Water supply energy recovery method and system of hydropower station unit

Abstract

The invention discloses a water supply energy recovery method and system for a hydropower station unit, and relates to the technical field of water conservancy, and the method comprises the steps: collecting pipeline branch data of the hydropower station unit, and carrying out the operation state analysis of the collected pipeline branch data; and controlling the on-off state of a generator outlet of the hydropower station unit according to an analysis result, and controlling the rectification process according to water pumping data of a water pump in the hydropower station unit to complete intelligent monitoring of water supply energy recovery. The hydropower station unit water supply energy recovery process is accurately monitored and analyzed, the defects that in a traditional system, data collection is not comprehensive, state analysis is lagged, and control is not accurate are overcome, the response speed and the control precision of the system are improved, the energy recovery efficiency is effectively improved, and the energy utilization rate is increased. The whole system can operate more stably and efficiently, the optimal operation state of the hydropower station under different working conditions is ensured, and therefore the water resource utilization efficiency and economic benefits are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy, and in particular to a method and system for recovering water supply energy of a hydropower station unit. Background Art

[0002] As society pays more and more attention to renewable energy, hydropower stations, as an important source of green energy, have become the key to efficient utilization and energy recovery. However, traditional hydropower station energy recovery systems have problems such as incomplete data collection, insufficient operation status analysis and inaccurate rectification control. These problems not only reduce the overall efficiency of the hydropower station, but also affect the effective use of energy.

[0003] In the prior art, hydropower stations often face defects such as insufficient pipeline branch data collection, delayed operation status analysis and inaccurate rectification process control during the energy recovery process. These problems lead to low energy recovery efficiency and difficulty in achieving optimal resource utilization. To this end, the present invention proposes a water supply energy recovery method based on intelligent monitoring and big data analysis. Through detailed data collection, real-time status analysis, precise control and intelligent monitoring, the shortcomings of the prior art are overcome, thereby significantly improving the energy recovery efficiency of the hydropower station. Summary of the invention

[0004] In view of the problems existing in the existing water supply energy recovery method and system of the hydropower station units, the present invention is proposed.

[0005] Therefore, the present invention aims to solve the problems of incomplete data collection, delayed state analysis and inaccurate control in the prior art by adopting intelligent monitoring and diagnosis technology, big data analysis method and multi-level control logic technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a water supply energy recovery method for a hydropower station unit, which includes collecting pipeline branch data of the hydropower station unit, and performing operation status analysis on the collected pipeline branch data;

[0008] According to the analysis results, the generator outlet switch state of the hydropower station unit is controlled, the generator is started, the generated electric energy is rectified, and the rectification process is controlled according to the water pumping data of the hydropower station unit;

[0009] Intelligent monitoring and diagnosis technology is used to monitor the control process, and big data analysis methods are used to establish a benchmark model for system operation to complete intelligent monitoring of water supply energy recovery.

[0010] As a preferred solution of the water supply energy recovery method of the hydropower station unit of the present invention, wherein: the collection of pipeline branch data of the hydropower station unit includes using a flow sensor and a pressure sensor to collect flow data and pressure data of the pipeline branch, and the collected data is transmitted to the central processor through high-frequency wireless for analysis. The specific formula of the transmission is:

[0011]

[0012] Among them, Q i，j (t) represents the cumulative flow of pipeline branches i and j at time t, represents the instantaneous flow rate q of pipeline branches i and j from time 0 to time t i，j The integral of (τ).

[0013] As a preferred solution of the water supply energy recovery method of the hydropower station unit of the present invention, wherein: the operation status analysis includes filtering the flow data and pressure data of the pipeline branch, using a high-dimensional multivariate linear regression model to analyze the operation status of the pipeline branch, calculating the cumulative flow and obtaining a state analysis model, and applying a machine learning algorithm to predict the state analysis model;

[0014] The specific calculation formula of the state analysis model is:

[0015] Q threshold =q i，j (τ)β+∈

[0016] Among them, Q threshold Vector data representing the state analysis model within the cumulative flow, q i，j (τ) represents the instantaneous flow rate, β represents the regression coefficient vector, and ∈ represents the error term vector;

[0017] The prediction calculation formula of the state analysis model is:

[0018]

[0019] Among them, P threshold vector data representing the predicted state analysis model, represents the predicted state at time t+1, σ represents the activation function, and W hh Represents the weight matrix from hidden state to hidden state, W hx represents the weight matrix input to the hidden state, b h Bias vector representing the hidden state.

[0020] As a preferred solution of the water supply energy recovery method of the hydropower station unit of the present invention, wherein: the controlling the generator outlet switch state of the hydropower station unit includes judging whether the generator outlet switch needs to be started or closed according to the operation state analysis result;

[0021] If Q i，j (t)≥Q threshold or P i，j (t)≥P threshold When , turn on the generator switch;

[0022] If Q i，j (t) threshold or P i，j (t) <P threshold When the generator switch is turned off, P i，j (t) represents the pressure of the generator;

[0023] When the generator switch is turned off, the multi-level control logic technology is used to adjust the pressure of the generator. The specific calculation formula of the adjustment is:

[0024]

[0025] Among them, P gen (t) represents the power generated by the generator at time t, ρ represents the density of the fluid, A represents the blade area of ​​the impeller, v represents the flow rate of the fluid, C p represents the efficiency of the generator in extracting energy from the water flow, P i，j (t) represents the pressure of the generator.

[0026] As a preferred solution of the water supply energy recovery method of the hydropower station unit of the present invention, wherein: the rectification of the generated electric energy includes converting the alternating current generated by the generator through a rectifier, and the specific calculation formula of the conversion is:

[0027]

[0028] Among them, V dc Represents the DC voltage obtained after rectification, V ac represents AC voltage, ω represents angular frequency, and t represents time;

[0029] The inverter is used to convert the rectified DC voltage into AC power for the second time. The specific calculation formula for the second conversion is:

[0030]

[0031] Among them, V ac Represents the AC voltage obtained after inversion, V dc ​represents the DC voltage obtained after rectification, ω represents the angular frequency, t represents the time, and sin(ωt) represents the sine function.

[0032] As a preferred solution of the water supply energy recovery method of the hydropower station unit of the present invention, wherein: the control of the rectification process includes using the extraction of the pumping flow rate of the water pump at time t and the pressure of the water pump at time t to adjust the rectified DC voltage, and using the control algorithm to process the input variables, the input variables include the real-time flow rate Q of the water pump pump (t), real-time pressure of the water pump P pump (t) and the rectified DC voltage, the processing of the input variables includes using the control algorithm to infer the set obtained after the processing and define the control rules. The specific calculation formula of the control algorithm is:

[0033] output = fuzzy_logic(Q pump (t), P pump (t), V dc (t))

[0034] Among them, output represents the algorithm output, fuzzy_logic represents the algorithm logic, Q pump (t) represents the real-time flow rate of the water pump at time t, P pump (t) represents the real-time pressure of the water pump at time t, V dc (t) represents the DC voltage at time t after rectification;

[0035] At time t, the real-time flow rate of the water pump is Q pump (t)≥ set threshold Q set When , the rectifier output is increased;

[0036] At time t, the real-time pressure of the water pump is P pump (t)≥ set threshold P set When , the rectifier output is increased twice;

[0037] At time t, the real-time flow rate of the water pump is Q pump (t)<Set threshold Q set The real-time pressure P of the water pump at time t pump (t)<Set threshold value P set At this time, big data analysis methods are used to establish a benchmark model for system operation and monitor the operating status of each hydropower station unit.

[0038] As a preferred solution of the water supply energy recovery method of the hydropower station unit of the present invention, the specific calculation formula for establishing the benchmark model of system operation is:

[0039]

[0040] Among them, X baseline represents the benchmark value of the benchmark model, N represents the number of hydropower station units, x i represents the unit point of the i-th sample;

[0041] If |x real -x expected |>X baseline When the error range of the operating state is exceeded, an alarm is triggered and water supply energy recovery is completed;

[0042] If |x real -x expected |≤X baseline , it does not exceed the error range of the operating status. At this time, the hydropower station units operate normally.

[0043] In the second aspect, an embodiment of the present invention provides a water supply energy recovery system for a hydropower station unit, comprising: an analysis module, which collects pipeline branch data of the hydropower station unit and performs operating status analysis on the collected pipeline branch data; a control module, which controls the generator outlet switch state of the hydropower station unit according to the analysis result, starts the generator, rectifies the generated electric energy, and controls the rectification process according to the water pumping data of the water pump in the hydropower station unit; a recovery module, which uses intelligent monitoring and diagnosis technology to monitor the control process, uses big data analysis methods to establish a benchmark model of system operation, and completes intelligent monitoring of water supply energy recovery.

[0044] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the water supply energy recovery method of the hydropower station unit is implemented.

[0045] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the water supply energy recovery method of the above-mentioned hydropower station unit is implemented.

[0046] The beneficial effects of the present invention are as follows: the present invention realizes accurate monitoring and analysis of the water supply energy recovery process of the hydropower station units by adopting intelligent monitoring and diagnosis technology and big data analysis methods, overcomes the shortcomings of incomplete data collection, delayed state analysis and inaccurate control in traditional systems, and improves the response speed and control accuracy of the system through the application of multi-level control logic technology, effectively improving the energy recovery efficiency. In addition, a benchmark model for system operation is established, so that the overall system can operate more stably and efficiently, ensuring the optimal operating state of the hydropower station under different working conditions, thereby significantly improving the water resource utilization efficiency and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0048] Figure 1 A flow chart of a water supply energy recovery method and system for a hydropower station unit provided in one embodiment of the present invention.

[0049] Figure 2 A water supply energy recovery method for a hydropower station unit and a schematic diagram of the recovery method of the system are provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0051] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0052] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0053] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0054] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0055] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0056] Example 1

[0057] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a water supply energy recovery method for a hydropower station unit, comprising:

[0058] S1: Collect pipeline branch data of hydropower station units and perform operation status analysis on the collected pipeline branch data.

[0059] Among them, collecting pipeline branch data of hydropower station units includes using flow sensors and pressure sensors to collect flow data and pressure data of pipeline branches, and transmitting the collected data to the central processor through high-frequency wireless for analysis. The specific formula for transmission is:

[0060]

[0061] Among them, Q i，j (t) represents the cumulative flow of pipeline branches i and j at time t, represents the instantaneous flow rate q of pipeline branches i and j from time 0 to time t i，j The integral of (τ).

[0062] S1.1: Operation status analysis includes filtering the flow data and pressure data of the pipeline branch, using a high-dimensional multivariate linear regression model to analyze the operation status of the pipeline branch, calculating the cumulative flow and obtaining the status analysis model, and applying a machine learning algorithm to predict the status analysis model;

[0063] The specific calculation formula of the state analysis model is:

[0064] Q threshold=q i，j (τ)β+∈

[0065] Among them, Q threshold Vector data representing the state analysis model within the cumulative flow, q i，j (τ) represents the instantaneous flow rate, β represents the regression coefficient vector, and ∈ represents the error term vector;

[0066] The prediction calculation formula of the state analysis model is:

[0067]

[0068] Among them, P threshold vector data representing the predicted state analysis model, represents the predicted state at time t+1, σ represents the activation function, and W hh Represents the weight matrix from hidden state to hidden state, W hx represents the weight matrix input to the hidden state, b h Bias vector representing the hidden state.

[0069] Furthermore, flow and pressure sensors are used to collect real-time data from the pipeline branches of the hydropower station units. For example, a flow rate of 2,000 liters and a pressure change of 0.1 MPa are recorded per second. Through high-frequency wireless transmission, these data are transmitted to the central processor in real time for analysis. After the data is filtered, a high-dimensional multivariate linear regression model is used for state analysis, and it is calculated that the daily cumulative flow rate can reach 100,000 cubic meters. The machine learning algorithm further predicts future states, such as the flow rate may increase to 3,000 liters / second and the pressure may drop to 0.4 MPa, thereby accurately monitoring and adjusting the system operation.

[0070] S2: Control the generator outlet switch state of the hydropower station unit according to the analysis results, start the generator, rectify the generated electric energy, and control the rectification process according to the water pumping data of the hydropower station unit.

[0071] Among them, controlling the generator outlet switch state of the hydropower station unit includes judging whether the generator outlet switch needs to be started or closed according to the operation state analysis result;

[0072] If Q i，j (t)≥Q threshold or P i，j (t)≥P threshold When , turn on the generator switch;

[0073] If Q i，j (t) threshold or P i，j (t) <P threshold When the generator switch is turned off, P i，j ​(t) represents the pressure of the generator;

[0074] When the generator switch is turned off, the multi-level control logic technology is used to adjust the generator pressure. The specific calculation formula for the adjustment is:

[0075]

[0076] Among them, P gen (t) represents the power generated by the generator at time t, ρ represents the density of the fluid, A represents the blade area of ​​the impeller, v represents the flow rate of the fluid, C p represents the efficiency of the generator in extracting energy from the water flow, P i，j (t) represents the pressure of the generator.

[0077] S2.1: Rectifying the generated electric energy includes converting the alternating current generated by the generator through a rectifier. The specific calculation formula for the conversion is:

[0078]

[0079] Among them, V dc Represents the DC voltage obtained after rectification, V ac represents AC voltage, ω represents angular frequency, and t represents time;

[0080] The inverter is used to convert the rectified DC voltage into AC power for the second time. The specific calculation formula for the second time conversion is:

[0081]

[0082] Among them, V ac Represents the AC voltage obtained after inversion, V dc represents the DC voltage obtained after rectification, ω represents the angular frequency, t represents the time, and sin(ωt) represents the sine function.

[0083] Furthermore, in the energy recovery system of the hydropower station unit of the present invention, the outlet switch state of the generator is controlled according to the results of the operation status analysis. For example, when the pressure of the generator exceeds 2.5 MPa or the flow exceeds 2500 liters / second, the system will start the generator switch and start generating electricity. When the pressure is lower than 1.5 MPa or the flow is lower than 1500 liters / second, the system will turn off the generator switch. When the generator is in the off state, the pressure of the generator is adjusted using multi-level control logic technology to ensure that the pressure is maintained between 1.5 and 2.5 MPa.

[0084] The generated electric energy is converted into direct current through a rectifier. For example, the AC voltage output by the generator may be 230 volts. After passing through the rectifier, it is converted into a DC voltage of 300 volts. Subsequently, the DC voltage is converted back into AC power using an inverter to meet different power demands. During this process, the inverted AC voltage is usually maintained at around 220 volts with a frequency of 50 Hz. Through a series of controls and conversions, the effective recovery and stable output of system energy are ensured.

[0085] S2.2: Controlling the rectification process includes using the pumping flow rate and pressure of the pump at time t to adjust the rectified DC voltage and using the control algorithm to process the input variables, including the real-time flow rate Q of the pump. pump (t), real-time pressure of the water pump P pump (t) and the rectified DC voltage, the input variable processing includes using the control algorithm to infer the set obtained after processing and define the control rules. The specific calculation formula of the control algorithm is:

[0086] output = fuzzy_logic(Q pump (t), P pump (t), V dc (t))

[0087] Among them, output represents the algorithm output, fuzzy_logic represents the algorithm logic, Q pump (t) represents the real-time flow rate of the water pump at time t, P pump (t) represents the real-time pressure of the water pump at time t, V dc (t) represents the DC voltage at time t after rectification;

[0088] At time t, the real-time flow rate of the water pump is Q pump (t)≥ set threshold Q set When , the rectifier output is increased;

[0089] At time t, the real-time pressure of the water pump is P pump (t)≥ set threshold P set When , the rectifier output is increased twice;

[0090] At time t, the real-time flow rate of the water pump is Q pump (t)<Set threshold Q set The real-time pressure P of the water pump at time t pump (t)<Set threshold value P set At this time, big data analysis methods are used to establish a benchmark model for system operation and monitor the operating status of each hydropower station unit.

[0091] Furthermore, in the rectification control process of the present invention, the system extracts the pumping flow and pressure data of the water pump at different time points. For example, at a certain time point, the pumping flow of the water pump is 2000 liters / second and the pressure is 0.8 MPa. The system uses these real-time data to adjust the rectified DC voltage. If the detected flow exceeds the set threshold of 2500 liters / second, the rectifier output will increase by 30%, and when the pressure exceeds 1.0 MPa, the rectifier output will increase by another 20%. If the flow is lower than 2000 liters / second and the pressure is lower than 0.8 MPa, the system will use big data analysis methods to establish a benchmark model to monitor and predict the operating status of the unit to ensure the stable operation of the system under different working conditions. These adjustments and monitoring are realized through control algorithms to ensure the high efficiency and stability of the system energy recovery. The control data of the rectification adjustment are shown in Table 1 below:

[0092] Table 1 Control data of rectification adjustment

[0093]

[0094] The table shows the system adjustments made according to the pump flow and pressure data at different time points. In this way, the rectifier output is adjusted in time when the flow or pressure is abnormal, ensuring the efficiency and stability of the system energy recovery.

[0095] S3: Use intelligent monitoring and diagnosis technology to monitor the control process, use big data analysis methods to establish a benchmark model for system operation, and complete intelligent monitoring of water supply energy recovery.

[0096] Among them, the specific calculation formula for establishing the benchmark model of system operation is:

[0097]

[0098] Among them, X baseline represents the benchmark value of the benchmark model, N represents the number of hydropower station units, x i represents the unit point of the i-th sample;

[0099] If |x real -x expected |>X baseline When the error range of the operating state is exceeded, an alarm is triggered and water supply energy recovery is completed;

[0100] If |x real -x expected |≤X baseline , it does not exceed the error range of the operating status. At this time, the hydropower station units operate normally.

[0101] Preferably, in the intelligent monitoring process of the present invention, the intelligent monitoring and diagnosis technology is used to monitor the control process in real time. For example, the system collects key parameters such as flow, pressure and voltage of the hydropower station units in real time through sensors and data acquisition equipment. Using these data, the system establishes a benchmark model through a big data analysis method to reflect the normal operation status of each unit. The benchmark model is based on a large amount of historical data and current operation data. For example, the flow data of a hydropower station averages 2000 liters / second, the pressure is 0.9 MPa, and the DC voltage is 300 volts. The system will judge whether the current state is normal based on these benchmark values. If the flow of a unit suddenly drops to 1500 liters / second or the pressure drops to 0.7 MPa, which exceeds the set error range, the system will trigger an alarm mechanism to remind the operator to deal with the abnormal situation in time. This intelligent monitoring and big data analysis method ensures the stability and efficiency of the system, helps to take measures quickly when abnormalities occur, and ensures the effective recovery of water supply energy.

[0102] In a preferred embodiment, a water supply energy recovery system for a hydropower station unit includes an analysis module, which collects pipeline branch data of the hydropower station unit and performs operation status analysis on the collected pipeline branch data; a control module, which controls the generator outlet switch state of the hydropower station unit according to the analysis result, starts the generator, rectifies the generated electric energy, and controls the rectification process according to the water pumping data of the water pump in the hydropower station unit; and a recovery module, which uses intelligent monitoring and diagnosis technology to monitor the control process, uses big data analysis methods to establish a benchmark model of system operation, and completes intelligent monitoring of water supply energy recovery.

[0103] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0104] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0105] In summary, the present invention realizes accurate monitoring and analysis of the water supply energy recovery process of the hydropower station units by adopting intelligent monitoring and diagnosis technology and big data analysis methods, overcomes the shortcomings of incomplete data collection, delayed state analysis and inaccurate control in traditional systems, and improves the response speed and control accuracy of the system through the application of multi-level control logic technology, effectively improving the energy recovery efficiency. In addition, a benchmark model of system operation is established, so that the overall system can operate more stably and efficiently, ensuring the optimal operating state of the hydropower station under different working conditions, thereby significantly improving the water resource utilization efficiency and economic benefits.

[0106] Example 2

[0107] Reference Figure 1 and Figure 2 , which is the second embodiment of the present invention, and this embodiment provides a water supply energy recovery method for a hydropower station unit. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0108] During the experiment, the system extracted the water flow and pressure data of the water pump at different time points. For example, at the beginning of the experiment, the water flow of the water pump was 2200 liters / second and the pressure was 0.9 MPa. The experimenters gradually increased the flow and pressure. When the flow reached 2600 liters / second and the pressure reached 1.2 MPa, the system automatically adjusted the rectifier output, an increase of 35%. Subsequently, the flow was reduced to 1800 liters / second and the pressure was reduced to 0.6 MPa. The system started the baseline model for monitoring. The experiment continuously observed the changes in the rectifier output and the stability of the system to ensure that the rectification process could respond and adjust quickly when the flow and pressure changed to maintain the efficient operation of the system.

[0109] In the application process of intelligent monitoring and diagnosis technology, a benchmark model of system operation was established through big data analysis. For example, in the benchmark model construction stage, the system monitored the real-time data of 50 hydropower station units and recorded the operating status of these units. It was found in the experiment that when the error of the actual operating data exceeded the set 3% threshold (for example, the actual output power of the unit was higher than the predicted value by 3%), the system would trigger an alarm and start water supply energy recovery measures. On the contrary, if the error of the actual data was within 2% (for example, the actual output power of the unit was not more than 2% different from the predicted value), the system would continue to operate normally and maintain the current operating status. Such an intelligent monitoring mechanism ensures that when an abnormal situation occurs, the system can respond quickly to achieve efficient energy recovery, while maintaining the stable operation of the system within the normal range. The intelligent monitoring data is shown in Table 2 below:

[0110] Table 2 Intelligent monitoring data table

[0111]

[0112] This table shows the unit data at different time points monitored by the system during the benchmark model construction process, including actual output power, predicted output power, actual output and predicted output errors. The defect comparison between the present invention and the prior art is shown in Table 3 below:

[0113] Table 3 Comparison of defects between the present invention and the prior art

[0114]

[0115] The table shows the advantages of the technical solution of the present invention in data acquisition, analysis accuracy, control flexibility, system monitoring, energy recovery efficiency and benchmark model establishment, which has obvious improvements and enhancements compared with the existing technology.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for recovering water supply energy of a hydropower station unit, Features: include, Collect pipeline branch data of hydropower station units and analyze the operating status of the collected pipeline branch data; According to the analysis results, the generator outlet switch state of the hydropower station unit is controlled, the generator is started, the generated electric energy is rectified, and the rectification process is controlled according to the water pumping data of the hydropower station unit; Intelligent monitoring and diagnosis technology is used to monitor the control process, and big data analysis methods are used to establish a benchmark model for system operation to complete intelligent monitoring of water supply energy recovery.

2. The water supply energy recovery method of a hydropower station unit according to claim 1, Features: The collecting of pipeline branch data of the hydropower station unit includes using a flow sensor and a pressure sensor to collect flow data and pressure data of the pipeline branch, and transmitting the collected data to a central processor via high-frequency wireless transmission for analysis. The specific formula of the transmission is: Among them, Q i，j (t) represents the cumulative flow of pipeline branches i and j at time t, represents the instantaneous flow rate q of pipeline branches i and j from time 0 to time t i，j The integral of (τ).

3. The water supply energy recovery method of a hydropower station unit according to claim 2, Features: The operation status analysis includes filtering the flow data and pressure data of the pipeline branch, analyzing the operation status of the pipeline branch using a high-dimensional multivariate linear regression model, calculating the cumulative flow and obtaining a status analysis model, and applying a machine learning algorithm to predict the status analysis model; The specific calculation formula of the state analysis model is: Q threshold =q i，j (τ)β+∈ Among them, Q threshold Vector data representing the state analysis model within the cumulative flow, q i，j (τ) represents the instantaneous flow rate, β represents the regression coefficient vector, and ∈ represents the error term vector; The prediction calculation formula of the state analysis model is: Among them, P threshold vector data representing the predicted state analysis model, represents the predicted state at time t+1, σ represents the activation function, and W hh Represents the weight matrix from hidden state to hidden state, W hx represents the weight matrix input to the hidden state, b h Bias vector representing the hidden state.

4. The water supply energy recovery method of a hydropower station unit according to claim 3, Features: The controlling of the generator outlet switch state of the hydropower station unit includes determining whether the generator outlet switch needs to be started or closed according to the operation state analysis result; If Q i，j (t)≥Q threshold or P i，j (t)≥P threshold When , turn on the generator switch; If Q i，j (t) threshold or P i，j (t) <P threshold When the generator switch is turned off, P i，j (t) represents the pressure of the generator;​ When the generator switch is turned off, the multi-level control logic technology is used to adjust the pressure of the generator. The specific calculation formula of the adjustment is: Among them, P gen (t) represents the power generated by the generator at time t, ρ represents the density of the fluid, A represents the blade area of ​​the impeller, v represents the flow rate of the fluid, C p represents the efficiency of the generator in extracting energy from the water flow, P i，j (t) represents the pressure of the generator.

5. The water supply energy recovery method of a hydropower station unit according to claim 4, Features: The rectifying of the generated electric energy includes converting the alternating current generated by the generator through a rectifier, and the specific calculation formula of the conversion is: Among them, V dc Represents the DC voltage obtained after rectification, V ac represents AC voltage, ω represents angular frequency, and t represents time; The inverter is used to convert the rectified DC voltage into AC power for the second time. The specific calculation formula for the second conversion is: Among them, V ac Represents the AC voltage obtained after inversion, V dc represents the DC voltage obtained after rectification, ω represents the angular frequency, t represents the time, and sin(ωt) represents the sine function.

6. The water supply energy recovery method of a hydropower station unit according to claim 5, Features: The control of the rectification process includes adjusting the rectified DC voltage by extracting the pumping flow rate and the pressure of the pump at time t, and processing the input variables by using a control algorithm. The input variables include the real-time flow rate Q of the pump. pump (t), real-time pressure of the water pump P pump (t) and the rectified DC voltage, the processing of the input variables includes using the control algorithm to infer the set obtained after the processing and define the control rules. The specific calculation formula of the control algorithm is: output=fuzzy_logic(Q pump (t),P pump (t),V dc (t)) Among them, output represents the algorithm output, fuzzy_logic represents the algorithm logic, Q pump (t) represents the real-time flow rate of the water pump at time t, P pump (t) represents the real-time pressure of the water pump at time t, V dc (t) represents the DC voltage at time t after rectification; At time t, the real-time flow rate of the water pump is Q pump (t)≥ set threshold Q set When , the rectifier output is increased; At time t, the real-time pressure of the water pump is P pump (t)≥ set threshold P set When , the rectifier output is increased twice; At time t, the real-time flow rate of the water pump is Q pump (t)<Set threshold Q set The real-time pressure P of the water pump at time t pump (t)<Set threshold value P set At this time, big data analysis methods are used to establish a benchmark model for system operation and monitor the operating status of each hydropower station unit.

7. The water supply energy recovery method of a hydropower station unit according to claim 6, Features: The specific calculation formula for establishing the benchmark model for system operation is: Among them, X baseline represents the benchmark value of the benchmark model, N represents the number of hydropower station units, x i represents the unit point of the i-th sample; If |x real -x expected |>X baseline When the error range of the operating state is exceeded, an alarm is triggered and water supply energy recovery is completed; If |x real -x expected |≤X baseline , it does not exceed the error range of the operating status. At this time, the hydropower station units operate normally.

8. A water supply energy recovery system for a hydropower station unit, based on the water supply energy recovery method for a hydropower station unit according to any one of claims 1 to 7, Features: include, An analysis module collects pipeline branch data of the hydropower station units and performs operation status analysis on the collected pipeline branch data; A control module controls the generator outlet switch state of the hydropower station unit according to the analysis results, starts the generator, rectifies the generated electric energy, and controls the rectification process according to the water pumping data of the hydropower station unit; The recovery module uses intelligent monitoring and diagnosis technology to monitor the control process, uses big data analysis methods to establish a benchmark model for system operation, and completes intelligent monitoring of water supply energy recovery.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program. Features: When the processor executes the computer program, the steps of the water supply energy recovery method for a hydropower station unit according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, Features: When the computer program is executed by a processor, the steps of the water supply energy recovery method for a hydropower station unit according to any one of claims 1 to 7 are implemented.

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