Intelligent energy monitoring system and method for high-efficiency refrigeration system

Abstract

The application discloses a kind of high-efficiency refrigeration system's wisdom energy monitoring system and method, belong to the technical field of refrigeration equipment, it includes detecting current cooling water pump power, cold water unit power, total power, cooling water pump outlet temperature, cold water unit inlet temperature, cold water unit outlet temperature, retrieve current set refrigeration temperature, detect current ambient temperature and associate ambient temperature with refrigeration data set;Establish database, train preliminary cooling efficiency model and final cooling efficiency model, use the current detected data to obtain predicted cold water unit inlet temperature and predicted cold water unit outlet temperature, if predicted data is different from current data, then calculate predicted power, and use predicted power to adjust current power, the application has the effect of being able to automatically adjust power according to ambient temperature and actual demand, ensure power stability.

Description

Technical Field

[0001] This invention relates to the field of refrigeration equipment, and in particular to a smart energy monitoring system and method for a high-efficiency refrigeration system. Background Technology

[0002] Currently, high-efficiency refrigeration systems are typically used in applications requiring rapid and powerful cooling. Large-scale high-efficiency refrigeration systems include equipment such as cooling towers, cooling water pumps, and chiller units. High-efficiency refrigeration systems use cooling towers to cool large quantities of cooling water to meet high-efficiency cooling requirements. A cooling tower is a device that utilizes the contact between water and air for heat exchange, lowering the water temperature through evaporative cooling and convective heat transfer. Its operation involves distributing water from a height using devices such as water distributors, allowing outside air to enter the tower and carry away the heat from the water. Therefore, the cooling efficiency of a cooling tower is affected by the ambient temperature. A chiller unit is a device that achieves cooling through a compression or heat absorption refrigeration cycle, mainly composed of a compressor, evaporator, condenser, and expansion valve. Chiller units are generally used in conjunction with cooling towers, using water cooled by the cooling tower to cool the refrigerant. System control ensures that the temperature of the output refrigerant is lower than a preset temperature. The higher the temperature of the cooling water supplied by the cooling tower, the greater the power required for the cooling unit to operate.

[0003] The existing technical solutions mentioned above have the following drawbacks: due to the continuous changes in ambient temperature, the monitoring data of the energy monitoring system on the refrigeration system will also change continuously. When the intelligent system judges and adjusts the voltage and power, misjudgments may occur, leading to power problems. Summary of the Invention

[0004] In order to automatically adjust the power supply when the ambient temperature changes, this application provides a smart energy monitoring system and method for a high-efficiency cooling system.

[0005] On the one hand, the intelligent energy monitoring method for a high-efficiency refrigeration system provided in this application adopts the following technical solution: A smart energy monitoring method for a high-efficiency refrigeration system includes the following steps: The system detects the current cooling water pump power, chiller unit power, total power, cooling water pump outlet temperature, chiller unit inlet temperature, and chiller unit outlet temperature, retrieves the current set cooling temperature, and integrates all data to generate a cooling data set. Detect the current ambient temperature and correlate it with the cooling data set; Establish a database and import the associated ambient temperature and cooling data sets into the database for storage; The initial and final cooling efficiency models were trained using data stored in the database. The current detection data is imported into the preliminary cooling efficiency model and the final cooling efficiency model to obtain the expected chiller inlet water temperature and the expected chiller outlet water temperature, respectively. Compare the expected chiller inlet water temperature with the current chiller inlet water temperature, and compare the expected chiller outlet water temperature with the current chiller outlet water temperature. If the expected chiller inlet water temperature differs from the current chiller inlet water temperature, or the expected chiller outlet water temperature differs from the current chiller outlet water temperature, then the current ambient temperature and refrigeration data are imported into the preliminary cooling efficiency model and the final cooling efficiency model to obtain the expected cooling water pump power and the expected chiller power. The expected cooling water pump power and the expected chiller power are then used to calculate the expected total power. Finally, the expected cooling water pump power, the expected chiller power, and the expected total power are used to adjust the current cooling water pump power, chiller power, and total power.

[0006] By adopting the above scheme, and by collecting and judging data such as the current ambient temperature and the set cooling temperature, the most suitable cooling water pump power, chiller unit power and total power can be automatically generated. This allows for the adjustment and monitoring of the power supply of the cooling water pump, chiller unit and the total power supply of the refrigeration system, ensuring that each piece of equipment can operate at its optimal power.

[0007] Preferably, the step of "training the preliminary cooling efficiency model and the final cooling efficiency model using the data stored in the database" includes the following steps: The system retrieves the ambient temperature and associated cooling water pump power, cooling water pump outlet temperature, and chiller inlet temperature from the database and trains a neural network model to obtain a preliminary cooling efficiency model. The ambient temperature, cooling water pump power, and cooling water pump outlet temperature are used as the input parameters of the preliminary cooling efficiency model, and the chiller inlet temperature is used as the output parameter of the preliminary cooling efficiency model. The system calls the database to set the cooling temperature, chiller inlet water temperature, chiller power, and chiller outlet water temperature to train a neural network model and obtain the final cooling efficiency model. The set cooling temperature, chiller inlet water temperature, and chiller power are used as the input parameters of the final cooling efficiency model, and the chiller outlet water temperature is used as the output parameter of the final cooling efficiency model. The step of "importing the currently detected data into the preliminary cooling efficiency model and the final cooling efficiency model to obtain the expected chiller inlet water temperature and the expected chiller outlet water temperature" includes the following steps: The current ambient temperature, cooling water pump power, and cooling water pump outlet temperature are imported into the preliminary cooling efficiency model to obtain the expected chiller inlet water temperature. The current set cooling temperature, chiller inlet water temperature, and chiller power are imported into the final cooling efficiency model to obtain the expected chiller outlet water temperature.

[0008] By adopting the above scheme, the input and output parameters are set during model training, which can ensure the accuracy of data calculation and reduce interference when calculating the inlet and outlet water temperatures of the chiller unit.

[0009] Preferably, the step "importing the current ambient temperature and refrigeration data set into the preliminary cooling efficiency model and the final cooling efficiency model to obtain the expected cooling water pump power and the expected chiller unit power" includes the following steps: The current ambient temperature and cooling data set will then be imported into the preliminary cooling efficiency model and the final cooling efficiency model. The preliminary cooling efficiency model substitutes the current ambient temperature, cooling water pump outlet temperature, and chiller unit inlet water temperature into its calculation formula to obtain the expected cooling water pump power. The final cooling efficiency model substitutes the current set cooling temperature, chiller inlet water temperature, and chiller outlet water temperature into its own calculation formula to obtain the expected chiller power. The database is searched for refrigeration data sets with the same cooling water pump power and chiller power using the estimated cooling water pump power and estimated chiller power, and the associated ambient temperature is retrieved to compare the current ambient temperature with the associated ambient temperature. If an ambient temperature with the same current ambient temperature exists, proceed to the next step; If no ambient temperature matching the current ambient temperature is available, an alarm will be issued and adjustments to the cooling water pump power, chiller unit power, and total power will be stopped.

[0010] By adopting the above scheme, when calculating the final expected power, the stored cooling data set will be called to determine whether similar operating conditions occur, thus ensuring the stability of power adjustment.

[0011] Preferably, the following steps are also included: Calculate the current total voltage based on the current total power, calculate the cooling water pump voltage based on the current cooling water pump power, calculate the chiller unit voltage based on the current chiller unit power, and transmit the cooling water pump voltage and chiller unit voltage to the database for storage. The system retrieves the cooling water pump voltage and chiller unit voltage stored in the database, and generates pump voltage curves and chiller unit voltage curves by combining the storage time. The storage time is associated with the set cooling temperature. Extract the water pump voltage curve at the same set cooling temperature, determine the peak and valley values ​​of the extracted water pump voltage curve, and calculate the difference between the peak and valley values ​​as the variable range of the water pump voltage at that set cooling temperature. Capture the voltage curves of units with the same set cooling temperature, determine the peak and valley values ​​of the captured voltage curves, and calculate the difference between the peak and valley values ​​as the variable range of the unit voltage at that set cooling temperature. Connect the mains power supply and green power supply to the refrigeration system and monitor the mains power supply input voltage and green power supply input voltage in real time. When the green power supply input voltage is lower than the total voltage but higher than the cooling water pump voltage, determine whether the green power supply input voltage is within the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage. If it is within the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage, then connect the green power supply to the cooling water pump. When the input voltage of the green power supply is lower than the total voltage but higher than the chiller unit voltage, determine whether the input voltage of the green power supply is within the range of the chiller unit voltage plus the variable range of the unit voltage. If it is within the range of the chiller unit voltage plus the variable range of the unit voltage, then connect the green power supply to the chiller unit.

[0012] By adopting the above solution, since large-scale refrigeration systems are generally located in remote areas, they are suitable for green electricity supply. Therefore, the power supply for refrigeration systems often includes green electricity such as wind power and photovoltaic power. When using green electricity, due to its instability, it is usually necessary to combine it with mains power. This system, by judging the voltage at various points, allows the green electricity power supply to directly supply power to designated equipment when the input voltage is appropriate, thus eliminating the need for voltage stabilization processes when mixing green electricity and mains power.

[0013] Preferably, the following steps are also included: If, at the current set cooling temperature, the voltage range of the cooling water pump plus the variable range of the water pump voltage overlaps with the voltage range of the chiller unit plus the variable range of the chiller unit voltage, then the midpoint of the variable range of the chiller unit voltage and the midpoint of the variable range of the water pump voltage are selected. The sum of the cooling water pump voltage and the midpoint of the variable range of the water pump voltage is calculated as the average limit of the cooling water pump voltage. The sum of the chiller unit voltage and the midpoint of the variable range of the chiller unit voltage is calculated as the average limit of the chiller unit voltage. The average limit of the cooling water pump voltage and the average limit of the chiller unit voltage are then determined to be the closest to the average limit of the green power supply input voltage. The cooling water pump or chiller unit corresponding to the determined average limit is then connected to the green power supply.

[0014] By adopting the above scheme, if the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage is similar to the voltage range of the chiller unit voltage plus the variable range of the chiller unit voltage, the system will select the most suitable device to use the green power supply and connect it to the green power supply.

[0015] On the other hand, the intelligent energy monitoring system for a high-efficiency refrigeration system provided in this application adopts the following technical solution: A smart energy monitoring system for a high-efficiency refrigeration system includes a data detection module, a data storage module, a model generation module, a water pump judgment module, a unit judgment module, and a power distribution control module; The data detection module detects the current cooling water pump power, chiller unit power, total power, cooling water pump outlet temperature, chiller unit inlet temperature, and chiller unit outlet temperature, retrieves the current set cooling temperature, integrates all data to generate a cooling data set, detects the current ambient temperature and associates the ambient temperature with the cooling data set, and transmits the ambient temperature and cooling data set to the data storage module. The data storage module receives and stores ambient temperature and cooling data sets. The model generation module calls the ambient temperature and refrigeration data stored in the data storage module to train the preliminary cooling efficiency model and the final cooling efficiency model. The preliminary cooling efficiency model is transmitted to the water pump judgment module and the power distribution control module, and the final cooling efficiency model is transmitted to the unit judgment module and the power distribution control module. The water pump judgment module imports the currently detected data into the preliminary cooling efficiency model to obtain the expected chiller inlet water temperature, compares the expected chiller inlet water temperature with the current chiller inlet water temperature, and if the expected chiller inlet water temperature is different from the current chiller inlet water temperature, it sends an adjustment signal to the power distribution control module. The unit judgment module imports the currently detected data into the final cooling efficiency model to obtain the expected chiller outlet water temperature, compares the expected chiller outlet water temperature with the current chiller outlet water temperature, and if the expected chiller outlet water temperature is different from the current chiller outlet water temperature, it sends an adjustment signal to the power distribution control module. After receiving the adjustment signal, the power distribution control module imports the current ambient temperature and cooling data into the preliminary cooling efficiency model and the final cooling efficiency model to obtain the expected cooling water pump power and the expected chiller unit power. It then uses the expected cooling water pump power and the expected chiller unit power to calculate the expected total power, and finally uses the expected cooling water pump power, the expected chiller unit power, and the expected total power to adjust the current cooling water pump power, chiller unit power, and total power.

[0016] By adopting the above scheme, and by collecting and judging data such as the current ambient temperature and the set cooling temperature, the most suitable cooling water pump power, chiller unit power and total power can be automatically generated. This allows for the adjustment and monitoring of the power supply of the cooling water pump, chiller unit and the total power supply of the refrigeration system, ensuring that each piece of equipment can operate at its optimal power.

[0017] Preferably, the model generation module calls the ambient temperature and associated cooling water pump power, cooling water pump outlet temperature, and chiller inlet temperature from the data storage module to train a neural network model to obtain a preliminary cooling efficiency model. The ambient temperature, cooling water pump power, and cooling water pump outlet temperature are used as input parameters of the preliminary cooling efficiency model, and the chiller inlet temperature is used as the output parameter of the preliminary cooling efficiency model. The module then calls the set cooling temperature, chiller inlet temperature, chiller power, and chiller outlet temperature from the data storage module to train a neural network model to obtain a final cooling efficiency model. The set cooling temperature, chiller inlet temperature, and chiller power are used as input parameters of the final cooling efficiency model, and the chiller outlet temperature is used as the output parameter of the final cooling efficiency model. The water pump judgment module imports the current ambient temperature, cooling water pump power and cooling water pump outlet temperature into the preliminary cooling efficiency model to obtain the expected chiller unit inlet water temperature. The unit judgment module imports the current set cooling temperature, chiller inlet water temperature, and chiller power into the final cooling efficiency model to obtain the expected chiller outlet water temperature.

[0018] By adopting the above scheme, the input and output parameters are set during model training, which can ensure the accuracy of data calculation and reduce interference when calculating the inlet and outlet water temperatures of the chiller unit.

[0019] Preferably, it also includes a data verification module; after receiving the adjustment signal, the power distribution control module imports the current ambient temperature and cooling data set into the preliminary cooling efficiency model and the final cooling efficiency model. The preliminary cooling efficiency model substitutes the current ambient temperature, cooling water pump outlet temperature, and chiller unit inlet water temperature into its own calculation formula to obtain the expected cooling water pump power. The final cooling efficiency model substitutes the current set cooling temperature, chiller unit inlet water temperature, and chiller unit outlet water temperature into its own calculation formula to obtain the expected chiller unit power. The data verification module calls the expected cooling water pump power and expected chiller power from the power distribution control module. It then uses these values ​​to search for refrigeration data groups with the same cooling water pump power and chiller power in the data storage module. The module also calls the associated ambient temperature and compares the current ambient temperature with the associated ambient temperature. If an ambient temperature matching the current ambient temperature is found, the module proceeds to the next step. Otherwise, an alarm is issued and the adjustment of cooling water pump power, chiller power, and total power is stopped.

[0020] By adopting the above scheme, when calculating the final expected power, the stored cooling data set will be called to determine whether similar operating conditions occur, thus ensuring the stability of power adjustment.

[0021] Preferably, it also includes a voltage calculation module, a voltage judgment module, and a power control module; the data detection module detects the mains power input voltage and the green power input voltage in real time; The voltage calculation module calls the total power, cooling water pump power and chiller unit power detected by the data detection module, calculates the current total voltage based on the current total power, calculates the cooling water pump voltage based on the current cooling water pump power, calculates the chiller unit voltage based on the current chiller unit power, and transmits the cooling water pump voltage and chiller unit voltage to the data storage module for storage. The voltage judgment module calls the cooling water pump voltage and chiller unit voltage stored in the data storage module, and generates water pump voltage curve and chiller unit voltage curve by combining the storage time. The storage time is associated with the set cooling temperature. The water pump voltage curve with the same set cooling temperature is extracted, and the peak value and valley value of the extracted water pump voltage curve are judged. The difference between the peak value and valley value is calculated as the variable range of water pump voltage under the set cooling temperature. The chiller unit voltage curve with the same set cooling temperature is extracted, and the peak value and valley value of the extracted chiller unit voltage curve are judged. The difference between the peak value and valley value is calculated as the variable range of chiller unit voltage under the set cooling temperature. The variable range of water pump voltage and the variable range of chiller unit voltage are transmitted to the power control module. The power control module connects the mains power and the green power supply to the refrigeration system. The power control module calls the data detection module to detect the green power input voltage, total voltage, cooling water pump voltage, and chiller unit voltage. When the green power input voltage is lower than the total voltage but higher than the cooling water pump voltage, it determines whether the green power input voltage is within the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage. If it is within the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage, then the green power supply is connected to the cooling water pump. When the green power input voltage is lower than the total voltage but higher than the chiller unit voltage, it determines whether the green power input voltage is within the voltage range of the chiller unit voltage plus the variable range of the chiller unit voltage. If it is within the voltage range of the chiller unit voltage plus the variable range of the chiller unit voltage, then the green power supply is connected to the chiller unit.

[0022] By adopting the above solution, since large-scale refrigeration systems are generally located in remote areas, they are suitable for green electricity supply. Therefore, the power supply for refrigeration systems often includes green electricity such as wind power and photovoltaic power. When using green electricity, due to its instability, it is usually necessary to combine it with mains power. This system, by judging the voltage at various points, allows the green electricity power supply to directly supply power to designated equipment when the input voltage is appropriate, thus eliminating the need for voltage stabilization processes when mixing green electricity and mains power.

[0023] Preferably, the power control module determines whether there is an overlap between the voltage range of the cooling water pump plus the variable range of the water pump voltage and the voltage range of the chiller unit plus the variable range of the unit voltage at the current set cooling temperature. If there is an overlap, the module selects the median value of the variable range of the unit voltage and the median value of the variable range of the water pump voltage, calculates the sum of the cooling water pump voltage and the median value of the variable range of the water pump voltage as the average limit of the cooling water pump voltage, calculates the sum of the chiller unit voltage and the median value of the variable range of the unit voltage as the average limit of the chiller unit voltage, and determines the average limit of the green power supply input voltage between the average limit of the cooling water pump voltage and the average limit of the chiller unit voltage. The module then connects the cooling water pump or chiller unit corresponding to the determined average limit to the green power supply.

[0024] By adopting the above scheme, if the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage is similar to the voltage range of the chiller unit voltage plus the variable range of the chiller unit voltage, the system will select the most suitable device to use the green power supply and connect it to the green power supply.

[0025] In summary, the present invention has the following beneficial effects: 1. It can adjust and monitor the power supply of cooling water pumps, chiller units, and the main power supply of the refrigeration system to ensure that each piece of equipment can operate at its optimal power.

[0026] 2. By judging the voltage at various points, when the input voltage of the green power supply is appropriate, the green power supply can be directly used to supply power to the designated equipment, thus eliminating the need for voltage regulation and other processes when green power and mains power are mixed. Attached Figure Description

[0027] Figure 1 This is an overall system block diagram of Embodiment 2 of this application.

[0028] Explanation of reference numerals in the attached figures: 1. Data detection module; 2. Data storage module; 3. Model generation module; 4. Pump judgment module; 5. Unit judgment module; 6. Power distribution control module; 7. Data verification module; 8. Voltage calculation module; 9. Voltage judgment module; 10. Power control module. Detailed Implementation

[0029] Example 1: This application discloses a smart energy monitoring method for a high-efficiency refrigeration system, with the following specific steps: S100 detects the current cooling water pump power, chiller unit power, total power, cooling water pump outlet temperature, chiller unit inlet temperature, and chiller unit outlet temperature, retrieves the current set cooling temperature, and integrates all data to generate a cooling data set.

[0030] S101. Detect the current ambient temperature and associate the ambient temperature with the cooling data set.

[0031] S102. Establish a database and import the associated ambient temperature and cooling data groups into the database for storage.

[0032] S200: Call the ambient temperature and associated cooling water pump power, cooling water pump outlet temperature, and chiller inlet temperature from the database and train the neural network model to obtain a preliminary cooling efficiency model. Use the ambient temperature, cooling water pump power, and cooling water pump outlet temperature as input parameters of the preliminary cooling efficiency model, and use the chiller inlet temperature as output parameter of the preliminary cooling efficiency model.

[0033] S201. Call the database to set the cooling temperature, chiller inlet water temperature, chiller power and chiller outlet water temperature to train the neural network model and obtain the final cooling efficiency model. Use the set cooling temperature, chiller inlet water temperature and chiller power as input parameters of the final cooling efficiency model and use the chiller outlet water temperature as output parameter of the final cooling efficiency model.

[0034] S202. Import the current ambient temperature, cooling water pump power, and cooling water pump outlet temperature into the preliminary cooling efficiency model to obtain the expected chiller inlet water temperature. Import the current set cooling temperature, chiller inlet water temperature, and chiller power into the final cooling efficiency model to obtain the expected chiller outlet water temperature.

[0035] S300: Compare the expected chiller inlet water temperature with the current chiller inlet water temperature, and compare the expected chiller outlet water temperature with the current chiller outlet water temperature.

[0036] S301. If the expected chiller inlet water temperature is different from the current chiller inlet water temperature, or the expected chiller outlet water temperature is different from the current chiller outlet water temperature, then import the current ambient temperature and refrigeration data into the preliminary cooling efficiency model and the final cooling efficiency model.

[0037] S302. The preliminary cooling efficiency model substitutes the current ambient temperature, cooling water pump outlet temperature, and chiller unit inlet water temperature into its own calculation formula to obtain the expected cooling water pump power.

[0038] S303, the final cooling efficiency model substitutes the current set cooling temperature, chiller inlet water temperature, and chiller outlet water temperature into its own calculation formula to obtain the expected chiller power.

[0039] S304. Calculate the expected total power using the expected cooling water pump power and the expected chiller unit power, and adjust the current cooling water pump power, chiller unit power and total power using the expected cooling water pump power, the expected chiller unit power and the expected total power.

[0040] S400: Use the estimated cooling water pump power and estimated chiller power to search the database for refrigeration data sets with the same cooling water pump power and chiller power, and retrieve the associated ambient temperature to compare the current ambient temperature with the associated ambient temperature.

[0041] S401. If an ambient temperature with the same current ambient temperature exists, proceed to the next step.

[0042] S402. If no ambient temperature matching the current ambient temperature is found, an alarm is issued and adjustments to the cooling water pump power, chiller unit power, and total power are stopped. When calculating the final projected power, stored refrigeration data sets are retrieved to determine if similar operating conditions occur, ensuring the stability of power adjustments.

[0043] S500: Calculate the current total voltage based on the current total power, calculate the cooling water pump voltage based on the current cooling water pump power, calculate the chiller unit voltage based on the current chiller unit power, and transmit the cooling water pump voltage and chiller unit voltage to the database for storage.

[0044] S600: Calls the cooling water pump voltage and chiller unit voltage stored in the database, generates pump voltage curves and chiller unit voltage curves based on the storage time, and sets the cooling temperature in conjunction with the storage time.

[0045] S601. Extract the water pump voltage curve at the same set cooling temperature, determine the peak and valley values ​​of the extracted water pump voltage curve, and calculate the difference between the peak and valley values ​​as the variable range of the water pump voltage at the set cooling temperature.

[0046] S602. Extract the unit voltage curves at the same set cooling temperature, determine the peak and valley values ​​of the extracted unit voltage curves, and calculate the difference between the peak and valley values ​​as the variable range of the unit voltage at that set cooling temperature.

[0047] S700 connects the mains power supply and green power supply to the refrigeration system and monitors the mains power supply input voltage and green power supply input voltage in real time.

[0048] S800 When the green power supply input voltage is lower than the total voltage but higher than the cooling water pump voltage, determine whether the green power supply input voltage is within the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage. If it is within the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage, then connect the green power supply to the cooling water pump.

[0049] S801. When the green power supply input voltage is lower than the total voltage but higher than the chiller unit voltage, determine whether the green power supply input voltage is within the range of the chiller unit voltage plus the variable range of the chiller unit voltage. If it is within this range, connect the green power supply to the chiller unit. Since large refrigeration systems are generally located in remote areas, making them suitable for green power supply, the power supply for refrigeration systems often includes green power from wind power, photovoltaic power, etc. When using green power, due to its instability, it usually needs to be combined with mains power. This system, by judging the voltage at various points, allows the green power supply to directly supply power to designated equipment when the green power supply input voltage is appropriate, eliminating the need for voltage stabilization processes when mixing green power and mains power.

[0050] S802. If, at the current set cooling temperature, the voltage range of the cooling water pump plus its variable range overlaps with the voltage range of the chiller unit plus its variable range, then the midpoint of the chiller unit's variable range and the midpoint of the cooling water pump's variable range are selected. The sum of these two values ​​is used as the average limit of the cooling water pump voltage. Similarly, the sum of the midpoint of the chiller unit's voltage and its variable range is used as the average limit of the chiller unit's voltage. The system then determines which of these two average limits is closest to the average limit of the green power supply input voltage and connects the cooling water pump or chiller unit corresponding to this average limit to the green power supply. If the voltage range of the cooling water pump plus its variable range is very close to the voltage range of the chiller unit plus its variable range, the system will connect the device most suitable for using the green power supply.

[0051] The implementation principle of the intelligent energy monitoring system and method for a high-efficiency refrigeration system in this application embodiment is as follows: by collecting and judging data such as the current ambient temperature and the set refrigeration temperature, the system can automatically generate the most suitable cooling water pump power, chiller unit power and total power, thereby adjusting and monitoring the power supply of the cooling water pump, the power supply of the chiller unit and the total power supply of the refrigeration system to ensure that each device can operate at the best power.

[0052] Example 2: This application discloses a smart energy monitoring system for a high-efficiency refrigeration system, such as... Figure 1 As shown, it includes a data detection module 1, a data storage module 2, a model generation module 3, a water pump judgment module 4, a unit judgment module 5, a power distribution control module 6, a data verification module 7, a voltage calculation module 8, a voltage judgment module 9, and a power control module 10.

[0053] Data detection module 1 detects the current cooling water pump power, chiller unit power, total power, cooling water pump outlet temperature, chiller unit inlet temperature, and chiller unit outlet temperature. It retrieves the current set cooling temperature, integrates all data to generate a cooling data set, detects the current ambient temperature, associates the ambient temperature with the cooling data set, and transmits the ambient temperature and cooling data set to data storage module 2. Data storage module 2 receives the ambient temperature and cooling data set and stores them.

[0054] Model generation module 3 calls the ambient temperature and associated cooling water pump power, cooling water pump outlet temperature, and chiller inlet temperature from data storage module 2 to train a neural network model, obtaining a preliminary cooling efficiency model. The ambient temperature, cooling water pump power, and cooling water pump outlet temperature are used as input parameters for the preliminary cooling efficiency model, and the chiller inlet temperature is used as the output parameter. The model then calls the set cooling temperature, chiller inlet temperature, chiller power, and chiller outlet temperature from data storage module 2 to train the neural network model, obtaining a final cooling efficiency model. The set cooling temperature, chiller inlet temperature, and chiller power are used as input parameters for the final cooling efficiency model, and the chiller outlet temperature is used as the output parameter. The final cooling efficiency model is then transmitted to unit judgment module 5 and power distribution control module 6.

[0055] The water pump judgment module 4 imports the current ambient temperature, cooling water pump power, and cooling water pump outlet temperature into the preliminary cooling efficiency model to obtain the expected chiller inlet water temperature. It then compares the expected chiller inlet water temperature with the current chiller inlet water temperature. If the expected chiller inlet water temperature is different from the current chiller inlet water temperature, it sends an adjustment signal to the power distribution control module 6.

[0056] The unit judgment module 5 imports the current set cooling temperature, chiller inlet water temperature and chiller power into the final cooling efficiency model to obtain the expected chiller outlet water temperature. It compares the expected chiller outlet water temperature with the current chiller outlet water temperature. If the expected chiller outlet water temperature is different from the current chiller outlet water temperature, it sends an adjustment signal to the power distribution control module 6.

[0057] After receiving the adjustment signal, the power distribution control module 6 imports the current ambient temperature and cooling data into the preliminary cooling efficiency model and the final cooling efficiency model to obtain the expected cooling water pump power and the expected chiller unit power. It then uses the expected cooling water pump power and the expected chiller unit power to calculate the expected total power and adjusts the current cooling water pump power, chiller unit power and total power using the expected cooling water pump power, the expected chiller unit power and the expected total power.

[0058] After receiving the adjustment signal, the power distribution control module 6 imports the current ambient temperature and cooling data into the preliminary cooling efficiency model and the final cooling efficiency model. The preliminary cooling efficiency model substitutes the current ambient temperature, cooling water pump outlet temperature, and chiller inlet water temperature into its own calculation formula to obtain the expected cooling water pump power. The final cooling efficiency model substitutes the current set cooling temperature, chiller inlet water temperature, and chiller outlet water temperature into its own calculation formula to obtain the expected chiller power.

[0059] The data verification module 7 calls the estimated cooling water pump power and estimated chiller power from the power distribution control module 6. It then uses these estimated cooling water pump power and estimated chiller power to search for refrigeration data sets with the same cooling water pump power and chiller power in the data storage module 2. It also calls the associated ambient temperature and compares it to the current ambient temperature. If a matching ambient temperature is found, the process proceeds to the next step. Otherwise, an alarm is issued and adjustments to the cooling water pump power, chiller power, and total power are stopped. When calculating the final estimated power, the stored refrigeration data sets are called to determine if similar operating conditions occur, ensuring the stability of power adjustments.

[0060] The voltage calculation module 8 calls the total power, cooling water pump power and chiller unit power detected by the data detection module 1, calculates the current total voltage based on the current total power, calculates the cooling water pump voltage based on the current cooling water pump power, calculates the chiller unit voltage based on the current chiller unit power, and transmits the cooling water pump voltage and chiller unit voltage to the data storage module 2 for storage.

[0061] The voltage judgment module 9 calls the cooling water pump voltage and chiller unit voltage stored in the data storage module 2, and generates a water pump voltage curve and a chiller unit voltage curve by combining the storage time. The storage time is associated with the set cooling temperature. The water pump voltage curve with the same set cooling temperature is captured, and the peak and valley values ​​of the captured water pump voltage curve are judged. The difference between the peak and valley values ​​is calculated as the variable range of the water pump voltage under the set cooling temperature. The chiller unit voltage curve with the same set cooling temperature is captured, and the peak and valley values ​​of the captured chiller unit voltage curve are judged. The difference between the peak and valley values ​​is calculated as the variable range of the chiller unit voltage under the set cooling temperature. The variable range of the water pump voltage and the variable range of the chiller unit voltage are transmitted to the power control module 10.

[0062] The power control module 10 connects the mains power and the green power supply to the refrigeration system. The power control module 10 calls the green power input voltage, total voltage, cooling water pump voltage, and chiller unit voltage from the data detection module 1. When the green power input voltage is lower than the total voltage but higher than the cooling water pump voltage, it determines whether the green power input voltage is within the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage. If it is within the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage, then the green power supply is connected to the cooling water pump. When the green power input voltage is lower than the total voltage but higher than the chiller unit voltage, it determines whether the green power input voltage is within the voltage range of the chiller unit voltage plus the variable range of the chiller unit voltage. If it is within the voltage range of the chiller unit voltage plus the variable range of the chiller unit voltage, then the green power supply is connected to the chiller unit.

[0063] Since large-scale refrigeration systems are typically located in remote areas, they are well-suited for green electricity supply. Therefore, the power supply for these systems often includes green electricity from sources such as wind and solar power. However, due to the instability of green electricity, it usually needs to be combined with mains power. This system, by assessing the voltage at various points, allows green electricity to directly supply power to designated equipment when the input voltage is appropriate, eliminating the need for voltage stabilization processes when mixing green and mains power.

[0064] The power control module 10 determines whether there is any overlap between the cooling water pump voltage plus the variable range of the water pump voltage and the chiller unit voltage plus the variable range of the chiller unit voltage at the current set cooling temperature. If there is overlap, the system selects the median value of the chiller unit voltage variable range and the median value of the water pump voltage variable range, calculates the sum of the cooling water pump voltage and the median value of the water pump voltage variable range as the average limit of the cooling water pump voltage, calculates the sum of the chiller unit voltage and the median value of the chiller unit voltage variable range as the average limit of the chiller unit voltage, and determines which of the two average limits is closest to the average limit of the green power supply input voltage. The system then connects the cooling water pump or chiller unit corresponding to the determined average limit to the green power supply. If the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage is close to the voltage range of the chiller unit voltage plus the variable range of the chiller unit voltage variable range, the system will select the most suitable device to use the green power supply to connect to the green power supply.

[0065] The embodiments described herein are preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape, and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A smart energy monitoring method for a high-efficiency refrigeration system, characterized in that, Includes the following steps: The system detects the current cooling water pump power, chiller unit power, total power, cooling water pump outlet temperature, chiller unit inlet temperature, and chiller unit outlet temperature, retrieves the current set cooling temperature, and integrates all data to generate a cooling data set. Detect the current ambient temperature and correlate it with the cooling data set; Establish a database and import the associated ambient temperature and cooling data sets into the database for storage; The initial and final cooling efficiency models were trained using data stored in the database. The current detection data is imported into the preliminary cooling efficiency model and the final cooling efficiency model to obtain the expected chiller inlet water temperature and the expected chiller outlet water temperature, respectively. Compare the expected chiller inlet water temperature with the current chiller inlet water temperature, and compare the expected chiller outlet water temperature with the current chiller outlet water temperature. If the expected chiller inlet water temperature differs from the current chiller inlet water temperature, or the expected chiller outlet water temperature differs from the current chiller outlet water temperature, then the current ambient temperature and refrigeration data are imported into the preliminary cooling efficiency model and the final cooling efficiency model to obtain the expected cooling water pump power and the expected chiller power. The expected cooling water pump power and the expected chiller power are then used to calculate the expected total power. Finally, the expected cooling water pump power, the expected chiller power, and the expected total power are used to adjust the current cooling water pump power, chiller power, and total power.

2. The intelligent energy monitoring method for a high-efficiency refrigeration system according to claim 1, characterized in that, The step of "training the preliminary cooling efficiency model and the final cooling efficiency model using the data stored in the database" includes the following steps: The system retrieves the ambient temperature and associated cooling water pump power, cooling water pump outlet temperature, and chiller inlet temperature from the database and trains a neural network model to obtain a preliminary cooling efficiency model. The ambient temperature, cooling water pump power, and cooling water pump outlet temperature are used as the input parameters of the preliminary cooling efficiency model, and the chiller inlet temperature is used as the output parameter of the preliminary cooling efficiency model. The system calls the database to set the cooling temperature, chiller inlet water temperature, chiller power, and chiller outlet water temperature to train a neural network model and obtain the final cooling efficiency model. The set cooling temperature, chiller inlet water temperature, and chiller power are used as the input parameters of the final cooling efficiency model, and the chiller outlet water temperature is used as the output parameter of the final cooling efficiency model. The step of "importing the currently detected data into the preliminary cooling efficiency model and the final cooling efficiency model to obtain the expected chiller inlet water temperature and the expected chiller outlet water temperature" includes the following steps: The current ambient temperature, cooling water pump power, and cooling water pump outlet temperature are imported into the preliminary cooling efficiency model to obtain the expected chiller inlet water temperature. The current set cooling temperature, chiller inlet water temperature, and chiller power are imported into the final cooling efficiency model to obtain the expected chiller outlet water temperature.

3. The intelligent energy monitoring method for a high-efficiency refrigeration system according to claim 2, characterized in that, The step "importing the current ambient temperature and refrigeration data into the preliminary cooling efficiency model and the final cooling efficiency model to obtain the expected cooling water pump power and the expected chiller unit power" includes the following steps: The current ambient temperature and cooling data set will then be imported into the preliminary cooling efficiency model and the final cooling efficiency model. The preliminary cooling efficiency model substitutes the current ambient temperature, cooling water pump outlet temperature, and chiller unit inlet water temperature into its own calculation formula to obtain the expected cooling water pump power. The final cooling efficiency model substitutes the current set cooling temperature, chiller inlet water temperature, and chiller outlet water temperature into its own calculation formula to obtain the expected chiller power. The database is searched for refrigeration data sets with the same cooling water pump power and chiller power using the estimated cooling water pump power and estimated chiller power, and the associated ambient temperature is retrieved to compare the current ambient temperature with the associated ambient temperature. If an ambient temperature with the same current ambient temperature exists, proceed to the next step; If no ambient temperature matching the current ambient temperature is available, an alarm will be issued and adjustments to the cooling water pump power, chiller unit power, and total power will be stopped.

4. The intelligent energy monitoring method for a high-efficiency refrigeration system according to claim 1, characterized in that, It also includes the following steps: Calculate the current total voltage based on the current total power, calculate the cooling water pump voltage based on the current cooling water pump power, calculate the chiller unit voltage based on the current chiller unit power, and transmit the cooling water pump voltage and chiller unit voltage to the database for storage. The system retrieves the cooling water pump voltage and chiller unit voltage stored in the database, and generates pump voltage curves and chiller unit voltage curves by combining the storage time. The storage time is associated with the set cooling temperature. Extract the water pump voltage curve at the same set cooling temperature, determine the peak and valley values ​​of the extracted water pump voltage curve, and calculate the difference between the peak and valley values ​​as the variable range of the water pump voltage at that set cooling temperature. Capture the voltage curves of units with the same set cooling temperature, determine the peak and valley values ​​of the captured voltage curves, and calculate the difference between the peak and valley values ​​as the variable range of the unit voltage at that set cooling temperature. Connect the mains power supply and green power supply to the refrigeration system and monitor the mains power supply input voltage and green power supply input voltage in real time. When the green power supply input voltage is lower than the total voltage but higher than the cooling water pump voltage, determine whether the green power supply input voltage is within the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage. If it is within the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage, then connect the green power supply to the cooling water pump. When the input voltage of the green power supply is lower than the total voltage but higher than the chiller unit voltage, determine whether the input voltage of the green power supply is within the range of the chiller unit voltage plus the variable range of the unit voltage. If it is within the range of the chiller unit voltage plus the variable range of the unit voltage, then connect the green power supply to the chiller unit.

5. The intelligent energy monitoring method for a high-efficiency refrigeration system according to claim 4, characterized in that, It also includes the following steps: If, at the current set cooling temperature, the voltage range of the cooling water pump plus the variable range of the water pump voltage overlaps with the voltage range of the chiller unit plus the variable range of the chiller unit voltage, then the midpoint of the variable range of the chiller unit voltage and the midpoint of the variable range of the water pump voltage are selected. The sum of the cooling water pump voltage and the midpoint of the variable range of the water pump voltage is calculated as the average limit of the cooling water pump voltage. The sum of the chiller unit voltage and the midpoint of the variable range of the chiller unit voltage is calculated as the average limit of the chiller unit voltage. The average limit of the cooling water pump voltage and the average limit of the chiller unit voltage are then determined to be the closest to the average limit of the green power supply input voltage. The cooling water pump or chiller unit corresponding to the determined average limit is then connected to the green power supply.

6. A smart energy monitoring system for a high-efficiency refrigeration system, characterized in that: It includes a data detection module (1), a data storage module (2), a model generation module (3), a water pump judgment module (4), a unit judgment module (5), and a power distribution control module (6); The data detection module (1) detects the current cooling water pump power, chiller unit power, total power, cooling water pump outlet temperature, chiller unit inlet temperature, chiller unit outlet temperature, retrieves the current set cooling temperature, integrates all data to generate a cooling data group, detects the current ambient temperature and associates the ambient temperature with the cooling data group, and transmits the ambient temperature and cooling data group to the data storage module (2). The data storage module (2) receives and stores ambient temperature and cooling data sets. The model generation module (3) calls the ambient temperature and refrigeration data group stored in the data storage module (2) to train the preliminary cooling efficiency model and the final cooling efficiency model. The preliminary cooling efficiency model is transmitted to the water pump judgment module (4) and the power distribution control module (6), and the final cooling efficiency model is transmitted to the unit judgment module (5) and the power distribution control module (6). The water pump judgment module (4) imports the currently detected data into the preliminary cooling efficiency model to obtain the expected chiller inlet water temperature, compares the expected chiller inlet water temperature with the current chiller inlet water temperature, and if the expected chiller inlet water temperature is different from the current chiller inlet water temperature, it sends an adjustment signal to the power distribution control module (6). The unit judgment module (5) imports the currently detected data into the final cooling efficiency model to obtain the expected chiller outlet water temperature, compares the expected chiller outlet water temperature with the current chiller outlet water temperature, and if the expected chiller outlet water temperature is different from the current chiller outlet water temperature, it sends an adjustment signal to the power distribution control module (6). After receiving the adjustment signal, the power distribution control module (6) imports the current ambient temperature and cooling data into the preliminary cooling efficiency model and the final cooling efficiency model to obtain the expected cooling water pump power and the expected chiller power. It then uses the expected cooling water pump power and the expected chiller power to calculate the expected total power and adjusts the current cooling water pump power, chiller power and total power using the expected cooling water pump power, the expected chiller power and the expected total power.

7. The intelligent energy monitoring system for a high-efficiency refrigeration system according to claim 6, characterized in that: The model generation module (3) calls the ambient temperature and associated cooling water pump power, cooling water pump outlet temperature, and chiller unit inlet temperature in the data storage module (2) and trains the neural network model to obtain a preliminary cooling efficiency model. The ambient temperature, cooling water pump power, and cooling water pump outlet temperature are used as input parameters of the preliminary cooling efficiency model, and the chiller unit inlet temperature is used as output parameter of the preliminary cooling efficiency model. The set cooling temperature, chiller unit inlet temperature, chiller unit power, and chiller unit outlet temperature in the data storage module (2) are called to train the neural network model to obtain a final cooling efficiency model. The set cooling temperature, chiller unit inlet temperature, and chiller unit power are used as input parameters of the final cooling efficiency model, and the chiller unit outlet temperature is used as output parameter of the final cooling efficiency model. The water pump judgment module (4) imports the current ambient temperature, cooling water pump power and cooling water pump outlet temperature into the preliminary cooling efficiency model to obtain the expected chiller unit inlet temperature. The unit judgment module (5) imports the current set cooling temperature, chiller inlet water temperature and chiller power into the final cooling efficiency model to obtain the expected chiller outlet water temperature.

8. The intelligent energy monitoring system for a high-efficiency refrigeration system according to claim 7, characterized in that: It also includes a data verification module (7); after receiving the adjustment signal, the power distribution control module (6) imports the current ambient temperature and cooling data group into the preliminary cooling efficiency model and the final cooling efficiency model. The preliminary cooling efficiency model substitutes the current ambient temperature, cooling water pump outlet temperature, and chiller unit inlet water temperature into its own calculation formula to obtain the expected cooling water pump power. The final cooling efficiency model substitutes the current set cooling temperature, chiller unit inlet water temperature, and chiller unit outlet water temperature into its own calculation formula to obtain the expected chiller unit power. The data verification module (7) calls the expected cooling water pump power and expected chiller power of the power distribution control module (6), uses the expected cooling water pump power and expected chiller power to search for the same cooling water pump power and chiller power in the data storage module (2), and calls the associated ambient temperature, compares the current ambient temperature with the associated ambient temperature. If there is an ambient temperature that is the same as the current ambient temperature, proceed to the next step. If there is no ambient temperature that is the same as the current ambient temperature, issue an alarm and stop adjusting the cooling water pump power, chiller power and total power.

9. The intelligent energy monitoring system for a high-efficiency refrigeration system according to claim 6, characterized in that: It also includes a voltage calculation module (8), a voltage judgment module (9), and a power control module (10); the data detection module (1) detects the mains power input voltage and the green power input voltage in real time; The voltage calculation module (8) calls the total power, cooling water pump power and chiller unit power detected by the data detection module (1), calculates the current total voltage based on the current total power, calculates the cooling water pump voltage based on the current cooling water pump power, calculates the chiller unit voltage based on the current chiller unit power, and transmits the cooling water pump voltage and chiller unit voltage to the data storage module (2) for storage. The voltage judgment module (9) calls the cooling water pump voltage and chiller unit voltage stored in the data storage module (2), generates the water pump voltage curve and the chiller unit voltage curve by combining the storage time, associates the storage time with the set cooling temperature, extracts the water pump voltage curve with the same set cooling temperature, and judges the peak and valley values ​​of the extracted water pump voltage curve, calculates the difference between the peak and valley values ​​as the variable range of the water pump voltage under the set cooling temperature, extracts the chiller unit voltage curve with the same set cooling temperature, and judges the peak and valley values ​​of the extracted chiller unit voltage curve, calculates the difference between the peak and valley values ​​as the variable range of the chiller unit voltage under the set cooling temperature, and transmits the variable range of the water pump voltage and the variable range of the chiller unit voltage to the power control module (10). The power control module (10) connects the mains power and the green power to the refrigeration system. The power control module (10) calls the green power input voltage, total voltage, cooling water pump voltage and chiller voltage of the data detection module (1). When the green power input voltage is lower than the total voltage and higher than the cooling water pump voltage, it determines whether the green power input voltage is within the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage. If it is within the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage, the green power is connected to the cooling water pump. When the green power input voltage is lower than the total voltage and higher than the chiller voltage, it determines whether the green power input voltage is within the voltage range of the chiller voltage plus the variable range of the chiller voltage. If it is within the voltage range of the chiller voltage plus the variable range of the chiller voltage, the green power is connected to the chiller.

10. The intelligent energy monitoring system for a high-efficiency refrigeration system according to claim 9, characterized in that: The power control module (10) determines whether there is an overlap between the voltage range of the cooling water pump voltage plus the variable range of the water pump voltage and the voltage range of the chiller voltage plus the variable range of the unit voltage under the current set cooling temperature. If there is an overlap, the middle value of the variable range of the unit voltage and the middle value of the variable range of the water pump voltage are selected, and the sum of the cooling water pump voltage and the middle value of the variable range of the water pump voltage is calculated as the average limit of the cooling water pump voltage. The sum of the voltage of the chiller and the middle value of the variable range of the unit voltage is calculated as the average limit of the chiller voltage. The module determines the average limit of the green power supply input voltage that is closest to the average limit of the average limit of the cooling water pump voltage and the average limit of the chiller voltage. The cooling water pump or chiller corresponding to the determined average limit is connected to the green power supply.

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