Compressor surge detection methods, devices, air conditioning units and storage media

Abstract

This application relates to a compressor surge detection method, apparatus, air conditioning unit, and storage medium. The method includes: acquiring compressor operating parameter data; processing the operating parameter data within the current time window using a sliding window mechanism based on a preset time window to obtain a first characteristic value characterizing the stable operating level of the compressor within the current time window, and a second characteristic value characterizing the intensity of operating fluctuations of the compressor within the current time window; determining a fluctuation threshold corresponding to the current time window based on the first characteristic value; and determining that the compressor is in a surge state when the second characteristic value exceeds the fluctuation threshold. This effectively overcomes the technical defects of existing technologies, such as underreporting at low loads and false alarms at high loads due to the inability of fixed thresholds to adapt to changes in operating conditions, significantly improving the accuracy and reliability of surge detection across the entire operating condition range.

Description

Technical Field

[0001] This application relates to the field of air conditioning, and more particularly to a method, apparatus, air conditioning unit and storage medium for detecting compressor surge. Background Technology

[0002] Compressors are widely used in refrigeration and air conditioning systems, and their stable operation is crucial for ensuring equipment efficiency and lifespan. Surge is a common unstable operating phenomenon in centrifugal compressors, screw compressors, and similar types of compressors under low-flow conditions. When surge occurs, the airflow inside the compressor experiences periodic backflow and reattachment, causing drastic fluctuations in parameters such as discharge pressure, motor current, and power, and resulting in periodic abnormal noises and vibrations from the unit. Long-term or frequent surge not only significantly reduces system operating efficiency but also causes fatigue damage to critical components such as compressor blades, diffusers, and bearings, and may even trigger sudden equipment failures, increasing maintenance frequency and operating costs.

[0003] Currently, surge detection methods widely used in engineering practice are mainly based on fixed thresholds for single physical quantities. For example, by monitoring the instantaneous amplitude of compressor motor current or the high and low limits of exhaust pressure, surge is determined when the collected value exceeds a preset threshold. However, the actual operating conditions of compressors (such as load rate, ambient temperature, and cooling water temperature) often change significantly. Under low-load conditions, the current fluctuation during normal operation is relatively small, and fixed thresholds can easily lead to "missed detections"; while under high-load conditions, the normal current fluctuation range is larger, and fixed thresholds can easily lead to "false alarms". This problem of detection reliability and accuracy caused by changes in operating conditions has long troubled those skilled in the art. Summary of the Invention

[0004] This application provides a compressor surge detection method, device, air conditioning unit, and storage medium to solve the technical problem of low surge detection accuracy in the prior art.

[0005] In a first aspect, this application provides a method for detecting compressor surge, the method comprising: Obtain compressor operating parameter data; A sliding window mechanism based on a preset time window is used to process the operating parameter data within the current time window to obtain a first characteristic value characterizing the stable operating level of the compressor within the current time window, and a second characteristic value characterizing the intensity of the operating fluctuation of the compressor within the current time window. Based on the first feature value, determine the fluctuation threshold corresponding to the current time window; If the second characteristic value exceeds the fluctuation threshold, the compressor is determined to be in a surge state.

[0006] In one possible implementation, the first feature value is the average or median of the running parameter data within the current time window; The second characteristic value is the standard deviation or variance of the operating parameter data within the current time window.

[0007] In one possible implementation, determining the fluctuation threshold corresponding to the current time window based on the first feature value includes: Substituting the first feature value into the preset formula, the fluctuation threshold corresponding to the current time window is obtained; The preset formula is as follows:

[0008] In the formula, For fluctuation threshold, A is the first eigenvalue, and A and B are preset coefficients.

[0009] In one possible implementation, the method further includes: After the compressor stops due to surge protection, acquire the historical operating parameter data of the compressor within a set time period before the shutdown. The sliding window mechanism based on the preset time window is used to extract multiple historical time windows from the set time period before the shutdown. The historical operating parameter data within each historical time window are processed to obtain a third characteristic value characterizing the stable operating level of the compressor within each historical time window, and a fourth characteristic value characterizing the intensity of the operating fluctuation of the compressor within each historical time window. Based on multiple sets of the third and fourth eigenvalues, a univariate linear regression analysis was performed to obtain the following relationship: In the formula, Represents the fourth eigenvalue. Indicates the third eigenvalue; According to the relational formula and Update the preset coefficients A and B.

[0010] In one possible implementation, the method further includes: If the compressor is determined to be in a surge state, the current unit load rate and current water temperature of the unit where the compressor is located are obtained; If the current unit load rate exceeds the preset load threshold and the current water temperature does not reach the target water temperature, a surge mitigation control strategy is executed.

[0011] In one possible implementation, the surge mitigation control strategy includes: In the case that the unit is a multi-compressor unit, at least one compressor in the unit that is in standby mode is forcibly started; When the unit is a single-compressor unit and the compressor is operating in heating mode, the target water temperature is reduced. When the unit is a single-compressor unit and the compressor is operating in cooling mode, the target water temperature is increased.

[0012] In one possible implementation, the method further includes: The compressor's operating parameter data is monitored for directional changes within a preset historical period using a preset detection cycle; wherein, a single directional change is defined as the operating parameter data changing from a continuous upward trend to a continuous downward trend, or from a continuous downward trend to a continuous upward trend; the preset historical period is a continuous duration preceding the current moment. If the number of directional changes exceeds a preset threshold, the compressor is determined to be in an extreme surge state, and a compressor shutdown protection command is executed.

[0013] Secondly, this application provides a compressor surge detection device, the device comprising: The parameter acquisition module is used to acquire the compressor's operating parameter data; The data processing module is used to process the operating parameter data within the current time window using a sliding window mechanism based on a preset time window, to obtain a first characteristic value characterizing the stable operating level of the compressor within the current time window, and a second characteristic value characterizing the intensity of the operating fluctuation of the compressor within the current time window. The threshold determination module is used to determine the fluctuation threshold corresponding to the current time window based on the first feature value; The surge detection module is used to determine that the compressor is in a surge state when the second characteristic value exceeds the fluctuation threshold.

[0014] Thirdly, this application provides an air conditioning unit, comprising: compressor; Sensors installed on the compressor are used to collect operating parameter data of the compressor; and A processor and a memory, the processor being configured to execute a compressor surge detection program stored in the memory to implement the compressor surge detection method described in any one of the first aspects.

[0015] Fourthly, this application provides a storage medium storing one or more programs that can be executed by one or more processors to implement the compressor surge detection method described in any one aspect.

[0016] The technical solutions provided in this application have the following advantages compared with the prior art: The technical solution provided in this application first acquires the compressor's operating parameter data and processes the data within the current time window using a sliding window mechanism based on a preset time window. This yields a first characteristic value representing the compressor's stable operating level within the current time window and a second characteristic value representing the intensity of operating fluctuations. Since the sliding window mechanism continuously updates the data set within the window, the first and second characteristic values ​​always reflect the compressor's most recent operating state, thus achieving dynamic tracking of the compressor's operating characteristics. Secondly, this application determines the fluctuation threshold corresponding to the current time window based on the first characteristic value. Because the first characteristic value quantifies the compressor's stable operating level under the current operating conditions, its value automatically rises and falls with changes in operating parameters such as load and environment. Therefore, the fluctuation threshold determined based on this value will also adaptively adjust with changes in operating conditions. Finally, this application determines that the compressor is in a surge state when the second characteristic value exceeds the fluctuation threshold. Since the fluctuation threshold has been adaptively adjusted according to the current operating conditions, this judgment condition eliminates the influence of changes in operating conditions on the judgment benchmark. In summary, the technical solution of this application can effectively overcome the technical defects of the prior art, which are caused by the inability of fixed thresholds to adapt to changes in operating conditions, resulting in missed alarms under low loads and false alarms under high loads. It significantly improves the accuracy and reliability of surge detection across the entire operating range, providing a more accurate basis for subsequent surge control. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0020] Figure 1 A flowchart illustrating an embodiment of a compressor surge detection method provided in this application; Figure 2A flowchart illustrating an embodiment of another compressor surge detection method provided in this application; Figure 3 A flowchart illustrating another embodiment of a compressor surge detection method provided in this application; Figure 4 A flowchart illustrating another embodiment of a compressor surge detection method provided in this application; Figure 5 A block diagram illustrating an embodiment of a compressor surge detection device provided in this application; Figure 6 This is a schematic diagram of the structure of an air conditioning unit provided in an embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0023] To address the technical problem of low surge detection accuracy in existing technologies, this application provides a control method, device, power equipment, and storage medium for power equipment. This effectively overcomes the technical defects in existing technologies, such as underreporting under low load and false alarms under high load, caused by fixed thresholds failing to adapt to changes in operating conditions. It significantly improves the accuracy and reliability of surge detection across the entire operating range, providing a more accurate basis for subsequent surge control.

[0024] Figure 1 This is a flowchart illustrating an embodiment of a compressor surge detection method provided in this application. Figure 1 As shown, the method includes the following steps: Step 101: Obtain the compressor's operating parameter data.

[0025] The operating parameter data refers to physical quantities that reflect the real-time operating status of the compressor. In this embodiment, the operating parameter data includes at least one of parameters such as motor current, motor power, or exhaust pressure. These data are continuously collected by sensors (such as current transformers and pressure sensors) installed on the compressor at a certain sampling frequency.

[0026] When a compressor experiences surge, its essential characteristic is the periodic separation, backflow, and reattachment of airflow between the impeller and the diffuser. This fluid instability directly causes severe fluctuations in the compressor load, manifesting in the following ways: Large-amplitude, high-frequency alternating positive and negative oscillations in motor current and input power: Surge causes periodic abrupt changes in the compressor motor's output torque, resulting in large-amplitude, high-frequency alternating positive and negative fluctuations in motor current and input power. During normal operation, the current waveform is relatively flat; during surge, the current waveform exhibits violent oscillations resembling a sine wave or sawtooth wave. Therefore, the intensity of current / power fluctuations is the most direct and fastest-responding electrical parameter characterizing the degree of surge.

[0027] Periodic drops and rises in exhaust pressure: When surge occurs, gas backflow causes a momentary drop in exhaust pressure, followed by pressure recovery, forming periodic pressure pulsations. The frequency and amplitude of exhaust pressure fluctuations are positively correlated with the severity of surge.

[0028] Therefore, to detect whether a compressor is experiencing surge, these operating parameters (current, power, and discharge pressure) that have an instantaneous response to surge can be used as detection sources. By continuously acquiring their time-series data, a raw basis can be provided for subsequent adaptive judgment based on statistical characteristics. Furthermore, compared to slowly changing parameters such as temperature or parameters such as vibration that require additional hardware, the parameters selected in this step do not require additional sensors, are low-cost, and have a fast response. They can capture load fluctuations caused by surge within milliseconds, thereby achieving early warning and rapid determination of surge.

[0029] Step 102: Using a sliding window mechanism based on a preset time window, process the operating parameter data within the current time window to obtain a first characteristic value characterizing the stable operating level of the compressor within the current time window, and a second characteristic value characterizing the intensity of the operating fluctuation of the compressor within the current time window.

[0030] A preset time window is a fixed duration (e.g., 5 minutes) used to define the range of data used in each calculation. A sliding window mechanism is a data processing method where the window slides forward once every fixed step, always retaining data from the most recent time window. By using a sliding window mechanism based on a preset time window, it can be ensured that the data used in each calculation cycle represents the compressor's performance under the current operating conditions.

[0031] The first characteristic value is used to quantify the "central trend" or "baseline level" of the compressor within the current time window. In one embodiment, the first characteristic value is the average or median of the operating parameter data within the current time window, which directly reflects the stable operating level of the compressor under the current operating conditions. For example, the average current is higher under high load conditions and lower under low load conditions; therefore, the first characteristic value is essentially a digital representation of the current operating conditions.

[0032] The second eigenvalue is used to quantify the "dispersion" or "intensity of fluctuation" of the data around the central trend. In one embodiment, the second eigenvalue is the standard deviation or variance of the operating parameter data within the current time window, which reflects the smoothness of the compressor's actual operation under the current conditions. For example, under the same operating conditions, the fluctuation intensity is lower during normal operation and increases sharply during surge.

[0033] By introducing a sliding window mechanism to calculate the first and second eigenvalues, this step supports the compressor's "operating condition adaptability": the calculated first eigenvalue differs under different operating conditions, and consequently, the dynamic threshold in subsequent step 103 also changes accordingly. This ensures that the surge judgment standard always matches the current operating condition, thus avoiding the inherent defects of the fixed threshold method, which suffers from missed alarms under high loads and false alarms under low loads. The continuous sliding of the sliding window ensures smooth updates of the eigenvalues, enabling real-time perception of operating condition changes and laying a statistical foundation for subsequent adaptive threshold judgment and surge identification.

[0034] Step 103: Determine the fluctuation threshold corresponding to the current time window based on the first feature value.

[0035] The fluctuation threshold is a dynamically calculated threshold value used to determine whether the second characteristic value is too large. This threshold is not a fixed constant, but a value that varies linearly or non-linearly with the first characteristic value (i.e., the current load level of the compressor).

[0036] Therefore, in this embodiment, the fluctuation threshold is adaptively determined based on the current operating conditions, ensuring that the surge judgment standard always matches the real-time load level of the compressor. For example, when the compressor operates under high load conditions, the current is inherently larger, and the allowable standard deviation threshold is correspondingly increased, avoiding false alarms due to normal large current fluctuations; when the compressor operates under low load conditions, the threshold automatically decreases, ensuring sensitivity to surge. This is fundamentally different from the traditional fixed threshold method.

[0037] In one embodiment, the first feature value is substituted into a preset formula to obtain the fluctuation threshold corresponding to the current time window; wherein, the preset formula is: .

[0038] In the above formula, For fluctuation threshold, Let A be the first characteristic value, and let B be the preset coefficients. Among them, preset coefficient A reflects the sensitivity of the fluctuation intensity to the stability level (e.g., the allowable proportional coefficient of current fluctuation), and preset coefficient B reflects the absolute amount of fluctuation allowed by the base.

[0039] Step 104: If the second characteristic value exceeds the fluctuation threshold, the compressor is determined to be in a surge state.

[0040] Surge is an unstable operating condition of a compressor, typically characterized by periodic backflow of air, violent pressure fluctuations, and large-amplitude current oscillations.

[0041] In step 104, the second characteristic value obtained in step 102 is compared with the fluctuation threshold obtained in step 103. If the second characteristic value is less than or equal to the fluctuation threshold, the compressor is considered to be operating smoothly without surge. In this case, the process can return to step 101 to continue monitoring.

[0042] If the second characteristic value is greater than the fluctuation threshold, the compressor is determined to be in a surge state. In this case, a surge warning signal or surge status flag can be triggered.

[0043] The technical solution provided in this application first acquires the compressor's operating parameter data and processes the data within the current time window using a sliding window mechanism based on a preset time window. This yields a first characteristic value representing the compressor's stable operating level within the current time window and a second characteristic value representing the intensity of operating fluctuations. Since the sliding window mechanism continuously updates the data set within the window, the first and second characteristic values ​​always reflect the compressor's most recent operating state, thus achieving dynamic tracking of the compressor's operating characteristics. Secondly, this application determines the fluctuation threshold corresponding to the current time window based on the first characteristic value. Because the first characteristic value quantifies the compressor's stable operating level under the current operating conditions, its value automatically rises and falls with changes in operating parameters such as load and environment. Therefore, the fluctuation threshold determined based on this value will also adaptively adjust with changes in operating conditions. Finally, this application determines that the compressor is in a surge state when the second characteristic value exceeds the fluctuation threshold. Since the fluctuation threshold has been adaptively adjusted according to the current operating conditions, this judgment condition eliminates the influence of changes in operating conditions on the judgment benchmark. In summary, the technical solution of this application can effectively overcome the technical defects of the prior art, which are caused by the inability of fixed thresholds to adapt to changes in operating conditions, resulting in missed alarms under low loads and false alarms under high loads. It significantly improves the accuracy and reliability of surge detection across the entire operating range, providing a more accurate basis for subsequent surge control.

[0044] Figure 2 A flowchart illustrating another embodiment of a compressor surge detection method provided in this application. Figure 2As shown, the method includes the following steps: Step 201: Obtain the compressor's operating parameter data.

[0045] Step 202: Using a sliding window mechanism based on a preset time window, the operating parameter data within the current time window are processed to obtain a first characteristic value characterizing the stable operating level of the compressor within the current time window, and a second characteristic value characterizing the intensity of the operating fluctuation of the compressor within the current time window.

[0046] Step 203: Determine the fluctuation threshold corresponding to the current time window based on the first feature value.

[0047] Step 204: If the second characteristic value exceeds the fluctuation threshold, the compressor is determined to be in a surge state.

[0048] For a detailed description of steps 201 to 204, please refer to the relevant descriptions in the above embodiments, which will not be repeated here.

[0049] Step 205: If the compressor is determined to be in a surge state, obtain the current unit load rate and current water temperature of the unit where the compressor is located.

[0050] Step 206: If the current unit load rate exceeds the preset load threshold and the current water temperature has not reached the target water temperature, execute the surge mitigation control strategy.

[0051] After the compressor is determined to be in a surge state, this embodiment of the application continues to explore the cause of the compressor surge and takes the most appropriate intervention measures.

[0052] Specifically, the fluid dynamics of surge is that the compressor's operating point falls into the unstable region of the characteristic curve. In engineering, the most common cause of this situation is not a failure of the compressor itself, but a mismatch between the system capacity of the entire unit and the load demand: there is a significant gap between the target water temperature set by the user (seeking low temperature when cooling and high temperature when heating) and the actual cooling and heating capacity that the unit can provide, causing the compressor to operate at near full load for a long time, thereby inducing airflow backflow and pressure pulsation.

[0053] Accordingly, step 205 obtains two parameters that characterize the supply and demand imbalance of the unit: one is the current load rate of the unit where the compressor is located, which reflects the degree of utilization of the compressor's output capacity relative to its rated point, usually expressed as a percentage; the other is the current water temperature of the unit, which is compared with the target water temperature set by the user. If the load rate exceeds a preset threshold (e.g., 90%), while the water temperature has not yet reached the target value (e.g., the water temperature is still higher than the set point in cooling mode), it indicates that the unit is in a critical state of "supply falling short of demand", and surge is very likely to be triggered as a result.

[0054] Step 206 executes the corresponding surge mitigation control strategy based on the diagnostic results. The core logic is to either enhance the system's capacity or reduce user demand. For units with multiple compressors in parallel, at least one standby compressor that was previously in standby mode can be forcibly started. With the standby compressor connected, the total refrigerant circulation increases, improving the unit's cooling / heating capacity. The compressor that was originally running at full load can share some of the load, and its operating point automatically shifts to the right, exiting the unstable region. For single-unit systems with only one compressor, pressure cannot be shared through "reinforcements." Therefore, the approach is demand-side-based: adjusting the target water temperature appropriately based on the current operating mode—lowering the target water temperature during heating and raising it during cooling. While this adjustment temporarily sacrifices some comfort, it quickly reduces the actual load rate of the compressor, pulling it out of the surge zone and preventing mechanical damage or triggering extreme shutdown protection due to continuous surge. Accordingly, in one embodiment, the surge mitigation control strategy includes: when the unit is a multi-compressor unit, forcibly starting at least one compressor in the unit that is in standby mode; when the unit is a single-compressor unit and the compressor is operating in heating mode, lowering the target water temperature; and when the unit is a single-compressor unit and the compressor is operating in cooling mode, raising the target water temperature.

[0055] Taking current as an example of operating parameter data, see [link / reference]. Figure 3 For example, the compressor's current data is acquired, a time window W is set, and a sliding window mechanism based on this time window is used to process the current data within the current time window. This yields the average current value avg_c, representing the compressor's stable operating level within the current time window, and the standard deviation std_c, representing the intensity of the compressor's operating fluctuations within the current time window. If std_c is greater than the fluctuation threshold max_std, the compressor is determined to be in a surge state. Further, it is determined whether it is a multi-system unit. If it is a multi-system unit, another system is started to increase the cooling or heating capacity. If it is a single unit, the target water temperature is adjusted according to the current operating mode; the target water temperature is lowered during heating and raised during cooling.

[0056] Figure 2 The process shown is in Figure 1 Based on the illustrated process, through the combined execution of steps 205-206, the surge control strategy evolves from the traditional "immediate shutdown upon detection of a problem" to "diagnosing the cause and mitigating it in stages." This staged, adaptive control logic not only ensures the safety of the compressor under abnormal operating conditions but also maximizes the uninterrupted operation capability of the unit.

[0057] Figure 4 A flowchart illustrating another embodiment of a compressor surge detection method provided in this application. Figure 4 As shown, the method includes the following steps: Step 401: Monitor the number of times the operating parameter data of the compressor changes direction within a preset historical period using a preset detection cycle; wherein, a single directional change is defined as the operating parameter data changing from a continuous upward trend to a continuous downward trend, or from a continuous downward trend to a continuous upward trend; the preset historical period is a continuous duration preceding the current moment.

[0058] Step 402: If the number of directional changes exceeds a preset threshold, the compressor is determined to be in an extreme surge state, and a compressor shutdown protection command is executed.

[0059] First, it should be noted that steps 401 and 402 establish an extreme surge monitoring and protection mechanism independent of conventional surge detection (steps 101-104). Under this monitoring mechanism, the time series of compressor operating parameters is continuously scanned at a fixed detection cycle to directly capture the worst physical manifestation of surge: drastic alternating changes in parameter direction.

[0060] Specifically, in step 401, the number of directional changes in the compressor's operating parameter data within a preset historical time period is monitored using a preset detection cycle. The preset detection cycle refers to triggering a detection action every fixed time interval (e.g., 5 seconds). The preset historical time period is a fixed-length backtracking window (e.g., 1 minute prior to the current moment), and each detection re-captures a data segment of a fixed duration from the current moment backwards.

[0061] "Directional change" is a key characteristic of extreme surge: when surge reaches a severe stage, the periodic oscillations caused by airflow backflow and recovery cause the compressor's current, pressure, and other parameters to swing rapidly and violently back and forth. Each change from rising to falling or from falling to rising is counted as one directional change. This definition differs from the usual "zero crossing" or "extreme point"; it emphasizes the reversal of the trend, eliminating minor noise interference and capturing only significant changes in the direction of fluctuations. For example, a current sequence changing from 5A→6A→7A (continuously rising) to 6A→5A (turning to falling) is counted as one change; if it then changes again from 5A→6A (turning to rising), it is counted as another change. The statistically obtained value is the "number of directional changes".

[0062] The core difference between this step and the conventional surge detection step 102 is that step 102 calculates the "fluctuation intensity" (standard deviation), which reflects the overall dispersion; while step 401 counts the "direction change frequency", which directly quantifies the "severity" of parameter oscillation. Multiple consecutive directional changes mean that the system has entered a severe limit cycle oscillation, and is only one step away from mechanical damage.

[0063] In step 402, if the number of directional changes exceeds a preset threshold, the compressor is determined to be in an extreme surge state, and a compressor shutdown protection command is executed.

[0064] The "preset number threshold" is a pre-calibrated fixed integer (e.g., 5, 10, or 15 times), representing the maximum number of directional changes that can be tolerated per unit time (e.g., within 1 minute). This threshold does not change with operating conditions because when surge develops to an extreme degree, the frequency and amplitude of the oscillation will significantly exceed the range allowed by normal operation, regardless of the current load.

[0065] "Extreme surge condition" refers to a surge that is so severe that the mitigation and control strategies in steps 205-206 above (starting the standby compressor or adjusting the target water temperature) cannot eliminate it in a short time. If the compressor is not stopped immediately, core components such as compressor blades and bearings will face the risk of fatigue fracture or excessive shaft displacement.

[0066] Then, the number of directional changes counted in step 401 is compared with a preset threshold. If the number does not exceed the threshold, it is considered that the current state is not in an extreme surge state, and the monitoring continues according to the original detection cycle until the next monitoring. If the number exceeds the threshold, a shutdown protection command is immediately output, directly cutting off the compressor power supply or reducing the frequency to zero through the frequency converter.

[0067] The technical solutions provided in steps 401 and 402 can reliably capture extreme forms of surge. They complement the aforementioned sliding window adaptive detection. The former is responsible for sensitive early warning and graded mitigation under all operating conditions, while the latter is responsible for backup protection. The two operate in parallel and together construct a surge protection system that avoids frequent accidental shutdowns and ensures absolute safety.

[0068] Step 403: After the compressor stops due to surge protection, acquire the historical operating parameter data of the compressor within the set time period before the shutdown.

[0069] Step 404: Using a sliding window mechanism based on a preset time window, extract multiple historical time windows from the time period set before shutdown; process the historical operating parameter data in each historical time window to obtain a third characteristic value characterizing the stable operating level of the compressor in each historical time window, and a fourth characteristic value characterizing the intensity of the operating fluctuation of the compressor in each historical time window.

[0070] Step 405: Perform univariate linear regression analysis based on multiple sets of third and fourth eigenvalues ​​to obtain the following relationship: In the formula, Represents the fourth eigenvalue. This represents the third eigenvalue.

[0071] Step 406: Based on the relational expression and Update preset coefficients A and B.

[0072] The following provides a unified explanation of steps 403 to 406: Steps 403 to 406 together constitute the surge detection parameter self-learning mechanism, which aims to use the real operating data contained in each extreme surge event (i.e. the event that triggers the shutdown protection in step 402) to reverse correct the linear formula coefficients A and B used in step 103 to calculate the dynamic fluctuation threshold, so that the detection algorithm becomes more and more accurate and more and more adapted to the individual characteristics of specific units in practical applications.

[0073] Specifically, the actual operating conditions (such as pipe resistance, refrigerant charge, and ambient temperature trends) vary for each compressor and each installation site. Fixed, preset A and B coefficients may not perfectly match the surge boundary of a specific unit. When extreme surge occurs and leads to shutdown protection, it means that the actual operating data during this surge process has exceeded the current judgment threshold. These data are "real verification samples," which can be used to refit more accurate A and B coefficients, thereby improving the accuracy of future detections.

[0074] Accordingly, in step 403, after the compressor stops due to surge protection, historical operating parameter data of the compressor within a set time period before shutdown is acquired. This "set time period before shutdown" is a fixed time interval, such as 10 or 15 minutes before shutdown. This duration needs to be long enough to encompass the complete evolution of surge from its inception to its extreme, but not too long to avoid incorporating data from normal, stable operation, which could interfere with the fitting accuracy. The historical operating parameter data of the compressor within the set time period before shutdown represents actual surge evolution data that has already occurred, including complete information on the pre-surge stabilization period, surge warning period, surge intensification period, and finally, shutdown.

[0075] In step 404, a sliding window mechanism based on a preset time window is adopted to extract multiple historical time windows from the time period set before shutdown; the historical operating parameter data in each historical time window are processed to obtain a third characteristic value characterizing the stable operating level of the compressor in each historical time window, and a fourth characteristic value characterizing the intensity of the operating fluctuation of the compressor in each historical time window.

[0076] The "preset time window" here has the same length as the window in step 102. This is to ensure that the statistical features used for self-learning are consistent with the statistical features used during detection on the same time scale. The historical time window is a series of sub-intervals divided from the large interval of the time period set before shutdown using a sliding window method. For example, if the window length is 5 minutes 10 minutes before shutdown, two non-overlapping windows can be extracted: the [10-5] minute segment and the [5-0] minute segment; if a sliding step size of 1 minute is used, more overlapping windows can be extracted.

[0077] The third eigenvalue corresponds to a similar statistic (mean or median) of the first eigenvalue, and the fourth eigenvalue corresponds to a similar statistic (standard deviation or variance) of the second eigenvalue.

[0078] In step 405, a univariate linear regression analysis is performed based on multiple sets of third and fourth eigenvalues ​​to obtain the following relationship: .

[0079] "Univariate linear regression" is a classic statistical modeling method used to find the best linear fit between two variables. Here, we consider the third eigenvalue as the independent variable (representing the load level) and the fourth eigenvalue as the dependent variable (representing the actual fluctuation intensity). Through regression analysis, the optimal slope can be calculated. and intercept , making the straight line It can approximate the distribution trend of the data points as closely as possible. This regression line reflects the true proportional relationship between the actual fluctuation intensity and the load level when surge occurs on this unit.

[0080] Finally, in step 406, according to the relation... and Update the preset coefficients A and B.

[0081] In one embodiment, the coefficients obtained from the new regression are weighted and averaged or moved averaged with the original coefficients. For example, the updated preset coefficient A = (A + A0) / 2, and the updated coefficient B = (B + B0) / 2. The purpose of this is to avoid over-adjustment caused by a single extreme event, while retaining historical learning results, so that the coefficient changes are smooth and stable.

[0082] Subsequently, when the compressor restarts and runs again, step 103 will use the updated A and B to calculate the dynamic fluctuation threshold. Since the new coefficients are corrected based on the unit's own surge history data, their detection accuracy and adaptability will be significantly improved.

[0083] The technical solutions provided in steps 403 to 406 transform each extreme surge shutdown into a learning opportunity, enabling the detection algorithm to continuously evolve based on actual operational data. This self-learning mechanism overcomes the limitations of traditional fixed thresholds or one-time calibrations, allowing the surge detection system to maintain optimal performance throughout its entire lifecycle and significantly reducing false alarm and false negative rates. Furthermore, this mechanism operates entirely autonomously without relying on external calibration equipment or manual intervention, demonstrating high engineering practical value.

[0084] Figure 5 This is a block diagram illustrating an embodiment of a compressor surge detection device provided in this application. Figure 5 As shown, the device includes: The parameter acquisition module 51 is used to acquire the operating parameter data of the compressor; The data processing module 52 is used to process the operating parameter data within the current time window using a sliding window mechanism based on a preset time window, to obtain a first characteristic value characterizing the stable operating level of the compressor within the current time window, and a second characteristic value characterizing the intensity of the operating fluctuation of the compressor within the current time window. The threshold determination module 53 is used to determine the fluctuation threshold corresponding to the current time window based on the first feature value; The surge detection module 54 is used to determine that the compressor is in a surge state when the second characteristic value exceeds the fluctuation threshold.

[0085] In one possible implementation, the first feature value is the average or median of the running parameter data within the current time window; The second characteristic value is the standard deviation or variance of the operating parameter data within the current time window.

[0086] In one possible implementation, the threshold determination module 53 is specifically used for: Substituting the first feature value into the preset formula, the fluctuation threshold corresponding to the current time window is obtained; The preset formula is as follows:

[0087] In the formula, For fluctuation threshold, A is the first eigenvalue, and A and B are preset coefficients.

[0088] In one possible implementation, the device further includes: The optimization and update module is used to obtain historical operating parameter data of the compressor within a set time period before the shutdown after the compressor surge protection shutdown; The sliding window mechanism based on the preset time window is used to extract multiple historical time windows from the set time period before the shutdown. The historical operating parameter data within each historical time window are processed to obtain a third characteristic value characterizing the stable operating level of the compressor within each historical time window, and a fourth characteristic value characterizing the intensity of the operating fluctuation of the compressor within each historical time window. Based on multiple sets of the third and fourth eigenvalues, a univariate linear regression analysis was performed to obtain the following relationship: In the formula, Represents the fourth eigenvalue. Indicates the third eigenvalue; According to the relational formula and Update the preset coefficients A and B.

[0089] In one possible implementation, the device further includes: The surge root cause analysis module is used to obtain the current unit load rate and current water temperature of the unit where the compressor is located when it is determined that the compressor is in a surge state. The surge mitigation module is used to execute a surge mitigation control strategy when the current unit load rate exceeds a preset load threshold and the current water temperature does not reach the target water temperature.

[0090] In one possible implementation, the surge mitigation module executes a surge mitigation control strategy, including: In the case that the unit is a multi-compressor unit, at least one compressor in the unit that is in standby mode is forcibly started; When the unit is a single-compressor unit and the compressor is operating in heating mode, the target water temperature is reduced. When the unit is a single-compressor unit and the compressor is operating in cooling mode, the target water temperature is increased.

[0091] In one possible implementation, the device further includes: An extreme surge monitoring module is used to monitor the number of directional changes in the compressor's operating parameter data within a preset historical period at a preset detection cycle; wherein, a single directional change is defined as the operating parameter data changing from a continuous upward trend to a continuous downward trend, or from a continuous downward trend to a continuous upward trend; the preset historical period is a continuous duration preceding the current moment; If the number of directional changes exceeds a preset threshold, the compressor is determined to be in an extreme surge state, and a compressor shutdown protection command is executed.

[0092] like Figure 6 As shown, this application embodiment provides an air conditioning unit, including a compressor 111, a sensor 112 disposed on the compressor, a processor 113, a memory 114, and a communication bus 115, wherein the processor 113 and the memory 114 communicate with each other through the communication bus 115. Memory 114 is used to store computer programs; In one embodiment of this application, when the processor 113 executes the program stored in the memory 114, it implements the compressor surge detection method provided in any of the foregoing method embodiments, including: Obtain compressor operating parameter data; A sliding window mechanism based on a preset time window is used to process the operating parameter data within the current time window to obtain a first characteristic value characterizing the stable operating level of the compressor within the current time window, and a second characteristic value characterizing the intensity of the operating fluctuation of the compressor within the current time window. Based on the first feature value, determine the fluctuation threshold corresponding to the current time window; If the second characteristic value exceeds the fluctuation threshold, the compressor is determined to be in a surge state.

[0093] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the compressor surge detection method provided in any of the foregoing method embodiments.

[0094] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0096] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0097] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for detecting compressor surge, characterized in that, The method includes: Obtain compressor operating parameter data; A sliding window mechanism based on a preset time window is used to process the operating parameter data within the current time window to obtain a first characteristic value characterizing the stable operating level of the compressor within the current time window, and a second characteristic value characterizing the intensity of the operating fluctuation of the compressor within the current time window. Based on the first feature value, determine the fluctuation threshold corresponding to the current time window; If the second characteristic value exceeds the fluctuation threshold, the compressor is determined to be in a surge state.

2. The method according to claim 1, characterized in that, The first characteristic value is the average or median of the running parameter data within the current time window; The second characteristic value is the standard deviation or variance of the operating parameter data within the current time window.

3. The method according to claim 1, characterized in that, The step of determining the fluctuation threshold corresponding to the current time window based on the first feature value includes: Substituting the first feature value into the preset formula, the fluctuation threshold corresponding to the current time window is obtained; The preset formula is as follows: In the formula, For fluctuation threshold, A is the first eigenvalue, and A and B are preset coefficients.

4. The method according to claim 3, characterized in that, The method further includes: After the compressor stops due to surge protection, acquire the historical operating parameter data of the compressor within a set time period before the shutdown. The sliding window mechanism based on the preset time window is used to extract multiple historical time windows from the set time period before the shutdown. The historical operating parameter data within each historical time window are processed to obtain a third characteristic value characterizing the stable operating level of the compressor within each historical time window, and a fourth characteristic value characterizing the intensity of the operating fluctuation of the compressor within each historical time window. Based on multiple sets of the third and fourth eigenvalues, a univariate linear regression analysis was performed to obtain the following relationship: In the formula, Represents the fourth eigenvalue. Indicates the third eigenvalue; According to the relational formula and Update the preset coefficients A and B.

5. The method according to claim 1, characterized in that, The method further includes: If the compressor is determined to be in a surge state, the current unit load rate and current water temperature of the unit where the compressor is located are obtained; If the current unit load rate exceeds the preset load threshold and the current water temperature does not reach the target water temperature, a surge mitigation control strategy is executed.

6. The method according to claim 5, characterized in that, The implementation of the surge mitigation control strategy includes: In the case that the unit is a multi-compressor unit, at least one compressor in the unit that is in standby mode is forcibly started; When the unit is a single-compressor unit and the compressor is operating in heating mode, the target water temperature is reduced. When the unit is a single-compressor unit and the compressor is operating in cooling mode, the target water temperature is increased.

7. The method according to claim 1, characterized in that, The method further includes: The compressor's operating parameter data is monitored for directional changes within a preset historical period using a preset detection cycle; wherein, a single directional change is defined as the operating parameter data changing from a continuous upward trend to a continuous downward trend, or from a continuous downward trend to a continuous upward trend; the preset historical period is a continuous duration preceding the current moment. If the number of directional changes exceeds a preset threshold, the compressor is determined to be in an extreme surge state, and a compressor shutdown protection command is executed.

8. A compressor surge detection device, characterized in that, The device includes: The parameter acquisition module is used to acquire the compressor's operating parameter data; The data processing module is used to process the operating parameter data within the current time window using a sliding window mechanism based on a preset time window, to obtain a first characteristic value characterizing the stable operating level of the compressor within the current time window, and a second characteristic value characterizing the intensity of the operating fluctuation of the compressor within the current time window. The threshold determination module is used to determine the fluctuation threshold corresponding to the current time window based on the first feature value; The surge detection module is used to determine that the compressor is in a surge state when the second characteristic value exceeds the fluctuation threshold.

9. An air conditioning unit, characterized in that, include: compressor; Sensors installed on the compressor are used to collect the compressor's operating parameter data; as well as A processor and a memory, the processor being configured to execute a compressor surge detection program stored in the memory to implement the compressor surge detection method according to any one of claims 1-7.

10. A storage medium, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the compressor surge detection method according to any one of claims 1-7.

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