figure 9 This is a model diagram of a booster pump station in a town in Province S in Example 3;

[0074] Figure 10 This is a schematic diagram of the prediction of the water pressure at the highest daily water consumption in summer in Example 3;

[0075] Figure 11 This is a schematic diagram of water pressure prediction on the day with the highest water consumption in winter in Example 3;

[0076] Figure 12 This is the voltage regulation control situation of the highest day in summer in Example 3;

[0077] Figure 13 This is the voltage regulation control situation of the highest day in winter in Example 3;

[0078] Figure 14 This is a schematic diagram of the iterative results of the optimized operation of the pump station with H = 48.03 ~ 51.03m in Example 3;

[0079] Figure 15 This is a schematic diagram of the iterative results of the optimized operation of the pump station with H = 51.03 ~ 54.03m in Example 3;

[0080] Figure 16 This is a schematic diagram of the iterative results of the optimized operation of the pump station with H = 54.03 ~ 57.03m in Example 3;

[0081] Figure 17 This is a schematic diagram of the iterative results of the optimized operation of the pump station with H = 57.03 ~ 60.03m in Example 3;

[0082] Figure 18 This is the relationship diagram between efficiency, flow rate and head in winter water use in Example 3;

[0083] Figure 19 This is the relationship diagram of efficiency, flow rate and head in the summer water use situation in Example 3;

[0084] Figure 20 This is a schematic diagram of the pressure change analysis at the most unfavorable point in Example 3;

[0085] Figure 21 This is a schematic diagram of the pressure change analysis of overpressure point No. 1 in Example 3;

[0086] Figure 22 This is a schematic diagram of the pressure change analysis of overpressure point No. 2 in Example 3. DETAILED DESCRIPTION

[0087] The following is a further detailed description through specific implementation methods:

[0088] Example 1

[0089] like Figure 1 As shown, this embodiment discloses a method for regulating the outlet pressure of a pump station for a water supply network, comprising the following steps:

[0090] S1. Construct a pump station outlet pressure prediction model; it is used to predict the pump station outlet pressure in the next N hours based on the historical water supply flow sequence data, the pressure sequence data of the pressure measuring point, and the water supply pressure sequence data required by the pump station.

[0091] In specific implementation, the pump station outlet pressure prediction model is built based on the LSTM neural network model. In the established neural network prediction model, the historical water supply flow sequence data, the pressure sequence data of the pressure measuring point, and the water supply pressure sequence data required by the pump station are used as the input layer data, and the predicted water outlet pressure value is used as the output layer. The hidden layer uses black box operation to simulate and simulate the complex relationship between flow and pressure. The data relationship can be expressed as follows:

[0092] P pre =f LSTM (H i , P pump , Q);

[0093] Where, P ore To predict the water supply pressure m of the pump station; f LSTM is the LSTM model; H i is the historical pressure value m at the pressure measuring point; P pump is the water supply pressure value m required for the pump station's operation history; Q is the historical water supply flow of the pump station.

[0094] S2. Obtain historical training data and perform preprocessing to obtain model training data; the historical training data includes historical water supply flow sequence data, pressure sequence data of pressure measuring points, and water supply pressure sequence data required by the pump station.

[0095] Based on the specific conditions of the background city, multiple historical operational data sets were used as input variables, and the pump station outlet pressure was set as the output variable. These different input variables can be summarized as having two main functions: the first is the water supply flow rate and pump station outlet pressure data from the previous eight days, which is used to enable the model to learn the cyclical patterns of pipeline network operation; the second is the pressure at the detection point, which allows the model to perceive and verify the current predicted operation of the pipeline network.

[0096] During the specific implementation, the historical data of pump station flow rate was selected from ten days of pump station flow rate data, of which the water volume data of the first eight days was used as the training set of the model, and the water volume data of the last two days was used as the test set. The historical data of water supply pressure required by the pump station was used to ensure the constant pressure value of water supply at the most unfavorable point. The historical water supply flow data was used to calculate the water loss in the pipeline through hydraulic calculation, and the actual required pressure value of the pump station outlet was calculated as the variable input. The historical data of pressure at the pressure measuring point was selected from the pressure data that was consistent with the time period of the input water volume data, and representative pressure monitoring points in the water supply network were selected. Due to the limited number of actual pressure detection points, the most unfavorable point of the network system (underpressure complaint point) and the low-lying overpressure point were used as representative detection points, and pressure detection data were provided as variable input.

[0097] Preprocessing includes normalization. Factors influencing the pump station outlet pressure include the outlet flow rate and the pressure at the pressure measuring point, which are the input values ​​of the prediction model. Their dimensions and orders of magnitude are inconsistent. Similar to water volume prediction, these data need to be normalized.

[0098] S3, using the model training data obtained in S2, trains the pump station outlet pressure prediction model constructed in S1;

[0099] S4. Use the trained pump station outlet pressure prediction model to predict the pump station outlet pressure for the next N hours. In practice, N is 24. This allows the pump station outlet pressure to be predicted for 24 hours per day, ensuring both the immediate effectiveness of regulation and the frequency of regulation.

[0100] S5. Based on the prediction results of S4, the pump station outlet pressure is divided into multiple water supply periods. When dividing the water supply periods, the maximum continuous time interval in which the fluctuation value is less than a preset value is defined as a water supply period based on the predicted fluctuation of the pump station outlet pressure. This setting can effectively distinguish various water use time periods (such as peak time period, sub-peak time period, regular time period, valley time period, etc.).

[0101] S6. For each water supply period, the maximum pump station outlet pressure value during the water supply period is used as the constant outlet pressure of the water supply period; and the constant outlet pressures of each water supply period are integrated as a control scheme;

[0102] S7. According to the control scheme of S6, actual pump station outlet pressure control is performed, and corresponding constant outlet pressure is provided in each water supply period, so as to meet the actual pressure required by the water supply network in the corresponding time period.

[0103] This method constructs a pump station outlet pressure prediction model to predict the pump station outlet pressure over a time series N hours into the future. In practice, based on the model's prediction results, the pump station outlet pressure is divided into multiple water supply periods. Based on the predicted fluctuations in the pump station outlet pressure, the longest continuous time interval during which the fluctuation is less than a preset value is defined as a water supply period. For each water supply period, the maximum pump station outlet pressure value within the period is used as the constant outlet pressure for that water supply period. The constant outlet pressures across all water supply periods are then integrated to form a control scheme. Actual control is then performed based on this control scheme. Compared to the mainstream method of constant pump outlet pressure, this method effectively distinguishes various water use time periods (such as peak time period, sub-peak time period, regular time period, off-peak time period, etc.) by dividing the water supply period into different periods, and provides corresponding pump station outlet pressure values ​​for each period. During water supply periods other than the peak time period, the pump station outlet pressure is more closely aligned with actual demand, effectively reducing energy waste. Compared to maintaining a constant pressure value at the worst-case scenario, this method utilizes historical data (e.g., data from the past 10 days) to predict the pumping station outlet pressure. This eliminates the need to collect real-time pressure data at the worst-case scenario and transmit it to the booster station to guide its operation. There is no timeliness requirement for feedback on pressure measurement data, nor does it require frequent adjustments. This method is highly operational and has a low implementation cost. This method can effectively reduce energy waste at pumping stations while maintaining reasonable cost control.

[0104] In addition, the pump station outlet pressure prediction model constructed by this method is trained based on historical water supply flow sequence data, pressure measurement point pressure sequence data, and water supply pressure sequence data required by the pump station. The model can more accurately predict the time series pump station outlet pressure for the next N hours. This prediction capability helps to understand the changing trend of the pump station outlet pressure in advance and provide a reliable basis for subsequent regulation. In addition, based on the prediction results, this method divides the pump station outlet pressure into multiple water supply periods, and according to the predicted fluctuation of the pump station outlet pressure, the maximum continuous time interval with a fluctuation value less than a preset value is defined as a water supply period. This division method can arrange the water supply period more reasonably and reduce the adverse effects on the water supply system caused by excessive pressure fluctuations.

[0105] During specific implementation, in S7, when a water supply period provides a corresponding constant outlet pressure, the optimal power control scheme of the water pump unit is solved through the preset power optimization model, and the water pump unit is controlled according to the obtained optimal power control scheme. In this way, the power optimization model can accurately calculate the optimal power required by the water pump unit under different water supply periods and constant outlet pressures. This helps to avoid the water pump unit from running at too high a power when it is not necessary, thereby further reducing energy consumption and operating costs. In addition, running at too high a power for a long time will cause the water pump unit to wear more severely and shorten the service life of the equipment. Through power optimization control, it can ensure that the water pump unit operates within a reasonable power range, reduce the mechanical stress and wear caused by overload operation, and thus extend the service life of the equipment.

[0106] Among them, the objective function of the power optimization model is:

[0107]

[0108] Where c is the electricity price during the period; n is the number of speed regulating pumps; ω i To control the opening and closing coefficient of the i-th pump, the value is 0 or 1; Q i is the output flow of the i-th pump; S i is the speed ratio of the i-th pump; d 0i d 1i d 2i is the power fitting coefficient of the i-th pump.

[0109] The constraints of the power optimization model include single-pump flow rate constraints, pump head constraints, single-pump shaft power constraints, number of units in operation constraints, and speed ratio constraints. This ensures the effectiveness of the optimal power control solution.

[0110] Single pump flow restriction

[0111] Combined with the operating characteristics of the water pump in the high-efficiency section, the water supply flow rate of the water pump is limited within a certain range during the calculation process.

[0112] Q min <Q i <Q max ;

[0113] Where Q i is the flow rate of the i-th pump in the pumping station; Q min , Q max are the minimum flow rate and maximum flow rate of the i-th water pump respectively.

[0114] Pump head constraints

[0115] If the water pump is kept running in the high efficiency section, the water supply head of the water pump will fluctuate within a certain range.

[0116] H min <H i <H max ;

[0117] Where H i is the head of the i-th water pump; H min 、H max are the minimum head and maximum head of the i-th water pump respectively.

[0118] Single pump shaft power constraint

[0119] Each water pump has two states: on and off. When the water pump is in the on state, and it is necessary to ensure that the water pump operates in the high efficiency range, the shaft power of the water pump will vary within a certain range.

[0120] P min <P i <P max ;

[0121] Where, P i is the shaft power of the i-th water pump; P min 、P max are the minimum and maximum shaft power of the i-th pump in the pump station respectively; the number of running units is constrained

[0122] There are three speed-regulating pumps of the same model in the pumping station. Usually, one pump is used to supply water during low-peak water usage, and two pumps are used in parallel to supply water during peak water usage. This part constrains the number of pumps that are turned on, controls the actual number of pumps that are turned on to within two, and at the same time optimizes the number of pumps that are turned on in each time period.

[0123] 0<n<n max ;

[0124] Where n is the number of pumps in operation in the pumping station, n max is the preset maximum number of powered-on units. In this embodiment, n max =2.

[0125] Speed ​​ratio constraint

[0126] In actual operation, water pumps must maintain stable operation while preventing cavitation. To ensure safety, water pumps are typically not overloaded and cannot exceed their rated speed. Furthermore, when the speed ratio falls below a certain value, the pump's operating efficiency drops sharply. Therefore, the speed ratio of the water pump is generally controlled to remain within an appropriate range.

[0127] S i ∈[S imin ,1];

[0128] Where S i is the speed ratio of the i-th pump; S iminis the minimum speed ratio of the i-th pump, and its value needs to be determined according to the specific power and efficiency of the speed-regulating pump.

[0129] In specific implementation, when solving the optimal power control scheme of the water pump unit through the power optimization model, the solution is obtained through the optimization method based on the hybrid improved cuckoo algorithm; Figure 2 As shown in FIG, the workflow of the optimization method based on the hybrid improved cuckoo algorithm includes:

[0130] Step 1: Randomly generate N bird nests as the initial population, record the initial optimal nest location, and initialize the parameters. The initialization parameters include the abandonment probability Pa, the maximum number of iterations Tmax, and the parameters of the GWO algorithm.

[0131] Step 2: Use the Levy flight strategy to update the locations of other bird nests except the current best nest location, calculate the fitness value of each updated bird nest, and then use the nest location with the largest fitness value among all the bird nests as the new best nest location;

[0132] Step 3: Use the update strategy of the GWO algorithm to update the location of each bird's nest again;

[0133] Step 4: Randomly update the nest locations other than the optimal solution of the GWO algorithm based on the preset discovery probability Pa. The random update process includes: generating a random number r for a nest location to be randomly updated, and comparing r with the discovery probability Pa; if r>Pa, updating the nest location by random walking; otherwise, the nest location remains unchanged;

[0134] Step 5: Calculate the fitness values ​​of all bird nest positions again, and record the bird nest position corresponding to the best fitness value as the optimal solution of the current round;

[0135] Step 6: Determine whether the termination condition is met. If so, output the optimal solution of the current round as the final result. If not, return to step 2 and proceed to the next round of iteration. The termination condition is that the number of iterations reaches the maximum number of iterations Tmax.

[0136] In specific implementation, the pressure value in the power optimization model calculation optimization process solution meets the following conditions:

[0137] |H i -H j |≤0.2;

[0138] Where H i is the constant outlet pressure of the i-th water supply period in the control scheme; H j Calculate the pressure value in the optimization process solution for the corresponding water supply period.

[0139] In this way, the effectiveness of the optimal power control solution finally obtained can be guaranteed.

[0140] The main advantage of the cuckoo algorithm lies in its use of the Lévy flight mechanism to generate motion trajectories. This highly randomized flight method enables the search process to rapidly explore the solution space, thereby increasing the diversity of the population. However, the standard cuckoo algorithm relies entirely on Lévy flights to update the nest location. This mechanism has certain limitations in local search capabilities, resulting in low optimization accuracy, weak local optimization capabilities in the later stages, and slow convergence.

[0141] The problem with the Gray Wolf Optimization Algorithm is that the alpha wolf in its core pack doesn't always move toward the global optimal solution. Meanwhile, other pack members tend to gravitate toward the leader's top three solutions, making it prone to getting stuck in local optima and resulting in a relatively weak global search capability. In practice, the Gray Wolf Algorithm performs poorly when solving complex optimization problems because its original algorithm's relatively simple search strategy makes it difficult to effectively find the global optimal solution.

[0142] Analysis reveals that the Cuckoo Algorithm and the Gray Wolf Optimization Algorithm complement each other in their respective strengths and limitations. The Gray Wolf Optimization Algorithm possesses strong local search capabilities, while the Lévy flight strategy in the Cuckoo Algorithm can lead to a more diverse population. To fully leverage the strengths of both algorithms, the position update strategy from the Gray Wolf Optimization Algorithm can be incorporated into the Cuckoo Algorithm. This aims to reduce the blindness of the Lévy flight search, enhance the local search capability of the Cuckoo Algorithm, and improve the optimization accuracy of the algorithm.

[0143] The hybrid improved Cuckoo Search Algorithm (GWO-CS) combines the strengths of two algorithms, aiming to balance global and local search capabilities to enhance optimization performance. By combining the strengths of both algorithms, GWO-CS is able to perform rapid global searches while, in later stages, fine-tuning the solution space through the local search mechanism of the CS algorithm. This hybrid approach enables the algorithm to simultaneously optimize local optimal solutions while searching for the global optimal solution, thereby improving its performance.

[0144] Among the optimization methods described above, the hybrid improved cuckoo algorithm combines the randomness and long step size of the Lévy flight strategy, enabling the algorithm to rapidly explore the search space and escape from local optimal solutions, thus improving global search capabilities. The GWO algorithm's update strategy, leveraging the social hierarchy and hunting behavior of gray wolf packs to guide search direction, enhances the algorithm's local search capabilities and enables it to more accurately approach the global optimal solution. The Lévy flight strategy enables the algorithm to extensively explore the solution space in the early stages of the search, identifying potential high-quality solution regions. The GWO algorithm strengthens the detailed search for high-quality solutions in the later stages of the search, improving solution accuracy. This balance of exploration and exploitation makes the algorithm more efficient and stable in solving complex optimization problems. Furthermore, random updates based on a preset discovery probability Pa increase the algorithm's randomness and diversity, helping to prevent premature convergence. The random walk method for updating nest locations allows the algorithm to maintain a certain degree of flexibility during the search process, adapting to varying search environments and problem characteristics. Furthermore, the optimal power control problem for water pump units often involves multiple variables and complex constraints, making it difficult to effectively solve with traditional optimization methods. The hybrid improved Cuckoo algorithm combined with the GWO algorithm, through its powerful global and local search capabilities, can effectively handle such complex optimization problems and find high-quality solutions. This approach ensures the effectiveness of the resulting optimal power control solution.

[0145] Example 2

[0146] In order to better illustrate the performance of the optimization method of the hybrid improved cuckoo algorithm (GWO-CS algorithm) in this method, the following algorithm performance test description is carried out.

[0147] In order to test the performance of the GWO-CS algorithm, the Schwefel test function was selected for optimization test experiments, and a control group with the original algorithm was set up. The effect of the algorithm performance improvement was analyzed through the optimization results to verify the correctness of the improvement strategy.

[0148] The Schwefel function is a typical deception problem. It has a global minimum point that is far away from another local minimum point. Once it falls into the local optimum, it is difficult to jump out. Therefore, this type of function can be used as an ideal test function to test the global optimization performance of the improved algorithm.

[0149] We select two typical function formulas and name them f1(x) and f2(x). Their function expressions and performance test results are shown below:

[0150] f1(x)=max{|x i |,1≤i≤30},-100≤x i ≤100;

[0151] min(f1)=f1(0,…,0)=0;

[0152] from Figure 3 It can be seen that GWO-CS has a significant improvement in convergence speed compared with the original CS algorithm. Figure 4 It can be seen from the comparison that the traditional algorithm falls into the local optimal solution during the optimization process. The improved GWO-CS algorithm has significantly improved its performance in this aspect, and its optimization results are significantly better than the traditional CS algorithm.

[0153] The expression and test results of another Schwefel class test function f2(x) are as follows:

[0154]

[0155] min(f2)=f2(0,…,0)=0;

[0156] Similar to the test result analysis of the previous function, Figure 5 It can be seen that GWO-CS has a significant advantage in the convergence speed during the optimization process, and Figure 6 In the comparison, the traditional CS algorithm also exposed the problem of falling into the local optimal solution, while the global performance of the optimal solution of the GWO-CS algorithm is significantly better than that of the traditional CS algorithm.

[0157] Test result analysis

[0158] By comparing the optimization solution results, it can be found that the improved GWO-CS algorithm shows obvious advantages in convergence speed. In contrast, the original CS algorithm is easily disturbed by local optimal points and has difficulty in escaping the local optimal solution. However, the GWO-CS algorithm can quickly converge to the global optimal point and has higher solution accuracy. When optimizing the test function, it was observed that the GWO-CS algorithm showed obvious advantages over the original CS algorithm in terms of convergence speed and solution accuracy, and was not prone to falling into local optimal solutions. It showed good optimization effects on both Schwefel-type test functions and had good robustness. Therefore, it can be considered that the hybrid improved cuckoo algorithm provides a more effective optimization method for pump station operation optimization problems.

[0159] Example 3

[0160] In order to better illustrate the effect of this method, a specific example is used for illustration.

[0161] Basic Situation

[0162] A certain urban area in S Province covers an area of ​​46 square kilometers and has a registered population of 87,000. Its water supply is transferred by an intermediate pressure pump station with a water supply capacity of 20,000 tons / day. The pump station is supplied with water by three speed-regulating pumps of the same model (two in use and one in reserve). The rated flow rate of the pumps is 550m 3 / h, lift 65-72m, all controlled by variable frequency.

[0163] Current situation and existing problems

[0164] After field investigation, it was found that the pump stations in the study area had the following operational problems:

[0165] Problem 1: The pump station consumes a lot of energy during operation. Due to the extensive existing pressure regulation method, a relatively high surplus water pressure is generated during the actual operation of the pump station. This part of the head that exceeds the actual demand not only increases the energy loss of the pump station operation, but also causes the pipeline system to bear excessive pressure.

[0166] Problem 2: The pressure distribution of the water supply network system is unreasonable. The current pump station outlet pressure adopts the traditional control method of ensuring water supply at the most unfavorable point during the highest daily peak. This method lacks flexibility. The network pressure is significantly affected by the fluctuation of user water consumption. The network system pressure is relatively high during the low-peak period of water consumption in the early morning, which aggravates network leakage and is prone to pipe burst accidents.

[0167] ① High energy consumption of pumping station

[0168] The pump station adopts the traditional mode of ensuring the most unfavorable point pressure supply at the highest point on the highest day in terms of pressure management, controlling the outlet pressure of the pump station to be constant at 81m3 for water supply. In many periods of time, this value is much higher than the actual demand of the pipe network. The required pressure value of the pump station outlet is estimated by taking the highest water consumption day in the statistical historical data as follows: Figure 7 As shown in the figure, it can be seen that due to the extensive management mode adopted by the booster pump station in pressure control, there is a lot of excess pressure in the pipeline network during the low-peak water consumption period, resulting in a lot of unnecessary energy waste.

[0169] ②Irrational pipe network pressure control

[0170] Since the project city is a mountainous city with large terrain differences, under the traditional pressure management mode, the extensive constant pump station outlet pressure will face the risk of overpressure and pipe burst in the high-pressure area of ​​the low-lying area when the water consumption increases. Figure 8 This is a statistical record of pipe burst repairs from 2021 to 2023. It can be seen that the number of pipe burst repairs per year is over 100, the leakage rate calculated based on the difference between production and sales is high, and has been increasing in recent years with the increase in water consumption. The areas with the highest incidence of pipe burst accidents are mostly concentrated in two low-lying areas. It can be considered that the severe pressure fluctuations in the pipeline system are the main cause of the frequent pipe burst accidents in this area, indicating that the extensive pipe network pressure management model in this area is outdated.

[0171] Applied Research Framework

[0172] The water usage data for the peak water usage days in summer and winter, as well as the 30th day of each month, were used as the background data source for the project case study. Based on the results of the on-site survey, the actual operating water inlet pressure of the pump station is 0.24 MPa, and the water supply pressure at the pump station outlet is controlled at a constant 0.81 MPa throughout the day. Based on the actual operating data of the pump station in 2023 provided by the staff, the peak daily water usage in winter and summer was selected and summarized as shown in Table 1:

[0173] Table 1

[0174]

[0175] Take a 20,000 tons / day water consumption booster pump station in a mountain town in S Province as an example. Figure 9 This is a topological model diagram of the pumping station and its pipe network system. The pump outlet and the most unfavorable point in the pipe network are marked in the diagram. The water pressure at the most unfavorable point is required to be no less than 26m. The booster pump station routinely operates with two variable-frequency speed-regulating pumps, using the original constant outlet pressure scheme and time-varying pressure regulation. An optimization algorithm was used to simulate the EPANET 2.0 hydraulic system to explore the energy-saving effects of the optimized scheme.

[0176] Determination of pump curve parameters

[0177] (1) Performance curve fitting determination

[0178] The water delivery capacity of the booster pump station in the study area is 20,000 tons / day. There are three water pumps in the pump station (two in use and one in standby), all of which are variable frequency controlled speed regulating pumps with a rated flow of 780m 3 / h, the rated head is 0.72MPa, the rated efficiency is 82%, the rated speed is 1480r / min, and the water pump sample data are collected to obtain the discrete points on the QH curve and the QP curve, as shown in Table 2.

[0179] Table 2

[0180]

[0181] The basic performance QH curve, QP and Q-η curve of the water pump at rated speed are fitted using the least squares method based on the discrete data in the above table.

[0182] The fitting parameters obtained from the curve fitting process are summarized in Table 3.

[0183] Table 3

[0184]

[0185] Determination of time-sharing voltage regulation strategy

[0186] (1) Pump station outlet pressure prediction

[0187] The pressure prediction method in this method is used to predict the water outlet pressure on the highest day in summer and the highest day in winter. The water outlet pressure of the pump station within ten days is taken as the original data, the first nine days are used as the training set, and the last day, which is the day with the highest water consumption, is used as the prediction set. The prediction results are as follows Figure 10 and Figure 11 shown.

[0188] The data in the figure shows that the predicted pressure curves generally follow the actual pressure values, regardless of summer or winter, and there is no significant difference in prediction performance between seasons. This model's prediction results can be considered sufficient for subsequent method applications.

[0189] (2) Time-based voltage regulation strategy

[0190] Based on the predicted values ​​of the pump station outlet pressure on the highest day in summer and the highest day in winter, the pump station outlet pressure control plan for each time period of the day is determined, so that the supply pressure in each time period is guaranteed while the outlet pressure in some time periods is also reduced. Figure 12 and Figure 13 shown.

[0191] The pressure values ​​set for each time period can cover the pump outlet pressure curve. It is found that due to the difference in water consumption in winter and summer, the results of the pump station's time-based pressure regulation strategy will also vary greatly. Therefore, due to the different daily water consumption, the pump station outlet pressure curve will also change accordingly. In order to ensure the accuracy and applicability of the daily time-based pressure regulation strategy, it is recommended to adjust the pressure regulation period and pressure regulation value on a daily basis according to actual needs.

[0192] Once the time-based pressure regulation strategy is determined, the water supply pressure value for each time period is determined. Next, an optimization algorithm needs to be used to optimize the pump start-up and shutdown combinations and speed ratios in the pump station to obtain the optimal combination solution with the lowest energy consumption.

[0193] Verification of the applicability of GWO-CS algorithm for pump station optimization operation

[0194] To explore the applicability of the GWO-CS algorithm for solving the mathematical model for optimal pump station operation, the following algorithm environment was established: a 24-hour solution step was set, the VMD-LSTM model's predicted flow rate was used as the input, and the 24-hour pressure value determined by the pump station's time-based pressure regulation strategy was used as the pressure input. The algorithm then optimized the operating pump combination and the speed ratio of the speed-regulating pumps corresponding to each time period. To improve the applicability of the output optimization solution, and considering that the pressure differential in each time period in the time-based pressure regulation strategy does not exceed 3 meters, a pressure optimization range was set in the program every 3 meters of head, thereby appropriately narrowing the algorithm's optimization range.

[0195] The parameter settings for the GWO-CS algorithm to optimize the pump station operation problem are as follows: the population size is set to 30, and the maximum number of iterations is set to 100. The optimization iteration diagrams under different pressure range requirements obtained by Matlab2022b software programming are as follows: Figures 14 to 17 As shown in the figure. From the iteration diagram of the pump station optimization scheme under different pressure range requirements, it can be seen that the pump station operation optimization based on the GWO-CS algorithm has high calculation accuracy and fast iteration speed, and quickly converges to the optimal solution under different pressure range requirements. The different matching flow rates under the corresponding pressure range and the different start-up and shutdown schemes of the pump units will lead to differences in the difficulty of the optimization process. In the optimization process of the pump station operation scheme under different conditions based on the GWO-CS algorithm, the optimal solution is converged within 40 iterations, and the average calculation time is 0.52s, showing good optimization convergence ability and calculation speed. It can be considered that the GWO-CS algorithm is applicable and feasible when solving pump station optimization operation problems.

[0196] Pumping station optimization operation scheme based on GWO-CS algorithm

[0197] To ensure the normal operation of the pump station and reduce operating costs, a scientific and reasonable daily operation plan for the pump station is necessary. This optimization plan needs to meet the water supply demand in each time period while effectively reducing daily operating costs. Computer technology is used to perform optimization calculations, taking into account constraints such as flow rate, head, and number of pump units. The optimal daily operation plan for the pump station is found, ensuring that the pump station operation plan achieves optimal efficiency and economic benefits, avoiding the uncertainty of blindly adjusting operations based on experience.

[0198] The GWO-CS algorithm is used to determine the optimal pump station operation plan for each time period. This involves optimizing the most efficient pump operation combination and the speed ratio for the speed-regulating pumps within each time period. Furthermore, the efficiency of the operating pumps under each plan is calculated. The optimized results are shown in Tables 4 and 5.

[0199] Table 4 Operation status of optimized pressure regulation control scheme for booster pump station with water consumption in winter

[0200]

[0201]

[0202] Table 5 Operation status of optimized pressure regulation control scheme for booster pump station with water consumption in summer

[0203]

[0204] By observing the daily operation optimization plan, we can see that water consumption varies throughout the day. The GWO-CS algorithm can determine the optimal pump activation combination and speed ratio for the speed-regulating pump based on the water consumption in each time period. The optimized pump control allocation is more reasonable. Not only does it optimize the pump operating state while meeting water supply demand, but the control rhythm of the optimized plan also avoids frequent starting and stopping of the pump units, thereby reducing pump wear and extending their service life.

[0205] Through optimization calculations, computer technology can be used to assist in developing daily operation plans for pumping stations to achieve economical and efficient operation. This method not only improves the scientific and rational operation of pumping stations, but also effectively reduces the operating costs of pumping stations.

[0206] Energy saving effect analysis

[0207] (1) Analysis of pump unit working efficiency

[0208] For pump units, the key to saving energy consumption lies in ensuring that the pump operates in the high-efficiency range of the frequency conversion, so it is crucial to adjust the frequency conversion parameters reasonably. Therefore, with the user's water volume and head supply as the constraint condition, the minimum speed ratio of the pump unit frequency regulation is limited. The GWO-CS algorithm is used to calculate the optimal speed ratio of the pump unit operating within the constraint condition. The optimized solution results are sorted out and plotted into the efficiency-flow-head relationship as shown in the figure. Figure 18 and Figure 19 In the efficiency-flow-head relationship diagram, the higher the point position, the higher the pump efficiency during that period. If the points at high positions are more "dense", it means that the pump has good efficiency within a certain range of operating conditions. If the points at high positions are more dispersed, it means that the pump has good efficiency within a wider range of operating conditions.

[0209] from Figure 18 and Figure 19It can be seen that under the control strategy of the optimization scheme, the working efficiency of each pump unit is above 72% regardless of winter or summer, and most points are at high levels, indicating that the pump unit maintains a relatively high efficiency working state in most periods. The proportion of the efficient operation time of the internal water pump to the entire daily operation time is statistically analyzed, and the results are shown in Table 6.

[0210] Table 6

[0211]

[0212] The statistical results in Table 6 show that under the optimized solution, the pumps' operating efficiency exceeded 75% for over 80% of the time within a single day. High-efficiency operating periods exceeding 80% accounted for nearly 30% of the time in winter and over 20% in summer. Overall, the optimized solution maintained high pump efficiency throughout the day, indicating significant improvements in pump energy consumption and effective utilization of energy at most times.

[0213] (2) Analysis of electricity consumption costs

[0214] After using the GWO-CS algorithm to determine the optimal pump operation combination and speed ratio for each time period, the total pump power for each time period under the corresponding solution is calculated based on seasonal water consumption. This calculated total power can then be compared with the power consumption under the original solution. The comparison results can be used to evaluate the effectiveness of the improved solution. If the total power under the new solution is lower than the original solution, it means that the energy consumption of operating the water pumps has been reduced, thus achieving the energy conservation goal. Conversely, if the total power under the new solution is higher, the effectiveness of the solution needs to be reassessed.

[0215] In this way, the total power calculated for the running pumps under different seasonal water usage conditions of the optimization scheme given by the GWO-CS algorithm can be objectively evaluated and compared with the power consumption of the original scheme. The comparative analysis results are shown in Tables 7 and 8.

[0216] Table 7 Power consumption analysis of the optimized voltage regulation strategy in winter

[0217]

[0218]

[0219] Table 8 Power consumption analysis of the optimized voltage regulation strategy in summer

[0220]

[0221]

[0222] The results in the table above show that the optimized solution, operating both variable frequency pumps in full frequency conversion mode under high flow conditions, significantly reduces energy consumption and achieves significant energy savings. However, under lower flow conditions, fully variable frequency operation of a single pump fails to fully utilize the advantages of frequency conversion, resulting in relatively small energy savings. In winter, the highest hourly energy savings reached 24.2%, with an average daily energy savings of 13.4%. In summer, the highest hourly energy savings reached 32.2%, with an average daily energy savings of 18.1%. The city's base electricity rate for this water supply pump station is 0.6583 yuan / kWh, with no off-peak or off-peak electricity rates. The estimated annual electricity savings are 181,187.99 yuan.

[0223] Based on the above daily operation optimization results of the pumping station, it can be concluded that by applying the GWO-CS optimization algorithm to adjust the control strategy of the pumping station, the problem of blindly adjusting the operation based on experience is avoided, the operating efficiency of the pumping station is significantly improved, and the economic operation of the pumping station is achieved. This provides a reference for further improving the scientific and green automation control goals of water supply projects.

[0224] Analysis of operating pressure of pipe network system

[0225] The original plan maintained a constant pump station outlet pressure 24 hours a day. However, this unfavorable location would experience excessive pressure loss during nighttime hours when water demand is low. Furthermore, pressure at this location would fluctuate significantly throughout the day due to fluctuations in water demand. The pump station outlet pressure is regulated based on the predicted water volume, appropriately reducing it at night and adjusting it based on fluctuations in water demand. Table 9 shows basic information about the pressure monitoring points.

[0226] Table 9

[0227]

[0228] After adjusting the pump station operation strategy, the pressure value at the most unfavorable point is measured again and compared with the measured value under the original scheme before adjustment. Figures 20 to 22 shown.

[0229] (1) Analysis of pressure drop in the most unfavorable pipe section

[0230] from Figure 20 It can be seen that after adjusting the booster pump station's operating strategy, the pressure at the most unfavorable point decreased significantly at night, and the overall fluctuation trend was more stable than before. The pressure detected that day was 2.46m lower than under the original operating plan. This indicates that the optimized operation of the pump station reduced unnecessary pressure waste at the most unfavorable point and stabilized its pressure fluctuations.

[0231] (2) Analysis of pressure reduction in pipe sections in overpressure areas

[0232] Based on the collected historical statistical records, a pressure analysis was conducted on two high-risk areas with low-lying terrain and frequent pipe bursts. Both areas had obvious overpressure during the nighttime hours. The pressure value at the No. 1 overpressure detection point fluctuated greatly. The average pressure of the two areas on the monitoring day significantly exceeded the pressure required for calculation. After adjusting the pump station operation strategy, the pressure values ​​at the two overpressure points were re-measured and compared with the measured values ​​under the original scheme before the adjustment. Figure 21 and Figure 22 As shown in the figure, after the optimized operation of the pump station adjusted the pipeline network pressure, the pressure values ​​at the two overpressure points were significantly reduced at night, the pressure fluctuations became more stable, and the pressure values ​​at the two nodes were generally reduced. The pressure value of the No. 1 overpressure detection point decreased by an average of 3.49m per day, and the pressure value of the No. 2 overpressure detection point decreased by an average of 4.16m per day. The pressure values ​​at both locations are closer to the actual required pressure values, which not only eliminates a lot of unnecessary pressure waste, but also reduces the risk of pipe burst.

[0233] summary

[0234] Taking the actual engineering booster pump station and water supply network as an example, the actual operation status of the pump station and the problems existing in the pipeline network were analyzed. The pump station optimization operation strategy based on the GWO-CS algorithm was applied to the urban pump station project example. The optimization scheme of the pump station water pump combination and speed ratio distribution at 24 moments was obtained with a single day as the research scale. The optimized scheme was compared with the traditional scheme to explore the energy-saving effect of the optimized scheme in actual engineering.

[0235] The main conclusions from the engineering case study are as follows: In terms of energy conservation and consumption reduction, the optimized scheme maintains high pump efficiency across all time periods, demonstrating its significant energy-saving potential. Comparisons with historical energy consumption further validate the optimized scheme's energy-saving potential. The results show that average daily energy savings in winter are 13.4%, and in summer are 18.1%, with an estimated annual electricity bill savings of 181,187.99 yuan. Regarding reducing pipeline pressure during low-peak periods, the water supply system, operating under the time-based pressure regulation strategy, experienced no underpressure. While ensuring normal water supply demand, the pressure during some low-peak periods was appropriately reduced. Analysis of several representative pressure measurement points in the system revealed that the most unfavorable point experienced a maximum pressure reduction of 7 m during low-peak periods, with an average daily reduction of 2.46 m. ​​In overpressure areas, the pressure during low-peak periods decreased by 6-7 m, with an average daily reduction of 3-4 m. Overall, the problem of overpressure operation in the pipeline system has been effectively improved, which is of great significance for reducing the pipeline leakage rate and the burst pipe maintenance rate.

[0236] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the technical solutions. Those skilled in the art should understand that modifications or equivalent replacements of the technical solutions of the present invention that do not depart from the purpose and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for regulating the outlet pressure of a pump station for a water supply network, characterized in that: The following steps are involved: S1. Construct a pump station outlet pressure prediction model; use it to predict the pump station outlet pressure in the next N hours based on the historical water supply flow sequence data, the pressure sequence data of the pressure measuring point, and the water supply pressure sequence data required by the pump station; S2. Obtain historical training data and preprocess it to obtain model training data; the historical training data includes historical water supply flow sequence data, pressure sequence data of pressure measuring points, and water supply pressure sequence data required by the pump station; S3, using the model training data obtained in S2, trains the pump station outlet pressure prediction model constructed in S1; S4. Predict the pump station outlet pressure in the next N hours using the trained pump station outlet pressure prediction model; S5. Based on the prediction result of S4, the outlet pressure of the pumping station is divided into multiple water supply periods; When dividing the water supply period, based on the predicted fluctuation of the pump station outlet pressure, the maximum continuous time interval in which the fluctuation value is less than the preset value is taken as a water supply period; S6. For each water supply period, the maximum pump station outlet pressure value during the water supply period is used as the constant outlet pressure of the water supply period; and the constant outlet pressures of each water supply period are integrated as a control scheme; S7. According to the control scheme of S6, actual pump station outlet pressure control is performed, and corresponding constant outlet pressure is provided in each different water supply period; wherein, when the corresponding constant outlet pressure is provided in one water supply period, the optimal power control scheme of the water pump unit is solved by a preset power optimization model, and the water pump unit is controlled according to the obtained optimal power control scheme; When solving the optimal power control scheme of the water pump unit through the power optimization model, the solution is obtained through an optimization method based on the hybrid improved cuckoo algorithm. The workflow of the optimization method based on the hybrid improved cuckoo algorithm includes: Step 1: Randomly generate N bird nests as the initial population, record the initial optimal bird nest location, and perform parameter initialization settings; Step 2: Use the Levy flight strategy to update the locations of other bird nests except the current best nest location, calculate the fitness value of each updated bird nest, and then use the nest location with the largest fitness value among all the bird nests as the new best nest location; Step 3: Use the update strategy of the GWO algorithm to update the location of each bird's nest again; Step 4: Randomly update the nest locations other than the optimal solution of the GWO algorithm based on the preset discovery probability Pa. The random update process includes: generating a random number r for a nest location to be randomly updated, and comparing r with the discovery probability Pa; if r>Pa, updating the nest location by random walking; otherwise, the nest location remains unchanged; Step 5: Calculate the fitness values ​​of all bird nest positions again, and record the bird nest position corresponding to the best fitness value as the optimal solution of the current round; Step 6: Determine whether the termination condition is met. If so, output the optimal solution of the current round as the final result; if not, return to step 2 and perform the next round of iteration.

2. The method for regulating the outlet pressure of a pump station for a water supply network according to claim 1, wherein: The objective function of the power optimization model is: Where c is the electricity price during the period; n is the number of speed regulating pumps; ω i To control the opening and closing coefficient of the i-th pump, the value is 0 or 1; Q i is the output flow of the i-th pump; S i is the speed ratio of the i-th pump; d 0i d 1i d 2i is the power fitting coefficient of the i-th pump.

3. The method for regulating the outlet pressure of a pump station for a water supply network according to claim 1, wherein: The constraints of the power optimization model include single pump flow constraint, pump head constraint, single pump shaft power constraint, number of units in operation constraint, and speed ratio constraint.

4. The method for regulating the outlet pressure of a pump station for a water supply network according to claim 3, wherein: The single pump flow constraint is: Q min <Q i <Q max ; Where Q i is the flow rate of the i-th pump in the pumping station; Q min , Q max are the minimum flow rate and maximum flow rate of the i-th water pump respectively; The pump head constraint is: H min <H i <H max ; Where H i is the head of the i-th water pump; H min 、H max are the minimum head and maximum head of the i-th water pump respectively.

5. The method for regulating the outlet pressure of a pump station for a water supply network according to claim 3, wherein: The power constraint of a single pump shaft is: P min <P i <P max ; Where, P i is the shaft power of the i-th water pump; P min 、P max are the minimum and maximum shaft power of the i-th pump in the pumping station respectively; The number of startup units is constrained as follows: 0<n<n max ; Where n is the number of pumps in operation in the pumping station, n max The preset maximum number of bootable units.

6. The method for regulating the outlet pressure of a pump station for a water supply network according to claim 3, wherein: The speed ratio constraint is: S i ∈[S imin ,1]; Where S i is the speed ratio of the i-th pump; S imin is the minimum speed ratio of the i-th pump.

7. The method for regulating the outlet pressure of a pump station for a water supply network according to claim 1, wherein: In step 1, the initialization parameters include the abandonment probability Pa, the maximum number of iterations Tmax, and the parameters of the GWO algorithm; In step 6, the termination condition is that the number of iterations reaches the maximum number of iterations Tmax.

8. The method for regulating the outlet pressure of a pump station for a water supply network according to claim 1, wherein: The pressure value in the power optimization model calculation optimization process satisfies the following conditions: |H i -H j |≤0.2; Where H i is the constant outlet pressure of the i-th water supply period in the control scheme; H j Calculate the pressure value in the optimization process solution for the corresponding water supply period.

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