Water pump energy-saving optimization control method and system

Abstract

The invention relates to the technical field of water pump control, and discloses a water pump energy-saving optimization control method and system. The method comprises the steps of obtaining water pump outlet pressure, pipe network tail end flow and water pump motor power monitoring signals; calculating a flow-pressure propagation time compensation coefficient and a pressure fluctuation compensation value to eliminate the influence of signal propagation lag and amplitude fluctuation; extracting an operation condition feature vector by combining the motor power frequency domain energy distribution feature and the pressure fluctuation compensation value, and matching the operation condition feature vector with an energy-saving parameter mapping rule base to generate an energy-saving parameter combination; correcting a water pump efficiency reference curve in real time based on the combination, outputting an efficiency attenuation factor, and generating a control deviation value in combination with a target water supply demand; according to the control deviation value, the water pump rotating speed adjusting quantity and a start-stop priority sequence are calculated, and the priority sequence is optimized in combination with historical operation data to generate a machine number control instruction; and finally synthesizing a control signal to drive an execution mechanism.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water pump control, in particular to a water pump energy-saving optimization control method and system. BACKGROUND

[0002] In the current industrial production, urban water supply, building heating and ventilation fields, as the core equipment of fluid transportation, the operation energy consumption of water pump accounts for a high proportion in the overall system energy consumption. With the increasingly prominent energy shortage problem and the continuous improvement of energy saving and environmental protection requirements, the energy-saving operation of water pump system has become the focus of the industry. The traditional water pump control method mostly adopts a fixed parameter-based constant value control mode, that is, the start-stop control and speed regulation are performed according to the preset outlet pressure or flow threshold. This control method fails to fully consider the dynamic characteristics of the pipe network system, resulting in many problems in the operation process.

[0003] In actual operation scenarios, the fluid transmission of the pipe network system has obvious hysteresis, and there is a time difference between the propagation of the water pump outlet pressure signal and the pipe network end flow signal. The traditional control method does not compensate for this time difference, so that the issuance of the control instruction does not match the actual pipe network demand, and the pressure overshoot or flow shortage may occur. At the same time, the pipe network system is easily affected by factors such as water load fluctuation, pipe resistance change, and equipment aging during operation, resulting in amplitude fluctuations of the pressure and flow signals. The traditional control method lacks an effective fluctuation compensation mechanism, further exacerbating the deviation of control accuracy.

[0004] The traditional water pump control method mostly relies on a single parameter for working condition judgment, such as judging the operating condition only by motor power or outlet pressure, without considering the correlation characteristics of multiple parameters, resulting in low accuracy of working condition identification. When the system operating condition changes, the energy-saving parameters cannot be adjusted in time, so that the water pump is operated in a non-efficient interval for a long time, causing a large amount of energy waste. At the same time, in the multi-pump parallel operation scenario, the determination of the water pump start-stop priority by the traditional control method is mostly based on experience setting and does not combine historical operation data for optimization, resulting in unreasonable pump set operation combination and further reducing the overall system operation efficiency.

[0005] In the traditional water pump control method, the water pump efficiency reference curve is mostly a fixed curve, without considering the efficiency decay problem during equipment operation. With the increase of use time, the deviation between actual efficiency and reference curve gradually increases, and the control instruction based on the fixed reference curve cannot achieve precise energy-saving control. These problems collectively result in that the traditional water pump control method cannot meet the current industry demand in terms of energy-saving effect and control accuracy, and an optimization control method that can comprehensively consider the dynamic characteristics of the pipe network, multi-parameter working condition identification, efficiency decay compensation, and intelligent number control is urgently needed to improve the energy-saving level and operation stability of the water pump system. SUMMARY

[0006] The application aims to provide a water pump energy-saving optimization control method to solve the problems in the background art.

[0007] To achieve the above-mentioned purpose, the application provides a water pump energy-saving optimization control method, which comprises the following steps: acquiring a water pump outlet pressure monitoring signal, a pipe network terminal flow monitoring signal and a water pump motor power monitoring signal; calculating a flow-pressure propagation time compensation coefficient according to the propagation characteristics between the water pump outlet pressure monitoring signal and the pipe network terminal flow monitoring signal, and generating a pressure fluctuation compensation value based on the amplitude fluctuation correlation of the compensated pressure monitoring signal and flow monitoring signal within a sliding time window; extracting an operating condition characteristic vector according to the energy distribution characteristics of the water pump motor power monitoring signal in the frequency domain and the pressure fluctuation compensation value; matching the operating condition characteristic vector with a preset energy-saving parameter mapping rule library to generate a matched energy-saving parameter combination; correcting a water pump efficiency reference curve in real time according to the matched energy-saving parameter combination, and outputting an efficiency attenuation factor; generating a control deviation amount based on the efficiency attenuation factor and a target water supply demand; calculating a water pump rotating speed adjustment amount and a water pump start-stop priority sequence according to the control deviation amount; optimizing the water pump start-stop priority sequence in combination with historical operation data to generate a number control instruction; synthesizing a control signal by combining the water pump rotating speed adjustment amount and the number control instruction, and driving a water pump actuator.

[0008] Preferably, the calculation of the flow-pressure propagation time compensation coefficient comprises the following steps: presetting an initial iteration value and an iteration step length of the propagation time compensation coefficient; constructing a cross-covariance function by using the water pump outlet pressure monitoring signal and the pipe network terminal flow monitoring signal at different sampling times; calculating the function value of the cross-covariance function in the iteration process, and taking the propagation time corresponding to the maximum function value as the flow-pressure propagation time compensation coefficient.

[0009] Preferably, the extraction of the operating condition characteristic vector comprises the following steps: performing wavelet packet decomposition on the compensated pressure monitoring signal to extract the energy proportion of a preset frequency band; performing weighted fusion on the energy proportion and the pressure fluctuation compensation value to generate a frequency domain operating condition index; superimposing the frequency domain operating condition index and the harmonic distortion rate of the water pump motor power monitoring signal to constitute the operating condition characteristic vector.

[0010] Preferably, the generating the matching energy-saving parameter combination comprises: Indexing a reference feature vector with the minimum Euclidean distance from the operating condition feature vector from the energy-saving parameter mapping rule library; Extracting the head-flow reference parameter, efficiency threshold parameter and rotating speed constraint parameter associated with the reference feature vector; Combining the head-flow reference parameter, efficiency threshold parameter and rotating speed constraint parameter into the matching energy-saving parameter combination.

[0011] Preferably, the real-time correction of the water pump efficiency reference curve comprises: Querying the theoretical efficiency value of the water pump efficiency reference curve according to the pipe network terminal flow monitoring signal at the current time; Calculating the relative deviation between the theoretical efficiency value and the actual efficiency value derived from the real-time water pump motor power monitoring signal; Multiplying the relative deviation by the efficiency attenuation factor to update the curvature coefficient of the water pump efficiency reference curve.

[0012] Preferably, the generating the control deviation amount comprises: Analyzing the target head and target flow according to the target water supply demand; Multiplying the difference between the actual head and the target head by the efficiency attenuation factor to generate the head control deviation component; Multiplying the difference between the actual flow and the target flow by the efficiency attenuation factor to generate the flow control deviation component; Normalizing and weighting the head control deviation component and the flow control deviation component to generate the control deviation amount.

[0013] Preferably, the calculating the water pump rotating speed adjustment amount comprises: Inputting the control deviation amount into a proportional-integral-derivative controller to output an initial rotating speed adjustment amount; Sensitivity correcting the initial rotating speed adjustment amount according to the current slope of the water pump efficiency reference curve; Limiting the change rate threshold of the corrected rotating speed adjustment amount to generate the final water pump rotating speed adjustment amount.

[0014] Preferably, the optimizing the water pump start-stop priority sequence comprises: Statistically analyzing the cumulative running time and start-stop frequency of each water pump; Adjusting the weight coefficient of the cumulative running time according to the efficiency attenuation factor; Arranging the cumulative running time corrected by the weight coefficient in descending order to generate the water pump start-stop priority sequence.

[0015] Preferably, the synthesized control signal comprises: Converting the water pump rotating speed adjustment amount into a pulse width modulation duty cycle instruction; Encode the number of control instructions as a relay group control code; Synchronously output the pulse width modulation duty cycle instruction and the relay group control code to the water pump actuator.

[0016] Preferably, the present application also includes a water pump energy-saving optimization control system, comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned water pump energy-saving optimization control method when executing the computer program.

[0017] Compared with the prior art, the present application has the following beneficial effects: The method builds the foundation of multi-parameter collaborative control by acquiring three types of key monitoring signals: water pump outlet pressure, pipe network end flow and motor power. Compared with the traditional single parameter control mode, it can more comprehensively reflect the system running state. For the hysteresis problem of pipe network fluid transmission, the time difference between the outlet pressure and the end flow signal is compensated by calculating the flow-pressure propagation time compensation coefficient, and the pressure fluctuation compensation value is generated based on the amplitude fluctuation correlation of the pressure and flow signals in the sliding time window, effectively eliminating the influence of signal propagation hysteresis and amplitude fluctuation on control accuracy, making the control instruction accurately match the actual demand of the pipe network, avoiding pressure overshoot and insufficient flow, and improving the stability of system operation.

[0018] In the working condition identification link, the method extracts the operating condition characteristic vector by combining the frequency energy distribution characteristics of the motor power monitoring signal and the pressure fluctuation compensation value, realizing multi-parameter fusion of working condition identification. This working condition identification method fully considers the frequency domain characteristic differences of motor power under different working conditions, and integrates the accurate signal after pressure fluctuation compensation, which greatly improves the accuracy of working condition identification compared with the traditional single parameter working condition judgment. By matching the working condition characteristic vector with the preset energy-saving parameter mapping rule library, the energy-saving parameter combination suitable for the current working condition can be quickly generated, ensuring that the water pump always operates in the high efficiency interval and reducing energy waste caused by working condition misjudgment.

[0019] The method introduces the concept of efficiency attenuation factor, which effectively solves the problem of mismatch between traditional fixed reference curve and actual efficiency attenuation by real-time correction of water pump efficiency reference curve. During system operation, the efficiency reference curve is dynamically adjusted according to the matched energy-saving parameter combination, accurately reflecting the current actual efficiency level of the water pump, and the control deviation generated based on the reference curve is more in line with the actual operation demand, providing accurate basis for subsequent speed regulation and number control, avoiding control deviation caused by efficiency attenuation, and further improving the accuracy of energy-saving control.

[0020] In the multi-pump parallel operation scene, the method calculates the water pump speed regulation amount and the start-stop priority sequence, and optimizes the priority sequence combined with the historical operation data to generate reasonable number control instructions. This number control method breaks through the limitations of traditional experience setting, fully utilizes the optimal operation law contained in the historical operation data, ensures that the pump group operation combination is always in the optimal state, reduces unnecessary start-stop operation and non-efficient pump group operation, and improves the overall operation efficiency of the pump group. At the same time, the coordinated action of the speed regulation amount and the number control instruction realizes the organic combination of single pump precise speed regulation and multi-pump intelligent combination, so that the entire water pump system can flexibly adjust the operation state according to the target water supply demand, while meeting the water demand, and minimizing energy consumption.

[0021] The control logic of the method has strong adaptability and robustness, and can cope with dynamic interference factors such as water load fluctuation and pipe resistance change in the pipe network system. By monitoring the dynamic changes of the signals in real time, the compensation coefficient, the working condition characteristic vector and the control instruction are adjusted in time to ensure that the system can still maintain efficient and stable operation in complex and variable operating environment, avoiding the control misalignment problem of traditional control methods under dynamic interference, and further improving the operation reliability and energy saving stability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 The working principle diagram of the water pump energy-saving optimization control method described in the application; Figure 2 The flowchart for calculating the flow-pressure propagation time compensation coefficient; Figure 3 The flowchart for generating a matching energy-saving parameter combination; Figure 4 The flowchart for generating a control deviation. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0024] Please refer to Figure 1 The application provides a water pump energy-saving optimization control method, which comprises: The water pump outlet pressure monitoring signal, the pipe network terminal flow monitoring signal and the water pump motor power monitoring signal are acquired; the flow-pressure propagation time compensation coefficient is calculated according to the propagation characteristic between the water pump outlet pressure monitoring signal and the pipe network terminal flow monitoring signal; the pressure fluctuation compensation value is generated based on the amplitude fluctuation correlation of the compensated pressure monitoring signal and the flow monitoring signal in the sliding time window; the operating condition characteristic vector is extracted according to the energy distribution characteristic of the water pump motor power monitoring signal in the frequency domain and the pressure fluctuation compensation value; the matching energy-saving parameter combination is generated by matching the operating condition characteristic vector with the preset energy-saving parameter mapping rule library; the water pump efficiency reference curve is corrected in real time according to the matching energy-saving parameter combination, and the efficiency attenuation factor is output; the control deviation is generated based on the efficiency attenuation factor and the target water supply demand; the water pump rotating speed adjustment amount and the water pump start-stop priority sequence are calculated according to the control deviation; the water pump start-stop priority sequence is optimized in combination with the historical operation data, and the number control instruction is generated; the water pump rotating speed adjustment amount and the number control instruction are combined into a control signal to drive the water pump actuator.

[0025] Embodiment 1: refer to Figure 2 In the implementation process, the water pump outlet pressure monitoring signal, the pipe network terminal flow monitoring signal and the water pump motor power monitoring signal are acquired. These signals are collected in real time by sensors installed on the pipeline and the water pump motor. The pressure sensor is usually located near the water pump outlet flange, the flow sensor is installed near the pipe network terminal user access point, and the power sensor is integrated in the motor control cabinet. The signals are transmitted to the control system in the form of 4-20mA analog quantity or Modbus digital signal. The sampling frequency is set according to the pipe network scale and the water pump response speed, and is usually not less than 10Hz to ensure signal integrity.

[0026] When calculating the flow-pressure propagation time compensation coefficient, the initial iteration value and iteration step of the preset propagation time compensation coefficient are set. The initial iteration value is estimated based on the physical length of the pipe network and the propagation speed of sound waves in the fluid, for example, for a pipe network with a length of about 1000 meters, the initial value can be set to 2 seconds. The iteration step is set according to the system accuracy requirement, usually 0.1 seconds. The cross-covariance function is constructed using the water pump outlet pressure monitoring signal and the pipe network end flow monitoring signal at different sampling times. In specific operation, the pressure signal sequence and the flow signal sequence in a continuous time window are selected, and the time window length should be more than several times the estimated propagation time, for example, a 30-second data segment. By moving the time offset, the covariance values of the two sequences at different time delays are calculated to form the cross-covariance function curve. The function value of the cross-covariance function in the iteration process is calculated, and by traversing the preset time delay range, the point at which the cross-covariance function reaches the maximum value is found. The time delay corresponding to this point is the flow-pressure propagation time compensation coefficient. For example, when the time delay is 3.5 seconds, the cross-covariance value is maximum, and the propagation time compensation coefficient is determined to be 3.5 seconds. This coefficient is used to align the time reference of the pressure signal and the flow signal, and to eliminate the error caused by signal transmission delay.

[0027] Based on the correlation between the amplitude fluctuations of the compensated pressure monitoring signal and the flow monitoring signal in the sliding time window, the pressure fluctuation compensation value is generated. After time offsetting the pressure signal by the propagation time compensation coefficient, the correlation analysis is performed with the flow signal in the same sliding time window. The sliding time window length is usually set to several times the propagation time, for example, a 10-second window. In each window, the correlation coefficient of the pressure signal amplitude and the flow signal amplitude is calculated, and the ratio of the fluctuation amplitudes of the two is also calculated. The pressure fluctuation compensation value is determined by the correlation coefficient and the amplitude ratio, and is calculated by weighted average method. For example, when the correlation coefficient is high and the amplitude ratio is small, it indicates that the pressure fluctuation is mainly affected by the flow change, and the pressure fluctuation compensation value is small; when the correlation coefficient is low and the amplitude ratio is large, it indicates that there are other interference factors, and a larger pressure fluctuation compensation value is needed.

[0028] When extracting the operating condition feature vector, the compensated pressure monitoring signal is decomposed by wavelet packet. The db4 wavelet basis function is used for 6-layer decomposition, and the pressure signal frequency band is divided into multiple sub-bands. Focus on the 0-10Hz, 10-25Hz, 25-50Hz three preset frequency bands, which correspond to the low frequency fluctuation, medium frequency disturbance and high frequency noise of the pipe network system respectively. Calculate the energy proportion of each sub-band, that is, the ratio of the energy of each sub-band signal to the total energy. These energy proportions reflect the distribution characteristics of the pressure signal in different frequency bands, which can represent the operating state of the pipe network. The energy proportions and the pressure fluctuation compensation values are weighted and fused to generate the frequency domain operating condition index. The weighting coefficients are adjusted according to the actual pipe network characteristics, usually the low frequency band weight is higher, and the high frequency band weight is lower. The pressure fluctuation compensation value is used as a correction factor to participate in the calculation, which is used to compensate the energy attenuation in the signal propagation process.

[0029] The frequency domain operating condition index and the harmonic distortion rate of the pump motor power monitoring signal are superimposed to form the operating condition feature vector. The frequency spectrum of the power signal is obtained by fast Fourier transform through frequency spectrum analysis of the pump motor power monitoring signal. The harmonic distortion rate is calculated, that is, the ratio of the effective value of each harmonic to the effective value of the fundamental wave. The harmonic distortion rate reflects the load characteristics and power quality of the motor. The frequency domain operating condition index and the harmonic distortion rate are combined according to the predetermined weight to form a multi-dimensional operating condition feature vector. The feature vector comprehensively reflects the operating state of the pump unit and the pipe network, including the pressure fluctuation characteristics, flow response characteristics and motor operating characteristics, etc. multiple dimensions of information.

[0030] The whole implementation process is realized by signal processing algorithm, using digital filter for signal preprocessing, using moving window technology for real-time calculation, and using optimization algorithm to find the best time delay. The calculation results are used for subsequent energy-saving parameter matching and control decision, providing data support for the optimized operation of the pump system. In the implementation, attention should be paid to the accuracy and synchronization of the sensor signal, and the sensor should be calibrated regularly to ensure the reliability of the data. At the same time, according to the actual pipe network characteristics, adjust the algorithm parameters, so that the calculation results are more consistent with the actual operating conditions.

[0031] Example 2: see Figure 3In the implementation process, the construction of the energy-saving parameter mapping rule library is the preparatory work. The rule library is established through historical operation data analysis, covering typical working conditions under different seasons and different load conditions. Each record contains an operating condition feature vector and its corresponding optimal operating parameter combination. The feature vector dimension is consistent with the operating condition feature vector output in the foregoing embodiment, including frequency domain working condition indicators and harmonic distortion rate elements. The parameter combination includes head-flow benchmark parameters, efficiency threshold parameters, and speed constraint parameters. The rule library uses a database table structure for storage, with the feature vector field being a floating-point array and the parameter combination field being structured data. The rule library capacity is set according to the system complexity, usually not less than 200 data records, covering common operating scenarios.

[0032] When the system obtains the real-time operating condition feature vector, it starts to match the energy-saving parameter combination. First, the real-time feature vector is standardized to eliminate dimensional differences. The Z-score standardization method is used to normalize the mean and standard deviation of all reference feature vectors in the rule library. Then, the Euclidean distance between the real-time feature vector and each reference feature vector in the rule library is calculated. The distance calculation traverses the entire rule library, using the square sum of vector component differences and the square root formula. The system records the minimum distance value and its corresponding reference feature vector index. When the minimum distance exceeds the preset threshold, the rule library update mechanism is started, and the current working condition is added to the library as a new sample.

[0033] The head-flow benchmark parameter associated with the matched reference feature vector is extracted. This parameter is stored in the form of a two-dimensional data table, containing multiple sets of flow-head corresponding points, defining the high-efficiency working area of the water pump under the current working condition. For example, in a certain matching parameter, the flow is in the 50-80 m³ / h interval, and the corresponding head range is 40-45 meters. The efficiency threshold parameter is a percentage value, representing the minimum allowable operating efficiency under the current working condition, such as 82%. The speed constraint parameter includes minimum and maximum speed limits, such as 800 rpm to 1500 rpm. These parameter combinations constitute the matching energy-saving parameter combination, which is transmitted to the efficiency benchmark curve correction module.

[0034] When the efficiency reference curve of the water pump is corrected in real time, firstly, the current flow monitoring signal of the pipe network terminal is obtained. The flow value is taken as the abscissa to query the corresponding theoretical efficiency value of the water pump efficiency reference curve. The efficiency reference curve is a standard performance curve provided by the manufacturer, which is usually expressed in the form of a quadratic function. For example, when the flow is 60 m³ / h, the theoretical efficiency value is 85%. At the same time, the current actual efficiency value is calculated according to the real-time water pump motor power monitoring signal and the actual output lift. The lift is obtained by converting the water pump outlet pressure, and the actual efficiency calculation formula is: (flow x lift x fluid density x gravitational acceleration) / (motor power x transmission efficiency). Assuming that the calculated actual efficiency is 83.5%, the relative deviation is (83.5%-85%) / 85%=-1.76%.

[0035] The efficiency attenuation factor is used as a correction coefficient to participate in the curve adjustment. The initial value of the factor is 1.0, and it is dynamically updated according to the system running state. The relative deviation is multiplied by the efficiency attenuation factor to obtain the correction amount, such as -1.76% x 0.95=-1.672%. The curvature coefficient adjustment of the efficiency reference curve adopts a segmented correction strategy. In the vicinity of the flow monitoring point, the slope of the curve is locally adjusted. For example, the slope of the original efficiency curve at 60 m³ / h is 0.15% / m³, and after correction, it is adjusted to 0.152% / m³. The correction range covers the working interval of 20% before and after the current flow point, and cubic spline interpolation is used to ensure smooth transition of the curve. The updated efficiency reference curve is stored in the system memory for subsequent control calculation.

[0036] The entire implementation process needs to process multiple data interfaces. The real-time data acquisition module updates the monitoring signal at a frequency of 1 Hz, the feature vector matching calculation period is 5 seconds, and the efficiency curve correction period is 30 seconds. The system maintains two databases: a real-time running database stores the latest 300 running records, and a historical database stores long-term running data for rule base optimization. When the actual efficiency is continuously lower than the theoretical efficiency, the efficiency attenuation factor is gradually reduced to amplify the correction amplitude; when the actual efficiency rebounds, the factor slowly recovers. The curve correction amount is protected by upper and lower limits, and the single correction does not exceed ±3%, and the cumulative correction does not exceed ±15%, to prevent abnormal working conditions from causing curve distortion.

[0037] For multiple parallel water pumps, their respective efficiency reference curves need to be established. When the water pump is overhauled or replaced with parts, the efficiency reference curve needs to be reset to the initial state. The system sets a manual correction mode to allow engineers to directly adjust the curve parameters according to the performance test results. All correction operations are recorded in the audit log, including time stamp, operator, correction parameter, etc. fields, to meet the operation management requirements.

[0038] Example 3: see Figure 4The operation of generating control deviation is based on the comparison and analysis of real-time monitoring data and target setting values. The target water supply demand is set by the upper monitoring system or the operator, usually including two core parameters of target head and target flow. The target head represents the pressure level that needs to be maintained at the end of the pipe network, and the target flow represents the water supply rate required by the user side. These target values are transmitted to the control system through the communication interface and stored in the dedicated register. The system reads the target value every 1 second and compares it with the real-time monitoring data.

[0039] When obtaining the actual operating parameters, the system reads the water pump outlet pressure monitoring signal and converts it to head value through unit conversion. The pressure signal unit is kilopascal, and the head conversion uses the industry standard formula: head value equals pressure value divided by the product of fluid density and gravitational acceleration. The fluid density takes the standard water density value, and the gravitational acceleration takes the local measured value. The actual flow value comes directly from the pipe network end flow monitoring signal, which has been temperature compensated and noise filtered. The efficiency attenuation factor is provided by the previous module calculation, which reflects the deviation of the current system operating efficiency from the ideal state, usually ranging from 0.85 to 1.15.

[0040] When calculating the head control deviation component, the difference between the actual head and the target head is multiplied by the efficiency attenuation factor. The actual head is calculated in real time from the pressure monitoring signal, and the target head is read from the set register. The difference between the two is a signed value, with a positive value indicating that the actual head is higher than the target, and a negative value indicating that it is lower than the target. The efficiency attenuation factor acts as an adjustment coefficient, amplifying the deviation when the system efficiency decreases and improving the control sensitivity. For example, when the actual head is 45 meters, the target head is 43 meters, and the efficiency attenuation factor is 0.95, the head control deviation component is (45-43) x 0.95 = 1.9 meters.

[0041] The calculation of the flow control deviation component uses a similar method. The actual flow comes from the pipe network end flow sensor, and the target flow is provided by the demand setting value. The difference between the two is multiplied by the same efficiency attenuation factor to obtain the flow control deviation component. This component represents the deviation in flow, also a signed value. For example, when the actual flow is 68 m³ / h, the target flow is 70 m³ / h, and the efficiency attenuation factor is 0.95, the flow control deviation component is (68-70) x 0.95 = -1.9 m³ / h.

[0042] When the two deviation components are normalized and weighted, the physical quantities with different dimensions need to be converted into dimensionless relative values. The head control deviation component is divided by its range, and the flow control deviation component is divided by its range to obtain standardized values. The range is determined according to the system design parameters, and the head range is usually the maximum working head, and the flow range is the maximum design flow. The weighting coefficient is configured according to the characteristics of the pipe network, and the head weight is usually higher than the flow weight because pressure stability is more critical to the water supply system. The weighted calculation formula is: Wherein: represents the final control deviation, which is a dimensionless value; represents the head control deviation component, with units of meters; represents the head range, with units of meters; represents the flow control deviation component, with units of cubic meters per hour; represents the flow range, with units of cubic meters per hour; represents the head weight coefficient; represents the flow weight coefficient. The weight coefficient satisfies the relationship , and is usually taken as .

[0043] During the calculation process, there are protection limits, and when any monitoring signal exceeds the reasonable range, the deviation calculation is suspended and an alarm is issued. The deviation component has upper and lower limits to prevent abnormal data from causing calculation overflow. The weighted control deviation also has an output limit, which is usually limited to -1.0 to +1.0. The system records the intermediate results and the final deviation of each calculation for operation state analysis and fault diagnosis.

[0044] The update period of the control deviation is synchronized with the sampling period of the monitoring signal, which is usually 1 second. During the calculation gap, the last valid output value is maintained. When the target value is lost due to communication interruption, the last valid target value is used for calculation. All calculation parameters and coefficients can be adjusted through the human-machine interface, and the adjustment records are stored in the system log. For multi-pump parallel system, the control deviation calculation is based on the total pipe network parameters, and the single pump operating state is not considered.

[0045] During implementation, attention should be paid to signal synchronization. The pressure signal and the flow signal use a timestamp alignment mechanism to ensure that the same sampling value is used at the same time. The efficiency decay factor has a longer update period, and the latest available value is used when calculating the deviation component. The system provides a manual mode that allows operators to directly input the control deviation, bypassing the automatic calculation process. All intermediate variables during the calculation process are displayed in real time, making it easy for operators to monitor the system status.

[0046] Example 4: The operation of calculating the water pump speed adjustment amount is based on the comprehensive analysis of the input of control deviation and the system operating state. The control deviation is provided by the previous module, which is a dimensionless standardized value ranging from negative one to positive one, indicating the deviation of the current operating state of the system from the target state. The value is updated once per second and transmitted to the speed adjustment calculation module through the data bus. The proportional-integral-derivative controller receives the control deviation as input. The controller parameters are set according to the characteristics of the water pump unit: the proportional coefficient determines the response strength to the current deviation, the integral time constant affects the elimination speed of the historical deviation accumulation, and the derivative time constant adjusts the predictive compensation of the change trend. The controller output is the initial speed adjustment amount, which is the change amount relative to the current speed, with units of revolutions per minute. For example, when the control deviation is positive zero point three, the controller may output an adjustment amount of fifty revolutions per minute.

[0047] The sensitivity of the initial speed adjustment amount is corrected according to the current slope of the water pump efficiency reference curve. The system queries the efficiency curve slope value corresponding to the current operating point in real time, which represents the influence of speed change on efficiency. When the slope is large, the efficiency is sensitive to speed change, and the adjustment amplitude needs to be reduced; when the slope is small, the adjustment amount can be appropriately increased. The correction method uses linear proportional adjustment, multiplying the initial speed adjustment amount by the slope compensation coefficient. The coefficient is dynamically calculated according to the size and direction of the slope value, maintaining the efficiency optimization characteristics of speed adjustment.

[0048] Limiting the change rate of the corrected speed adjustment amount is an important step. The system sets the maximum allowed speed change rate parameter, which is determined according to the mechanical characteristics of the water pump and the performance of the motor. The rate limiting algorithm is used to ensure that the speed adjustment amount does not exceed the acceleration limit allowed by the device. The algorithm detects the speed change amount in the adjacent calculation period, and automatically smooths the output value when the change amount exceeds the threshold. The final water pump speed adjustment amount is a signed integer value, indicating the speed value that needs to be increased or decreased, which is transmitted to the frequency control device through the analog output module.

[0049] The entire calculation process is executed in a loop, completing a complete calculation process once every second. The system maintains a speed adjustment amount output buffer that stores the calculation results of the last ten periods for trend analysis and fault diagnosis. When abnormal operating conditions are detected, it automatically switches to a safe mode using conservative adjustment parameters. All calculation parameters and intermediate variables are displayed in real time on the human-machine interface for operators to monitor and adjust.

[0050] The equipment protection should be paid attention to during implementation. The rotational speed adjustment amount is provided with hard upper and lower limits to ensure that it will not exceed the safe working range of the water pump. The change rate limit parameter is set according to the mechanical characteristic curve provided by the equipment manufacturer, and different parameter values are used for different types of water pumps. The system provides a manual adjustment mode to allow the operator to directly set the rotational speed change amount and the change rate. The main parameters involved in the calculation process and the corresponding relationship are shown in Table 1.

[0051] Table 1: Correspondence table of rotational speed adjustment amount calculation parameters.

[0052] The calculation process adopts a multi-level verification mechanism. Each calculation result is checked for reasonableness, and a re-calculation program is started when abnormal data is found. The system records the timestamp, input value, output value and intermediate parameters of each calculation, which are used for performance analysis and optimization adjustment. For the water pump group running in parallel, each water pump independently calculates the rotational speed adjustment amount, but accepts unified coordinated control. The signal transmission delay problem needs to be considered during implementation. The rotational speed adjustment amount output uses a timestamp synchronization mechanism to ensure that the system state is consistent when the frequency converter receives the instruction. When the communication delay exceeds the threshold, the adjustment amount output is automatically suspended, and the signal synchronization is waited for before continuing execution. The system also has an emergency stop protection, which immediately stops the rotational speed adjustment and triggers the alarm program when a serious abnormality is detected.

[0053] All calculation parameters can be modified online through the human-machine interface, and the modification records are stored in the system log. The running personnel can adjust the controller parameters and limit values according to the actual running situation to keep the system in the best adjustment performance. The system provides a parameter self-adaptive learning function, which can automatically optimize the calculation parameters according to the historical running data to improve the control precision and response speed.

[0054] Example 5: Operation of optimizing the priority sequence of water pump start-stop is based on the running history record of the equipment and the system state parameters. The system maintains a water pump running database to record the cumulative running time and start-stop frequency data of each water pump. The cumulative running time is accurate to two decimal places in hours, and it is continuously accumulated from the start of the water pump operation. The start-stop frequency records the number of operations of starting and stopping the water pump, which is archived and stored in a daily cycle. These data are marked with timestamps through the real-time clock to ensure the accuracy of the time sequence of the records. When calculating the cumulative running time and start-stop frequency of each water pump, the system scans the running state every minute. For the running water pump, the cumulative time is increased by six hundredths of an hour; for the start-stop operation, the corresponding counter is increased by one. The database uses a ring buffer structure to retain detailed running records for the last thirty days, and long-term data are stored in monthly summaries. When the water pump is in a maintenance state, the relevant statistics are temporarily suspended and a maintenance identifier is marked.

[0055] The weight coefficient of the cumulative running time is adjusted according to the efficiency attenuation factor. The efficiency attenuation factor is provided by the previous module and reflects the overall efficiency level of the system. The weight coefficient calculation uses a piecewise function: when the efficiency attenuation factor is greater than or equal to one, the weight coefficient remains the baseline value; when the factor is less than one, the weight coefficient increases linearly with the factor value. For example, when the efficiency attenuation factor is 0.9, the weight coefficient may be adjusted to 2 times the baseline value. This design ensures that when the system efficiency decreases, the water pump with shorter running time is given priority.

[0056] The sequence is generated by arranging the cumulative running time in descending order after the weight coefficient correction. First, multiply the original cumulative running time of each water pump by the corresponding weight coefficient to get the corrected running time. Then compare and sort all the corrected running times of the water pumps, with the largest value having the lowest priority and the smallest value having the highest priority. The sequence is stored in the memory as an array, with elements being the water pump number and its priority sequence number. The sequence is updated every five minutes, and the output remains stable during this period.

[0057] When generating the number of water pumps control instruction, the system calculates the number of water pumps needed to run according to the target water supply demand, and combines the priority sequence with the number of requirements: starting from the water pump with the highest priority (the smallest corrected running time), activate the specified number of water pumps in turn; the unselected water pumps remain in the stop state. The instruction code uses bitmap format, with each bit corresponding to the running state of a water pump, one indicating start, and zero indicating stop. The instruction is transmitted to the field control cabinet through the digital output module.

[0058] In the synthesis control signal stage, the speed regulation amount of the water pump is converted into the pulse width modulation duty cycle instruction. The speed regulation amount is a relative value output by the previous module, indicating the change amount relative to the current speed. The conversion process uses a linear mapping algorithm: read the current speed reference value, add the regulation amount to get the target speed; according to the frequency converter characteristic curve, convert the target speed into the corresponding duty cycle value. The duty cycle instruction is represented in the form of integer percentage from zero to one hundred, and is transmitted through the analog output channel.

[0059] The pulse width modulation duty cycle instruction and the relay group control code are output to the actuator simultaneously. The system uses a time synchronization mechanism to ensure that the speed control signal and the start-stop control signal take effect at the same time. The output port is configured with double-buffered registers: one set of registers stores the instructions ready to be sent, and the other set of registers maintains the current output; at the preset synchronization time, switch the register state. After receiving the signal, the frequency converter adjusts the output voltage frequency according to the duty cycle, and the relay group turns on and off the corresponding water pump circuit according to the control code.

[0060] The implementation process is provided with multiple protection mechanisms. When the water pump fault signal is detected, it is automatically removed from the priority sequence and an alarm is given. The start-stop operation is provided with minimum interval time protection to prevent frequent switching from damaging the equipment. Logical verification is performed before the control signal is output to ensure command consistency. The system retains a manual mode, allowing the operator to directly specify the water pump operating state and speed value. All control operations are recorded in detail, including timestamp, instruction content, execution result, etc.

[0061] The operation data maintenance adopts a redundant storage strategy. In addition to the real-time database in memory, key parameters are written to non-volatile memory every ten minutes. Historical data is exported to external storage devices every month for long-term trend analysis. The system provides a report generation function, which can customize the water pump operation statistics and energy consumption analysis within a certain period of time. Attention should be paid to the differences in device characteristics during implementation. For different types of water pumps, set up independent operating parameter groups. Newly commissioned water pumps have special handling during the running-in period, with appropriately increased initial weight coefficients. The system automatically calibrates the running timing accuracy regularly and synchronizes with the central clock server. Maintenance personnel can view and adjust the priority calculation parameters through a dedicated interface. All modification operations need to be verified by permission and record the operator information.

[0062] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0063] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A water pump energy saving optimization control method, characterized by, The method comprises the following steps: acquiring a water pump outlet pressure monitoring signal, a pipe network tail end flow monitoring signal and a water pump motor power monitoring signal; calculating a flow-pressure propagation time compensation coefficient according to the propagation characteristic between the water pump outlet pressure monitoring signal and the pipe network tail end flow monitoring signal; generating a pressure fluctuation compensation value based on the amplitude fluctuation correlation of the compensated pressure monitoring signal and the flow monitoring signal within a sliding time window; extracting an operating condition characteristic vector according to the energy distribution characteristic of the water pump motor power monitoring signal in the frequency domain and the pressure fluctuation compensation value; matching the operating condition characteristic vector with a preset energy-saving parameter mapping rule library to generate a matched energy-saving parameter combination; real-time correcting a water pump efficiency reference curve according to the matched energy-saving parameter combination, and outputting an efficiency attenuation factor; generating a control deviation amount based on the efficiency attenuation factor and a target water supply demand; calculating a water pump rotating speed adjustment amount and a water pump start-stop priority sequence according to the control deviation amount; optimizing the water pump start-stop priority sequence in combination with historical operation data to generate a number control instruction; synthesizing the water pump rotating speed adjustment amount and the number control instruction into a control signal to drive a water pump actuator.

2. The water pump energy-saving optimization control method according to claim 1, wherein The calculation of the flow-pressure propagation time compensation coefficient comprises: presetting an initial iteration value and an iteration step length of the propagation time compensation coefficient; constructing a cross-covariance function using the water pump outlet pressure monitoring signal and the pipe network tail end flow monitoring signal at different sampling times; calculating the function value of the cross-covariance function in the iteration process, and taking the propagation time corresponding to the maximum function value as the flow-pressure propagation time compensation coefficient.

3. The water pump energy-saving optimization control method according to claim 1, wherein The extraction of the operating condition characteristic vector comprises: performing wavelet packet decomposition on the compensated pressure monitoring signal to extract the energy proportion of a preset frequency band; weighting and fusing the energy proportion and the pressure fluctuation compensation value to generate a frequency domain operating condition index; superimposing the frequency domain operating condition index and the harmonic distortion rate of the water pump motor power monitoring signal to constitute the operating condition characteristic vector.

4. The water pump energy-saving optimization control method according to claim 1, characterized in that, The generation of the matched energy-saving parameter combination comprises: indexing a reference characteristic vector with the minimum Euclidean distance from the operating condition characteristic vector from the energy-saving parameter mapping rule library; extracting a head-flow reference parameter, an efficiency threshold parameter and a rotating speed constraint parameter associated with the reference characteristic vector; combining the head-flow reference parameter, the efficiency threshold parameter and the rotating speed constraint parameter into the matched energy-saving parameter combination.

5. The water pump energy saving optimization control method according to claim 1, wherein, The real-time correction of the water pump efficiency reference curve comprises: querying a theoretical efficiency value of the water pump efficiency reference curve according to the pipe network tail end flow monitoring signal at the current time; calculating the relative deviation between the theoretical efficiency value and an actual efficiency value derived from the real-time water pump motor power monitoring signal; multiplying the relative deviation and the efficiency attenuation factor to update the curvature coefficient of the water pump efficiency reference curve.

6. The water pump energy saving optimization control method according to claim 1, wherein, The generation of the control deviation amount comprises: analyzing a target head and a target flow according to the target water supply demand; multiplying the difference between the actual head and the target head by the efficiency attenuation factor to generate a head control deviation component; multiplying the difference between the actual flow and the target flow by the efficiency attenuation factor to generate a flow control deviation component; The head control deviation component and the flow control deviation component are normalized and weighted to generate a control deviation component.

7. The water pump energy saving optimization control method according to claim 1, wherein The calculation of the water pump rotating speed adjustment amount comprises: The control deviation component is input into a proportional-integral-derivative controller to output an initial rotating speed adjustment amount; The initial rotating speed adjustment amount is sensitivity corrected according to a current slope of the water pump efficiency reference curve; The change rate threshold of the corrected rotating speed adjustment amount is limited to generate a final water pump rotating speed adjustment amount.

8. The water pump energy saving optimization control method of claim 1, wherein, The optimization of the water pump start-stop priority sequence comprises: Cumulative running time and start-stop frequency of each water pump are counted; The weight coefficient of the cumulative running time is adjusted according to an efficiency attenuation factor; The cumulative running time corrected by the weight coefficient is arranged in descending order to generate a water pump start-stop priority sequence.

9. The water pump energy saving optimization control method according to claim 1, characterized by, The synthesized control signal comprises: The water pump rotating speed adjustment amount is converted into a pulse width modulation duty cycle instruction; The number control instruction is encoded into a relay group control code; The pulse width modulation duty cycle instruction and the relay group control code are synchronously output to a water pump actuator.

10. A water pump energy saving optimization control system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to realize the steps of the water pump energy-saving optimization control method according to any one of claims 1-9.

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