An intelligent fault monitoring system for UPS

Abstract

This invention relates to the field of power equipment monitoring technology, and in particular to an intelligent fault monitoring system for UPS. The invention utilizes the collaborative operation of a sensitive feature analysis module, a normal state analysis module, and a call analysis module. Based on the sensitive feature analysis module, it performs real-time monitoring and analysis of the power supply characteristics of each power supply unit, analyzes the sensitivity of each power supply unit to various power supply characteristics, and determines the associated sensitive power supply characteristics of each power supply unit. The normal state analysis module analyzes the sensitive power supply characteristics under normal conditions, constructs time-domain variation curves, selects anti-disturbance curve segments and verifies their effectiveness, and determines disturbance constraint limits. The call analysis module determines whether there are anomalies in the power supply unit based on changes in sensitive power supply characteristics and adjusts the acquisition frequency, thereby reducing the impact of power supply characteristic deviations caused by electrical coupling on monitoring accuracy, thus improving the accuracy and reliability of monitoring.

Description

Technical Field

[0001] This invention relates to the field of power equipment monitoring technology, and in particular to an intelligent fault monitoring system for UPS. Background Technology

[0002] With the rapid development of information technology, data centers and critical infrastructure have increasingly higher requirements for the reliability and stability of power supply. As a core component ensuring the continuous operation of these critical devices during power outages, UPS systems are closely related to the operational efficiency, data security, and business continuity of data centers. With more and more intelligent devices connecting to UPS systems, the changing operating states and power demands of these devices place higher demands on UPS performance. Against this backdrop, UPS systems have become an indispensable part of data centers and critical infrastructure.

[0003] Chinese Patent Publication No. CN118232506A discloses an intelligent management method and system for UPS power supplies used in energy-saving vending machines. This relates to the field of intelligent UPS power supply management technology, including: acquiring UPS power supply operation data for the vending machine; establishing an intelligent monitoring model to monitor the UPS power supply status in real time, diagnose potential problems, and implement handling measures; dynamically optimizing the UPS power supply load mode and predicting power supply lifespan; storing the data in a database and setting up security protection measures. This invention collects UPS power supply operation data, analyzes and judges the UPS power supply operation status through an intelligent monitoring model, and implements handling measures. It eliminates potential UPS power supply faults in real time, preventing losses caused by UPS power supply failures. It effectively helps staff understand the UPS power supply's operating status, optimizes the UPS power supply load, predicts UPS power supply lifespan, reduces UPS power supply maintenance frequency, and improves operating efficiency.

[0004] However, the following problems still exist in the existing technology.

[0005] In practice, due to the electrical coupling between the power supply units in a UPS system, these units can influence each other during operation, leading to problems such as voltage fluctuations and current deviations. During fault monitoring, this mutual influence may cause the electrical parameters of the power supply units to deviate from the normal range, thus affecting the accuracy of fault monitoring and potentially leading to misjudgments, thereby reducing the accuracy and reliability of the monitoring. Summary of the Invention

[0006] Therefore, the present invention provides an intelligent fault monitoring system for UPS, which overcomes the problem that in the prior art, due to the electrical coupling between the power supply units in the UPS system, the power supply units will affect each other during actual operation, resulting in changes in electrical parameters and affecting the accuracy of fault monitoring.

[0007] To achieve the above objectives, the present invention provides an intelligent fault monitoring system for UPS, comprising,

[0008] The sensitive feature analysis module is used to record the power supply characteristics of several power supply units in the UPS power supply group, analyze the sensitivity of each power supply unit to each power supply characteristic based on the changes in each power supply characteristic of each power supply unit, and determine the sensitive power supply characteristics associated with each power supply unit.

[0009] The normal analysis module is connected to the sensitive feature analysis module. It is used to analyze the sensitive power supply characteristics of each power supply unit under normal conditions, construct the time-domain variation curve of the sensitive power supply characteristics within a predetermined time period, screen the anti-disturbance curve segment based on the peak fluctuation of the time-domain variation curve segment of each sensitive power supply characteristic, and verify the effectiveness of each anti-disturbance curve segment in order to determine the disturbance constraint limit.

[0010] The invocation analysis module, which is connected to both the sensitive feature analysis module and the normal parsing module, includes a first invocation unit and a second invocation unit.

[0011] The first calling unit is used to call the sensitive power supply features associated with the power supply unit, determine whether the corresponding sensitive power supply feature is within the disturbance constraint limit, adjust the acquisition frequency for the sensitive power supply feature to continuously acquire the corresponding sensitive power supply feature, and determine whether the power supply unit is abnormal based on the anomaly of the corresponding sensitive power supply feature.

[0012] The second calling unit is used to call the remaining non-sensitive power supply characteristics of the power supply unit, and to determine whether there is an abnormality in the power supply unit based on whether the non-sensitive power supply characteristics are within the standard threshold range;

[0013] The power supply characteristics include current, voltage, impedance, and output frequency.

[0014] Furthermore, the sensitive feature analysis module is used to analyze the sensitivity of each power supply unit to each power supply feature in response to changes in each power supply feature of each power supply unit, including...

[0015] Used to obtain the fluctuation amplitude of each power supply characteristic of each of the power supply units;

[0016] This is used to determine the fluctuation amplitude of each power supply characteristic as the sensitivity of the corresponding power supply characteristic.

[0017] Furthermore, the sensitive feature analysis module is used to determine the sensitive power supply features associated with each power supply unit, including:

[0018] If the sensitivity of the power supply feature is greater than or equal to the corresponding sensitivity threshold, then the power supply feature is determined to be a sensitive power supply feature associated with the power supply unit.

[0019] Furthermore, the normal analysis module is used to screen disturbance rejection curve segments based on the peak fluctuations of the time-domain variation curve segments of each sensitive power supply characteristic, including:

[0020] This is used to determine the peak fluctuation of the time-domain variation curve segment of each sensitive power supply characteristic. If the peak fluctuation is less than the predetermined peak fluctuation threshold, the time-domain variation curve segment of the sensitive power supply characteristic is selected as the anti-disturbance curve segment.

[0021] Among them, the peak fluctuation is the variance of the amplitude corresponding to each peak.

[0022] Furthermore, the normal analysis module is used to verify the effectiveness of each of the disturbance rejection curve segments, including:

[0023] Used to determine the ratio of the total duration of the anti-disturbance curve segment to the predetermined duration;

[0024] This is used to determine whether each of the anti-disturbance curve segments meets the anti-disturbance conditions. If it meets the anti-disturbance conditions, then each of the anti-disturbance curve segments is determined to be valid.

[0025] The anti-disturbance conditions include that the peak fluctuation is less than a predetermined peak fluctuation threshold, and the ratio of the total duration of the anti-disturbance curve segment to the predetermined duration is greater than a predetermined duration ratio threshold.

[0026] Furthermore, the normal analysis module is used to determine the disturbance constraint limits, including:

[0027] If each of the aforementioned anti-disturbance curve segments is determined to be valid, then the average peak value corresponding to each of the aforementioned anti-disturbance curve segments is determined;

[0028] This is used to determine the disturbance constraint limits based on the average peak value corresponding to each of the disturbance resistance curve segments.

[0029] Furthermore, the call analysis module is used to determine whether the corresponding sensitive power supply feature is within the disturbance constraint limit, and adjusting the sampling frequency for the sensitive power supply feature includes:

[0030] Used to determine the average peak value corresponding to each sensitive power supply characteristic;

[0031] If the average peak value is within the disturbance constraint limit, then adjust the sampling frequency for sensitive power supply characteristics.

[0032] Furthermore, the method of calling the analysis module to adjust the sampling frequency for sensitive power supply characteristics includes increasing the sampling frequency for sensitive power supply characteristics.

[0033] Furthermore, the invocation analysis module is used to determine whether there is an anomaly in the power supply unit based on the anomalies of corresponding sensitive power supply characteristics, including:

[0034] This is used to construct a time-domain variation curve of the sensitive power supply characteristics based on the collected sensitive power supply characteristics;

[0035] It is used to determine the abnormal peak based on the disturbance constraint limit, and to determine whether there is an abnormality in the power supply unit based on the proportion of the abnormal peak;

[0036] If the peak value exceeds the disturbance constraint limit, the peak is identified as an abnormal peak. If the proportion of abnormal peaks is greater than the predetermined proportion threshold, the power supply unit is determined to be abnormal.

[0037] Furthermore, the call analysis module is used to determine whether there is an anomaly in the power supply unit based on whether the non-sensitive power supply characteristics are within the standard threshold range, including:

[0038] Used to obtain non-sensitive power supply characteristics;

[0039] If the non-sensitive power supply characteristics are not within the corresponding standard threshold range, the power supply unit is determined to be abnormal.

[0040] Compared with existing technologies, this invention, through the collaborative operation of a sensitive feature analysis module, a normal state analysis module, and an analysis call module, performs real-time monitoring and analysis of the power supply characteristics of each power supply unit based on the sensitive feature analysis module. It analyzes the sensitivity of each power supply unit to various power supply characteristics, identifies the associated sensitive power supply characteristics of each power supply unit, analyzes the sensitive power supply characteristics under normal conditions using the normal state analysis module, constructs time-domain variation curves, selects anti-disturbance curve segments and verifies their effectiveness, and determines disturbance constraint limits. The analysis call module determines whether there are anomalies in the power supply unit based on changes in sensitive power supply characteristics and adjusts the acquisition frequency. This reduces the impact of power supply characteristic deviations caused by electrical coupling on monitoring accuracy, thereby improving the precision and reliability of monitoring.

[0041] In particular, this invention analyzes the changes in the power supply characteristics of each power supply unit to determine the sensitivity of each power supply unit to each power supply characteristic, and identifies the sensitive power supply characteristics associated with each power supply unit. In practice, due to the electrical coupling between the power supply units in the UPS system, the power supply units will affect each other, leading to fluctuations in power supply characteristics. Furthermore, due to the circuit connection relationship, the degree of influence between the power supply units also varies. Different power supply characteristics of different power supply units may reflect different characteristics. For example, the current of some power supply units is easily affected and fluctuates, while the voltage of some power supply units is easily affected and fluctuates, thus reflecting different sensitivities. Based on this, this invention considers analyzing the sensitivity of the power supply units to each power supply characteristic to determine the sensitive and non-sensitive power supply characteristics for each power supply unit, which facilitates subsequent adaptive monitoring of different power supply units and improves the accuracy and reliability of monitoring.

[0042] In particular, this invention screens anti-disturbance curve segments based on the peak fluctuations of time-domain variation curve segments of various sensitive power supply characteristics, verifies the effectiveness of each anti-disturbance curve segment, and determines the disturbance constraint limit. Since the mutual influence between power supply units may cause electrical parameters to deviate from the normal range, thus affecting the accuracy of monitoring when sampling for fault monitoring, sampling anti-disturbance curve segments with low interference and high confidence can more accurately reflect the true state of the power supply unit. These curve segments have strong resistance to system disturbances and can provide a more reliable reference benchmark. Furthermore, simply selecting disturbance rejection curve segments is insufficient. Even if these segments appear smooth on the surface, they may not possess adequate disturbance rejection capabilities. For instance, even if a curve segment appears smooth in the short term, it cannot be considered a valid disturbance rejection curve if it cannot be sustained for a certain period or if potential anomalies exist, such as sudden voltage or current fluctuations. Therefore, it is also necessary to consider disturbance rejection curve segments from a time perspective. Based on this, disturbance conditions should be set to eliminate the influence of accidental phenomena and ensure that the selected disturbance rejection curve segments can truly reflect the stable operating state of the system. This provides an accurate basis for determining disturbance constraint limits, thereby improving the stability and reliability of the entire system.

[0043] In particular, regarding sensitive power supply characteristics, which are characterized by their susceptibility to electrical coupling and the introduction of significant noise that interferes with normal operation, this invention determines whether the corresponding sensitive power supply characteristics are within the disturbance constraint limits and adjusts the sampling frequency for these characteristics. If the sensitive power supply characteristics of a power supply unit are within the disturbance constraint limits, it indicates that the sensitive power supply characteristics of that power supply unit are in a stable state with high confidence and minimal interference. Therefore, the sampling frequency for these sensitive power supply characteristics can be increased to facilitate observation of potential anomalies, thereby improving the accuracy and reliability of monitoring.

[0044] In particular, for the non-sensitive power supply characteristics of the power supply unit, since it is less affected by electrical coupling and is in a stable state with less noise under normal conditions, it can be directly monitored whether it is within the standard threshold range. Therefore, the sampling frequency can be maintained, and different analysis strategies can be adopted for different power supply characteristics. This can reliably capture anomalies, reduce the impact of electrical coupling, and improve the accuracy and reliability of system monitoring while optimizing resource utilization. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the structure of an intelligent fault monitoring system for UPS according to an embodiment of the invention;

[0046] Figure 2 This is a logic decision diagram for verifying the effectiveness of the anti-disturbance curve segment in an embodiment of the invention.

[0047] Figure 3 This is a logic diagram for determining whether a power supply unit corresponding to a sensitive power supply feature is abnormal, as shown in the embodiment of the invention.

[0048] Figure 4 This is a logic diagram for determining whether a non-sensitive power supply feature is abnormal, as shown in an embodiment of the invention. Detailed Implementation

[0049] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0050] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0051] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0052] Please see Figure 1 The diagram shown is a structural schematic of an intelligent fault monitoring system for UPS according to an embodiment of the invention. The intelligent fault monitoring system for UPS according to an embodiment of the invention includes:

[0053] The sensitive feature analysis module is used to record the power supply characteristics of several power supply units in the UPS power supply group, analyze the sensitivity of each power supply unit to each power supply characteristic based on the changes in each power supply characteristic of each power supply unit, and determine the sensitive power supply characteristics associated with each power supply unit.

[0054] The normal analysis module is connected to the sensitive feature analysis module. It is used to analyze the sensitive power supply characteristics of each power supply unit under normal conditions, construct the time-domain variation curve of the sensitive power supply characteristics within a predetermined time period, screen the anti-disturbance curve segment based on the peak fluctuation of the time-domain variation curve segment of each sensitive power supply characteristic, and verify the effectiveness of each anti-disturbance curve segment in order to determine the disturbance constraint limit.

[0055] The invocation analysis module, which is connected to both the sensitive feature analysis module and the normal parsing module, includes a first invocation unit and a second invocation unit.

[0056] The first calling unit is used to call the sensitive power supply features associated with the power supply unit, determine whether the corresponding sensitive power supply feature is within the disturbance constraint limit, adjust the acquisition frequency for the sensitive power supply feature to continuously acquire the corresponding sensitive power supply feature, and determine whether the power supply unit has an anomaly based on the anomaly of the corresponding sensitive power supply feature.

[0057] The second calling unit is used to call the remaining non-sensitive power supply characteristics of the power supply unit, and to determine whether there is an abnormality in the power supply unit based on whether the non-sensitive power supply characteristics are within the standard threshold range;

[0058] The power supply characteristics include current, voltage, impedance, and output frequency.

[0059] Specifically, there are no restrictions on the structure of the sensitive feature analysis module, the normal parsing module, and the call analysis module. They can be composed of logical components or combinations of logical components, including field-programmable processors, computers, or microprocessors in computers.

[0060] Specifically, there is no limitation on the specific method for obtaining the power supply characteristics of the power supply unit. Those skilled in the art can select the corresponding equipment from the prior art to monitor the current, voltage, impedance and output frequency of the power supply unit. This is the prior art and will not be described in detail. The output frequency is the fundamental frequency of the output alternating current (AC).

[0061] Specifically, there are no restrictions on the deployment methods of the sensitive feature analysis module, the normal state analysis module, and the call analysis module. They can be integrated into a single physical device, such as an industrial computer or embedded controller, for centralized management and control; or they can be distributed across multiple devices, interacting and collaborating via a network to form a distributed system architecture. In a distributed deployment, each module can be deployed in different physical locations as needed. For example, the sensitive feature analysis module can be installed close to the sensors of the UPS power supply group to reduce data transmission latency, while the normal state analysis module and the call analysis module can be deployed on servers in the data center for centralized processing and analysis. It is only necessary to ensure that there are reliable communication links and stable data interaction mechanisms between the modules; this will not be elaborated further.

[0062] Specifically, the sensitive feature analysis module is used to analyze the sensitivity of each power supply unit to each power supply feature in response to changes in the power supply features of each power supply unit.

[0063] Used to obtain the fluctuation amplitude of each power supply characteristic of each of the power supply units;

[0064] This is used to determine the fluctuation amplitude of each power supply characteristic as the sensitivity of the corresponding power supply characteristic.

[0065] The fluctuation amplitude is the average of the fluctuation values ​​at each time point, and the fluctuation value at each time point is the difference between the power supply characteristics at that time point and the power supply characteristics at the previous time point.

[0066] Specifically, the sensitive feature analysis module is used to determine the sensitive power supply features associated with each power supply unit, including:

[0067] If the sensitivity of the power supply feature is greater than or equal to the corresponding sensitivity threshold, then the power supply feature is determined to be a sensitive power supply feature associated with the power supply unit.

[0068] In implementation, the purpose of setting a sensitivity threshold is to characterize whether the fluctuation amplitude of the power supply characteristic under normal operation is significantly higher than the average level. The sensitivity threshold is predetermined. Those skilled in the art can collect a large amount of power supply characteristic data under normal operation to determine the average sensitivity of each power supply characteristic, so as to characterize the sensitivity of the power supply characteristic under normal operation. In order to represent the correlation between the power supply characteristic and the sensitivity, the sensitivity threshold is set as the product of the average sensitivity and the accuracy coefficient. Under normal circumstances, the accuracy coefficient is selected in the range [1.25, 1.55], and it is preferred to be 1.3 in implementation.

[0069] It is understandable that power supply characteristics include current, voltage, impedance, and output frequency. The sensitivity thresholds corresponding to current, voltage, impedance, and output frequency can be determined separately, which will not be elaborated here.

[0070] This invention analyzes the changes in the power supply characteristics of each power supply unit to determine the sensitivity of each power supply unit to its respective power supply characteristics, thereby identifying the sensitive power supply characteristics associated with each power supply unit. In practice, due to the electrical coupling between power supply units in a UPS system, these units can influence each other, leading to fluctuations in power supply characteristics. Furthermore, due to circuit connections, the degree of influence between power supply units varies, and different power supply characteristics of different power supply units may reflect different features. For example, the current of some power supply units is easily affected and fluctuates, while the voltage of some power supply units is easily affected and fluctuates, thus reflecting different sensitivities. Based on this, this invention considers analyzing the sensitivity of power supply units to their respective power supply characteristics to determine the sensitive and non-sensitive power supply characteristics for each power supply unit, facilitating subsequent adaptive monitoring of different power supply units and improving monitoring accuracy and reliability.

[0071] Specifically, the normal analysis module is used to screen disturbance rejection curve segments based on the peak fluctuations of time-domain variation curve segments of various sensitive power supply characteristics, including:

[0072] This is used to determine the peak fluctuation of the time-domain variation curve segment of each sensitive power supply characteristic. If the peak fluctuation is less than the predetermined peak fluctuation threshold, the time-domain variation curve segment of the sensitive power supply characteristic is selected as the anti-disturbance curve segment.

[0073] Among them, the peak fluctuation is the variance of the amplitude corresponding to each peak.

[0074] In implementation, the purpose of the peak fluctuation threshold is to characterize the relatively stable power supply characteristics. The peak fluctuation threshold is predetermined. Those skilled in the art can collect a large amount of power supply characteristic data under normal operating conditions and calculate the mean of the peak fluctuations to characterize the stability of power supply characteristic fluctuations under normal conditions. To represent the disturbance resistance capability of sensitive power supply characteristics, the predetermined peak fluctuation threshold is set as the product of the mean variance and the error tolerance coefficient. Typically, the error tolerance coefficient is selected within the range [0.85, 0.95], and is preferably 0.9 in practice.

[0075] Please see Figure 2 As shown, this is a logic decision diagram for verifying the effectiveness of the anti-disturbance curve segments according to an embodiment of the invention. Specifically, the normal parsing module is used to verify the effectiveness of each of the anti-disturbance curve segments, including:

[0076] Used to determine the ratio of the total duration of the anti-disturbance curve segment to the predetermined duration;

[0077] This is used to determine whether each of the anti-disturbance curve segments meets the anti-disturbance conditions. If it meets the anti-disturbance conditions, then each of the anti-disturbance curve segments is determined to be valid.

[0078] The anti-disturbance conditions include that the peak fluctuation is less than a predetermined peak fluctuation threshold, and the ratio of the total duration of the anti-disturbance curve segment to the predetermined duration is greater than a predetermined duration ratio threshold.

[0079] It is understandable that the total duration of the anti-disturbance curve segment refers to the total duration of the anti-disturbance curve segment identified as having anti-disturbance characteristics during the analysis process, and the predetermined duration is the total monitoring duration.

[0080] In practice, to avoid occasional disturbances and ensure that the anti-disturbance curve segment has data representation, the duration of the anti-disturbance curve segment should be greater than the threshold by more than 20%, and preferably 30% in practice.

[0081] Specifically, the normal analysis module is used to determine the disturbance constraint limits, including:

[0082] If each of the aforementioned anti-disturbance curve segments is determined to be valid, then the average peak value corresponding to each of the aforementioned anti-disturbance curve segments is determined;

[0083] This is used to determine the disturbance constraint limits based on the average peak value corresponding to each of the disturbance resistance curve segments.

[0084] In implementation, the disturbance constraint limits are determined based on the average peak value corresponding to each disturbance rejection curve segment. 1.25 times the average peak value of each effective disturbance rejection curve segment is determined as the upper limit of the interval, and 0.75 times the average peak value of each effective disturbance rejection curve segment is determined as the lower limit of the interval. A closed interval is constructed based on the upper and lower limits of the interval, and the closed interval is determined as the disturbance constraint limits to ensure the stability of the system under normal operating conditions.

[0085] It is understandable that the disturbance rejection curve segment can be applied to any power supply characteristic, and the corresponding disturbance constraint limit can be determined based on the type of power supply characteristic, which will not be elaborated here.

[0086] This invention screens anti-disturbance curve segments based on the peak fluctuations of time-domain variation curve segments of various sensitive power supply characteristics, verifies the effectiveness of each anti-disturbance curve segment, and determines the disturbance constraint limit. Since the mutual influence between power supply units may cause electrical parameters to deviate from the normal range, thus affecting the accuracy of monitoring when sampling for fault monitoring, sampling anti-disturbance curve segments with low interference and high confidence can more accurately reflect the true state of the power supply unit. These curve segments have strong resistance to system disturbances and can provide a more reliable reference benchmark. Furthermore, simply selecting disturbance rejection curve segments is insufficient. Even if these segments appear smooth on the surface, they may not possess adequate disturbance rejection capabilities. For instance, even if a curve segment appears smooth in the short term, it cannot be considered a valid disturbance rejection curve if it cannot be sustained for a certain period or if potential anomalies exist, such as sudden voltage or current fluctuations. Therefore, it is also necessary to consider disturbance rejection curve segments from a time perspective. Based on this, disturbance conditions should be set to eliminate the influence of accidental phenomena and ensure that the selected disturbance rejection curve segments can truly reflect the stable operating state of the system. This provides an accurate basis for determining disturbance constraint limits, thereby improving the stability and reliability of the entire system.

[0087] Specifically, the call analysis module is used to determine whether the corresponding sensitive power supply feature is within the disturbance constraint limit, and adjusting the sampling frequency for the sensitive power supply feature includes,

[0088] Used to determine the average peak value corresponding to each sensitive power supply characteristic;

[0089] If the average peak value is within the disturbance constraint limit, the sampling frequency for sensitive power supply characteristics is adjusted.

[0090] Understandably, the fact that the average peak value is within the disturbance constraint limit indicates that the corresponding sensitive power supply characteristics have entered a relatively stable stage. Therefore, the sampling frequency for sensitive power supply characteristics is adjusted to capture subtle changes in these characteristics.

[0091] Please see Figure 3As shown, it is a logic determination diagram for determining whether there is an abnormality in the power supply unit corresponding to the sensitive power supply feature in an embodiment of the invention. Specifically, the method of calling the analysis module to adjust the sampling frequency for the sensitive power supply feature includes increasing the sampling frequency for the sensitive power supply feature.

[0092] Understandably, when adjusting the sampling frequency for sensitive power supply characteristics, increasing the sampling frequency is necessary to more accurately monitor changes in these characteristics and ensure the stability and reliability of the system.

[0093] In practice, the sampling frequency is increased to an integer multiple of the original frequency. Those skilled in the art can set this themselves, so it will not be elaborated here.

[0094] Specifically, the call analysis module is used to determine whether there is an anomaly in the power supply unit based on the anomalies of corresponding sensitive power supply characteristics, including:

[0095] This is used to construct a time-domain variation curve of the sensitive power supply characteristics based on the collected sensitive power supply characteristics;

[0096] It is used to determine the abnormal peak based on the disturbance constraint limit, and to determine whether there is an abnormality in the power supply unit based on the proportion of the abnormal peak;

[0097] If the peak value exceeds the disturbance constraint limit, the peak is identified as an abnormal peak. If the proportion of abnormal peaks is greater than the predetermined proportion threshold, the power supply unit is determined to be abnormal.

[0098] It is understandable that the sensitive power supply characteristics of the power supply unit are easily affected by the electrical coupling of the system. Therefore, there may be occasional abnormal peaks. Based on this, a proportional threshold is set to determine whether the abnormal phenomenon of the power supply unit is not occasional, and thus determine the abnormality.

[0099] In practice, the proportional threshold is preset. The power supply characteristics obtained by monitoring several power supply units during normal operation are recorded in advance and the monitoring frequency is increased. The time-domain change curve of the power supply characteristics is determined, the proportion of the abnormal peak is determined, the proportional mean is solved, and the proportional threshold is set as the product of the proportional mean and the amplification factor. The amplification factor is selected in the interval [1.4, 1.6], and is set to 1.5 in practice. This will not be elaborated further.

[0100] The sensitive power supply characteristics are characterized by their susceptibility to electrical coupling, which can introduce significant noise and interfere with normal operation. Based on this, this invention determines whether the corresponding sensitive power supply characteristics are within the disturbance constraint limits and adjusts the sampling frequency for these characteristics. If the sensitive power supply characteristics of a power supply unit are within the disturbance constraint limits, it indicates that the sensitive power supply characteristics of that power supply unit are in a stable state with high confidence and minimal interference. Therefore, the sampling frequency for these sensitive power supply characteristics can be increased to facilitate observation of any potential anomalies, thereby improving the accuracy and reliability of monitoring.

[0101] Please see Figure 4 As shown, this is a logic diagram for determining whether a non-sensitive power supply characteristic is abnormal according to an embodiment of the invention. Specifically, the step of calling the analysis module to determine whether a power supply unit is abnormal based on whether the non-sensitive power supply characteristic is within a standard threshold range includes:

[0102] Used to obtain non-sensitive power supply characteristics;

[0103] If the non-sensitive power supply characteristics are not within the corresponding standard threshold range, the power supply unit is determined to be abnormal.

[0104] In practice, the purpose of the standard threshold range is to characterize the fluctuation range of power supply characteristics under normal operating conditions. The standard threshold range is predetermined. Those skilled in the art can collect a large amount of power supply characteristic data under normal operating conditions, determine the maximum power supply characteristic and the minimum power supply characteristic, use the maximum power supply characteristic as the upper limit of the interval, use the minimum power supply characteristic as the lower limit of the interval, construct a closed interval, and determine the closed interval as the standard threshold range.

[0105] Understandably, power supply characteristics, including current, voltage, impedance, and output frequency, all require the determination of corresponding standard threshold ranges.

[0106] For non-sensitive power supply characteristics of power supply units, since they are less affected by electrical coupling and are in a stable state with less noise under normal conditions, it is possible to directly monitor whether they are within the standard threshold range. Therefore, the sampling frequency can be maintained, and different analysis strategies can be adopted for different power supply characteristics. This can reliably capture anomalies, reduce the impact of electrical coupling, and improve the accuracy and reliability of system monitoring while optimizing resource utilization.

[0107] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. An intelligent fault monitoring system for UPS, characterized in that, include: The sensitive feature analysis module is used to record the power supply characteristics of several power supply units in the UPS power supply group, analyze the sensitivity of each power supply unit to each power supply characteristic based on the changes in each power supply characteristic of each power supply unit, and determine the sensitive power supply characteristics associated with each power supply unit. The normal analysis module is connected to the sensitive feature analysis module. It is used to analyze the sensitive power supply characteristics of each power supply unit under normal conditions, construct the time-domain variation curve of the sensitive power supply characteristics within a predetermined time period, screen the anti-disturbance curve segment based on the peak fluctuation of the time-domain variation curve segment of each sensitive power supply characteristic, and verify the effectiveness of each anti-disturbance curve segment in order to determine the disturbance constraint limit. The invocation analysis module, which is connected to both the sensitive feature analysis module and the normal parsing module, includes a first invocation unit and a second invocation unit. The first calling unit is used to call the sensitive power supply features associated with the power supply unit, determine whether the corresponding sensitive power supply feature is within the disturbance constraint limit, adjust the acquisition frequency for the sensitive power supply feature to continuously acquire the corresponding sensitive power supply feature, and determine whether the power supply unit is abnormal based on the anomaly of the corresponding sensitive power supply feature. The second calling unit is used to call the remaining non-sensitive power supply characteristics of the power supply unit, and to determine whether there is an abnormality in the power supply unit based on whether the non-sensitive power supply characteristics are within the standard threshold range; The power supply characteristics include current, voltage, impedance, and output frequency.

2. The intelligent fault monitoring system for UPS according to claim 1, characterized in that, The sensitive feature analysis module is used to analyze the sensitivity of each power supply unit to each power supply feature in response to changes in the power supply features of each power supply unit. Used to obtain the fluctuation amplitude of each power supply characteristic of each of the power supply units; This is used to determine the fluctuation amplitude of each power supply characteristic as the sensitivity of the corresponding power supply characteristic.

3. The intelligent fault monitoring system for UPS according to claim 1, characterized in that, The sensitive feature analysis module is used to determine the sensitive power supply features associated with each power supply unit, including: If the sensitivity of the power supply feature is greater than or equal to the corresponding sensitivity threshold, then the power supply feature is determined to be a sensitive power supply feature associated with the power supply unit.

4. The intelligent fault monitoring system for UPS according to claim 1, characterized in that, The normal analysis module is used to screen anti-disturbance curve segments based on the peak fluctuations of the time-domain variation curve segments of each sensitive power supply characteristic, including... This is used to determine the peak fluctuation of the time-domain variation curve segment of each sensitive power supply characteristic. If the peak fluctuation is less than the predetermined peak fluctuation threshold, the time-domain variation curve segment of the sensitive power supply characteristic is selected as the anti-disturbance curve segment. Among them, the peak fluctuation is the variance of the amplitude corresponding to each peak.

5. The intelligent fault monitoring system for UPS according to claim 1, characterized in that, The normal analysis module is used to verify the effectiveness of each of the disturbance rejection curve segments, including: Used to determine the ratio of the total duration of the anti-disturbance curve segment to the predetermined duration; This is used to determine whether each of the anti-disturbance curve segments meets the anti-disturbance conditions. If it meets the anti-disturbance conditions, then each of the anti-disturbance curve segments is determined to be valid. The anti-disturbance conditions include that the peak fluctuation is less than a predetermined peak fluctuation threshold, and the ratio of the total duration of the anti-disturbance curve segment to the predetermined duration is greater than a predetermined duration ratio threshold.

6. The intelligent fault monitoring system for UPS according to claim 1, characterized in that, The normal analysis module is used to determine the disturbance constraint limits, including: If each of the aforementioned anti-disturbance curve segments is determined to be valid, then the average peak value corresponding to each of the aforementioned anti-disturbance curve segments is determined; This is used to determine the disturbance constraint limits based on the average peak value corresponding to each of the disturbance resistance curve segments.

7. The intelligent fault monitoring system for UPS according to claim 1, characterized in that, The call analysis module is used to determine whether the corresponding sensitive power supply feature is within the disturbance constraint limit, and to adjust the sampling frequency for the sensitive power supply feature, including... Used to determine the average peak value corresponding to each sensitive power supply characteristic; If the average peak value is within the disturbance constraint limit, the sampling frequency for sensitive power supply characteristics is adjusted.

8. The intelligent fault monitoring system for UPS according to claim 7, characterized in that, The method of calling the analysis module to adjust the sampling frequency for sensitive power supply characteristics includes increasing the sampling frequency for sensitive power supply characteristics.

9. The intelligent fault monitoring system for UPS according to claim 8, characterized in that, The call analysis module is used to determine whether there is an anomaly in the power supply unit based on the anomalies of corresponding sensitive power supply characteristics, including: This is used to construct a time-domain variation curve of the sensitive power supply characteristics based on the collected sensitive power supply characteristics; It is used to determine the anomaly peak based on the disturbance constraint limit, and to determine whether there is an anomaly in the power supply unit based on the proportion of the anomaly peak; If the peak value exceeds the disturbance constraint limit, the peak is identified as an abnormal peak. If the proportion of abnormal peaks is greater than the predetermined proportion threshold, the power supply unit is determined to be abnormal.

10. The intelligent fault monitoring system for UPS according to claim 1, characterized in that, The call analysis module is used to determine whether there is an anomaly in the power supply unit based on whether the non-sensitive power supply characteristics are within the standard threshold range. Used to obtain non-sensitive power supply characteristics; If the non-sensitive power supply characteristics are not within the corresponding standard threshold range, the power supply unit is determined to be abnormal.

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