Operation fault monitoring method and system for three-phase AC load box

Abstract

The present application discloses an operation fault monitoring method and system for a three-phase AC load box, and relates to the field of electrical fault detection technology. The operation fault monitoring method for a three-phase AC load box includes: obtaining power monitoring data of the load box; obtaining the timing imbalance of the three-phase voltage of the load box based on the power monitoring data; obtaining the initial abnormal moment based on the timing imbalance; obtaining the fault severity value based on each timing imbalance after the initial abnormal moment; and adjusting the sample subset size of the isolation forest algorithm based on the fault severity value. The present application selects a suitable sample subset size based on the characteristics of the acquired power monitoring data, and uses the isolation forest algorithm to perform anomaly detection on the load box data, which can avoid the phenomenon that the sample subset is too small and cannot capture the data characteristics during a serious fault, and can also avoid the phenomenon that the sample subset is too large and the detection efficiency is low.

Description

Technical Field

[0001] The present application relates to the technical field of electrical fault detection, and in particular to an operation fault monitoring method and system for a three-phase AC load box. Background Art

[0002] A three-phase AC load bank is a programmable load simulation device used to test and verify various electrical equipment and components in power systems. By simulating actual load conditions, it provides accurate data support for power system maintenance, commissioning, and optimization. Three-phase AC load banks are widely used in various industries, including power, communications, energy, and transportation. In the energy industry, they primarily support performance testing and safety assessment of key equipment such as inverters and transformers in renewable energy power generation systems like wind and solar power. Three-phase AC load banks play a vital role in power systems like wind power generation. Fault monitoring can promptly detect faults within the load bank, such as short circuits, overloads, and component damage. These faults can cause fluctuations in grid voltage and frequency, potentially impacting the stable operation of the entire power system. Therefore, monitoring three-phase AC load banks can promptly detect operational faults in the power system, effectively preventing safety issues and ensuring power generation efficiency.

[0003] The isolation forest algorithm is commonly used in existing technologies for anomaly detection in three-phase AC load banks. When using the isolation forest algorithm, it is important to select an appropriate sample subset size. If the sample subset size is set too small, it will not fully represent the distribution of the original dataset, resulting in the constructed isolation tree not being able to accurately reflect the true structure of the data, thus affecting the accuracy of anomaly detection. If the sample subset size is too large, it will increase computational cost and time, especially when processing large datasets. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for monitoring operation faults of a three-phase AC load box, so as to solve the technical problem that a suitable sample subset size cannot be selected when using an isolation forest algorithm to perform anomaly detection on a three-phase AC load box.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] In a first aspect, the present application provides a method for monitoring an operation fault of a three-phase AC load box. The method for monitoring an operation fault of a three-phase AC load box includes:

[0007] Acquire power monitoring data of the load box; the power monitoring data at least includes a time-series voltage value of each phase in the load box;

[0008] Based on the power monitoring data, obtaining the time sequence imbalance of the three-phase voltage of the load box;

[0009] Based on the timing imbalance, obtaining the initial abnormal time;

[0010] Obtaining a fault severity value based on each time sequence imbalance after the initial abnormal moment; the fault severity value is at least used to characterize the fault severity of the load box;

[0011] Adjusting a sample subset size of an isolation forest algorithm based on the fault severity value;

[0012] Based on the sample subset size, an isolation forest algorithm is used to monitor the operation faults of the load box.

[0013] As a specific solution in the technical solution of the present application, the obtaining of the initial abnormal moment based on the timing imbalance includes: if the timing imbalance is greater than or equal to a first preset value, and all timing imbalances within a time period of a second preset value length before the timing imbalance are less than the first preset value, then determining that the moment corresponding to the timing imbalance is the initial abnormal moment.

[0014] As a specific solution in the technical solution of the present application, the acquisition of the fault severity value based on each timing imbalance after the initial abnormal moment includes:

[0015] Cluster each time series imbalance to obtain multiple clusters; each cluster corresponds to a time period;

[0016] Based on each cluster, an imbalance change value corresponding to each time period is obtained; the imbalance change value is at least used to characterize the degree of fluctuation of the time series imbalance in the time period corresponding to each cluster;

[0017] The fault severity value is obtained based on the imbalance change value.

[0018] As a specific solution in the technical solution of this application, the calculation formula for obtaining the imbalance change value corresponding to each time period based on each cluster is as follows:

[0019]

[0020] Among them, B a Indicates the change in the unbalance degree of the three-phase voltage in the ath time period; represents the mean of all time series imbalances in the ath time period; A1 represents the time series imbalance at the initial abnormal moment; j a Indicates the number of extreme points of time series imbalance in the ath time period; t a Indicates the total duration of the a-th time period; ΔAa It represents the range of all time series imbalances in the a-th time period; norm() represents the normalization function, which is used to map the values ​​in the brackets to the interval [0, 1].

[0021] As a specific solution in the technical solution of the present application, obtaining the fault severity value based on the imbalance change value includes:

[0022] Based on the imbalance degree of each time sequence, the maximum abnormal phase corresponding to each time sequence is obtained; the maximum abnormal phase is the phase with the largest voltage deviation rate in the corresponding time sequence;

[0023] Based on each maximum abnormal phase, obtain the abnormal phase sequence corresponding to each time period;

[0024] Based on the abnormal phase sequence, a fault complexity value corresponding to each time period is obtained; the fault complexity value is at least used to characterize the irregularity of each abnormal phase in the abnormal phase sequence over time;

[0025] The fault severity value is obtained based on the imbalance change value and the fault complexity value.

[0026] As a specific solution in the technical solution of this application, the calculation formula for obtaining the fault complexity value corresponding to each time period based on the abnormal phase sequence is as follows:

[0027]

[0028] Among them, C a represents the fault complexity value of the ath time period; p a Indicates the overall deviation rate of voltage in the ath time period; z a represents the number of phase types contained in the abnormal phase sequence in the a-th time period; l a represents the number of sequence segments in the abnormal phase sequence of the ath time period; L a is the duration of the ath time period.

[0029] As a specific solution in the technical solution of the present application, obtaining the fault severity value based on the imbalance change value and the fault complexity value includes:

[0030] Based on the abnormal phase sequence, a target phase is obtained; the target phase is the phase that appears the most times in the abnormal phase sequence;

[0031] Based on the target phase, a maximum continuous number and a maximum interval number are obtained from the abnormal phase sequence; the maximum continuous number is the maximum number of consecutive occurrences of the target phase in the abnormal phase sequence; the maximum interval number is the maximum number of other phases between two adjacent target phases;

[0032] The fault severity value is obtained based on the maximum continuous number, the maximum interval number, the imbalance change value, and the fault complexity value.

[0033] As a specific solution in the technical solution of this application, the calculation formula for obtaining the fault severity value based on the maximum continuous number, the maximum interval number, the imbalance change value and the fault complexity value is as follows:

[0034] D a =B a ×C a ×q a ×exp(-m a )

[0035] Where D a Indicates the fault severity value in the ath time period; B a Indicates the change in the unbalance degree of the three-phase voltage in the ath time period; C a represents the fault complexity value of the ath time period; q a Indicates the maximum number of intervals in the a-th time period; m a Indicates the maximum continuous number in the a-th time period.

[0036] As a specific solution in the technical solution of the present application, adjusting the sample subset size of the isolation forest algorithm based on the fault severity value includes:

[0037] Obtain a severity sequence based on the fault severity value corresponding to each time period;

[0038] Based on the severity sequence, obtaining a severity change value;

[0039] Based on the severity change value, the sample subset size of the isolation forest algorithm is adjusted.

[0040] In a second aspect, the present application proposes an operation fault monitoring system for a three-phase AC load box, the operation fault monitoring system for a three-phase AC load box comprising:

[0041] The collector is used to obtain power monitoring data of the load box; the power monitoring data at least includes the time-series voltage value of each phase in the load box;

[0042] The server is configured to obtain a timing imbalance degree of the three-phase voltage of the load box based on the power monitoring data;

[0043] and, based on the timing imbalance, obtaining an initial abnormal time;

[0044] and obtaining a fault severity value based on each time sequence imbalance after the initial abnormal moment; wherein the fault severity value is at least used to characterize the fault severity of the load box;

[0045] and, adjusting a sample subset size of an isolation forest algorithm based on the fault severity value;

[0046] And, based on the sample subset size, an isolation forest algorithm is used to monitor the operation fault of the load box.

[0047] As a specific solution in the technical solution of the present application, the server is also used to determine that the moment corresponding to the timing imbalance is the initial abnormal moment if the timing imbalance is greater than or equal to a first preset value, and all timing imbalances within a time period of a second preset value length before the timing imbalance are less than the first preset value.

[0048] As a specific solution in the technical solution of the present application, the server is further used to cluster each time series imbalance degree to obtain multiple clusters; each cluster corresponds to a time period;

[0049] And, based on each cluster, obtaining an imbalance change value corresponding to each time period; the imbalance change value is at least used to characterize the degree of fluctuation of the time series imbalance in the time period corresponding to each cluster;

[0050] And, based on the imbalance change value, the fault severity value is obtained.

[0051] As a specific solution in the technical solution of this application, the server obtains the calculation formula of the imbalance change value corresponding to each time period based on each cluster cluster as follows:

[0052]

[0053] Among them, B a Indicates the change in the unbalance degree of the three-phase voltage in the ath time period; represents the mean of all time series imbalances in the ath time period; A1 represents the time series imbalance at the initial abnormal moment; j a Indicates the number of extreme points of time series imbalance in the ath time period; t a Indicates the total duration of the a-th time period; ΔA a It represents the range of all time series imbalances in the a-th time period; norm() represents the normalization function, which is used to map the values ​​in the brackets to the interval [0, 1].

[0054] As a specific solution in the technical solution of the present application, the server is further configured to obtain the maximum abnormal phase corresponding to each time sequence based on the imbalance degree of each time sequence; the maximum abnormal phase is the phase with the largest voltage deviation rate in the corresponding time sequence;

[0055] and, based on each maximum abnormal phase, obtaining an abnormal phase sequence corresponding to each time period;

[0056] And, based on the abnormal phase sequence, obtaining a fault complexity value corresponding to each time period; the fault complexity value is at least used to characterize the irregularity of each abnormal phase in the abnormal phase sequence changing over time;

[0057] And, based on the imbalance change value and the fault complexity value, the fault severity value is obtained.

[0058] As a specific solution in the technical solution of this application, the server obtains the calculation formula of the fault complexity value corresponding to each time period based on the abnormal phase sequence as follows:

[0059]

[0060] Among them, C a represents the fault complexity value of the ath time period; p a Indicates the overall deviation rate of voltage in the ath time period; z a represents the number of phase types contained in the abnormal phase sequence in the a-th time period; l a represents the number of sequence segments in the abnormal phase sequence of the ath time period; L a is the duration of the ath time period.

[0061] As a specific solution in the technical solution of the present application, the server is further configured to obtain a target phase based on the abnormal phase sequence; the target phase is the phase that appears the most times in the abnormal phase sequence;

[0062] And, based on the target phase, a maximum continuous number and a maximum interval number are obtained from the abnormal phase sequence; the maximum continuous number is the maximum number of consecutive appearances of the target phase in the abnormal phase sequence; the maximum interval number is the maximum number of other phases existing between two adjacent target phases;

[0063] And, the fault severity value is obtained based on the maximum continuous number, the maximum interval number, the imbalance change value and the fault complexity value.

[0064] As a specific solution in the technical solution of the present application, the server obtains the fault severity value based on the maximum continuous number, the maximum interval number, the imbalance change value and the fault complexity value using the following calculation formula:

[0065] D a =B a ×C a ×q a ×exp(-m a )

[0066] Where D a Indicates the fault severity value in the ath time period; B a Indicates the change in the unbalance degree of the three-phase voltage in the ath time period; C a represents the fault complexity value of the ath time period; q a Indicates the maximum number of intervals in the a-th time period; m a Indicates the maximum continuous number in the a-th time period.

[0067] As a specific solution in the technical solution of the present application, the server is further used to obtain a severity sequence based on the fault severity value corresponding to each time period;

[0068] and, based on the severity sequence, obtaining a severity change value;

[0069] and, adjusting a sample subset size of an isolation forest algorithm based on the severity change value.

[0070] Compared with the prior art, the present invention has the following advantages:

[0071] This application selects an appropriate sample subset size based on the characteristics of the acquired power monitoring data and uses the isolation forest algorithm to perform anomaly detection on the load box data. This can avoid the phenomenon that the sample subset is too small and cannot capture the data characteristics of serious faults, and can also avoid the phenomenon that the sample subset is too large and the detection efficiency is low. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a flow chart of a method for monitoring operating faults of a three-phase AC load box proposed in this application;

[0073] Figure 2 This is a structural diagram of an operation fault monitoring system for a three-phase AC load box proposed in this application. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0075] The terms "first", "second", etc. in the description of the embodiments of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. For example, the first preset value and the second preset value proposed below belong to different preset values. It should be understood that the names used in this way can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than the content illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The division of modules that appears in the embodiments of the present application is only a logical division. In actual applications, there may be other division methods. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not performed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in the embodiments of the present application. Moreover, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed into multiple circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0076] In order to solve the technical problem in the background art that it is impossible to select a suitable sample subset size when using the isolation forest algorithm to detect anomalies in a three-phase AC load box, this application proposes a method for monitoring operation faults of a three-phase AC load box, such as Figure 1 As shown, the method for monitoring operation faults of a three-phase AC load box includes steps S100 to S600.

[0077] Step S100: Obtain power monitoring data of the load box.

[0078] In this embodiment, the power monitoring data at least includes the time-series voltage value of each phase in the load box. Specifically, the method for collecting the time-series voltage value of each phase in the load box can be as follows: the smart meter is connected in parallel to the input end of each phase in the wind power generation system load box, and the voltage data of each phase can be directly measured and recorded to obtain the voltage of each phase at each moment, and the measured voltage data is transmitted to the server through the communication interface to participate in subsequent monitoring. The voltage data of each phase in the three-phase AC load box is continuously measured by the above method, and the collection frequency is once per second to obtain the time-series voltage value of each phase. Of course, in other embodiments of the present application, other collection frequencies can also be set according to actual needs, for example: once every 2 seconds or once every 3 seconds, etc.

[0079] Step S200: Based on the power monitoring data, the timing imbalance of the three-phase voltage of the load box is obtained.

[0080] It's important to understand that, under normal circumstances, the three-phase voltages within a three-phase AC load box are essentially balanced and within the rated voltage range. However, for three-phase AC load boxes used in wind power generation systems, the harsh outdoor environment of wind farms can expose them to long-term vibration, temperature, humidity, and rainwater intrusion. These factors can damage cable insulation, loosen connectors connecting the load box, and potentially cause short circuits. These faults can lead to unstable voltage transmission within the load box. Therefore, it's important to first determine the balance of the three-phase voltages at each moment and use this balance to determine if the three-phase AC load box is experiencing any anomalies.

[0081] In this embodiment, the symmetrical component method is used to calculate the imbalance of the three-phase voltage at each moment (that is, the time sequence imbalance mentioned above), which is recorded as ε U The symmetrical component method is a well-known technology and will not be described in detail here.

[0082] Step S300: Based on the timing imbalance, an initial abnormal time is obtained.

[0083] In this embodiment, the initial abnormal moment refers to the moment when the load box timing imbalance first becomes abnormal. Based on this, step S300, obtaining the initial abnormal moment based on the timing imbalance, includes: if the timing imbalance is greater than or equal to a first preset value, and all timing imbalances within a time period of a second preset value before the timing imbalance are less than the first preset value, then determining the moment corresponding to the timing imbalance as the initial abnormal moment.

[0084] In a wind power generation system, under normal circumstances, the three-phase voltage is basically balanced, so the timing imbalance should be 0. Therefore, if the imbalance ε at a certain moment UWhen the voltage is greater than the first preset value, the current moment can be recorded as an abnormal moment. The abnormal moment may be when all three phases are abnormal or when only one of the three phases is abnormal. In this embodiment, the first preset value can be set according to needs, for example, the first preset value can be 0.1 or 0.2.

[0085] In this embodiment, if the load bank's timing imbalance is abnormal, any timing imbalance after that moment may be considered abnormal. That is, any moment corresponding to any timing imbalance after that moment may be considered the initial abnormal moment. To avoid this, a second preset value is set to prevent excessive acquisition of initial abnormal moments, thereby reducing the subsequent computational complexity. In this embodiment, the second preset value can be set as needed, for example, 30 minutes or 60 minutes.

[0086] Step S400: obtaining a fault severity value based on each timing imbalance after the initial abnormal moment.

[0087] In this embodiment, the fault severity value is at least used to characterize the severity of the fault of the load box. It should be noted that if the load box has a three-phase voltage imbalance, that is, the load box has an abnormality, the cause of the abnormality may be that the electrical connection of the load box is loose or short-circuited, causing the voltage to fluctuate. It may also be a non-fault fluctuation caused by electromagnetic interference or other factors, which will automatically return to stability when the interference disappears. Since the acquired power monitoring data is acquired in chronological order, when the abnormal moment occurs, it is necessary to further observe the changes in the voltage and imbalance of each phase after the abnormal moment to determine the severity of the load box fault. It is easy to understand that if the non-fault fluctuation is caused by electromagnetic interference or other factors, the fault severity of the load box is small; if the fault fluctuation is caused by a short circuit or loose electrical connection, the fault severity of the load box is large.

[0088] In this embodiment, the fault severity value can be obtained based on the various timing imbalances after the initial abnormal moment in any reasonable manner. For example, step S400, which obtains the fault severity value based on the various timing imbalances after the initial abnormal moment, may include steps S410 to S430.

[0089] Step S410: clustering each time series imbalance degree to obtain multiple clusters.

[0090] In the embodiment of the present application, any reasonable clustering method can be used to cluster the time series imbalances, such as the Euclidean distance clustering algorithm or the Manhattan distance clustering algorithm. Clustering algorithms are mature technologies and will not be described in detail here.

[0091] It should be noted that, based on step S410, multiple clusters can be obtained, and the time corresponding to the data in each cluster is taken as a time period, that is, each cluster corresponds to a time period, thereby obtaining the time series imbalance of multiple time periods after the initial abnormal moment.

[0092] Step S420: Based on each cluster, obtain the imbalance change value corresponding to each time period.

[0093] In this embodiment, the imbalance change value is at least used to characterize the degree of fluctuation of the timing imbalance in the time period corresponding to each cluster. As can be seen from the foregoing, since each time period contains multiple timing imbalances, a sequence of three-phase voltage imbalances of the load box in each time period after the initial abnormal moment can be obtained. The imbalance may change in different time periods. Therefore, the overall data characteristics (i.e., the imbalance change value) after the load box abnormality (i.e., after the initial abnormal moment) can be obtained through the timing imbalance in each time period after the initial abnormal moment. In an embodiment of the present application, the imbalance change value after the load box abnormality can be obtained in any reasonable way. For example: the average value of the timing imbalance in each time period can be used as the imbalance change value of the time period. In another embodiment of the present application, step S420, based on each cluster, the calculation formula for obtaining the imbalance change value corresponding to each time period can be as follows:

[0094]

[0095] Among them, B a Indicates the change in the unbalance degree of the three-phase voltage in the ath time period; represents the mean of all time series imbalances in the ath time period; A1 represents the time series imbalance at the initial abnormal moment; j a Indicates the number of extreme points of time series imbalance in the ath time period; t a Indicates the total duration of the a-th time period; ΔA a It represents the range of all time series imbalances in the a-th time period; norm() represents the normalization function, which is used to map the values ​​in the brackets to the interval [0, 1].

[0096] It needs to be clear that It represents the difference between the mean of the time series imbalance in the ath time period and the imbalance at the initial abnormal moment. The larger the value of , the greater the change in the imbalance degree in the ath time period after the initial abnormal moment compared with the initial abnormal moment. a / t aIndicates the frequency of change of the timing imbalance in the a-th time period. When the time length is constant, the more times the change occurs, the higher the frequency of change. ΔA a Indicates the range of all time series imbalances in the a-th time period, which is used to indicate the range of imbalance variation; that is, (j a / t a ×ΔA a ) represents the specific change characteristics of the imbalance degree in the a-th time period, which is used to analyze the overall change Adjustment is made. The higher the frequency of change and the larger the range of change, the greater the change characteristics, and the corresponding imbalance change value B a Also bigger.

[0097] Step S430: Acquire the fault severity value based on the imbalance change value.

[0098] In the embodiment of the present application, the imbalance change value can be directly used as the fault severity value. It is easy to understand that the larger the imbalance change value, the greater the change in the sequential imbalance after the initial abnormal moment, that is, the more serious the fault generated by the load box.

[0099] It should be noted that while the imbalance change value can intuitively reflect the overall abnormality level and development trend of the three-phase voltage, it is too general when used to reflect the severity of the load bank fault, ignoring the specific voltage changes of each phase. Therefore, when determining the severity of the load bank fault, further analysis can be performed in conjunction with the voltage changes of each phase. That is, in this embodiment, step S430, which obtains the fault severity value based on the imbalance change value, can include steps S440 to S470.

[0100] Step S440: Based on the imbalance degree of each time sequence, obtain the maximum abnormal phase corresponding to each time sequence.

[0101] In this embodiment, the maximum abnormal phase is the phase with the largest voltage deviation rate in the corresponding time sequence. It should be understood that the voltage of a wind power generation system generally has a fixed standard value and fluctuation range, so the deviation of each phase voltage should also be within a certain limit. By calculating the voltage deviation rate of each phase at the current moment, it can be determined whether the voltage of each phase is abnormal. The calculation formula of the voltage deviation rate can be expressed as:

[0102]

[0103] Among them, R represents the voltage deviation rate of a certain phase at the current moment; Indicates the measured voltage of a certain phase; U γRepresents the standard voltage value. This formula can be used to calculate the voltage deviation rate of each phase at each moment in each time period, with the phase with the largest deviation rate being the most abnormal phase at that moment.

[0104] Step S450: Based on each maximum abnormal phase, obtain the abnormal phase sequence corresponding to each time period.

[0105] In this embodiment, according to step S440, the abnormal phase at each moment in each time period can be determined to obtain an abnormal phase sequence. That is, the maximum voltage deviation rate of the three phases at each moment in the time period is determined to obtain a plurality of maximum voltage deviation rates.

[0106] Step S460: Based on the abnormal phase sequence, obtain the fault complexity value corresponding to each time period.

[0107] In this embodiment, the fault complexity value is used to characterize at least the irregularity of each abnormal phase in the abnormal phase sequence over time. It is readily understood that the greater the regularity of the data in the abnormal phase sequence, the more regular the load bank faults, i.e., the less severe the load bank faults. The less regularity of the data in the abnormal phase sequence, the more irregular the load bank faults, i.e., the more severe the load bank faults.

[0108] In the embodiment of the present application, any reasonable method can be used to obtain the fault complexity value corresponding to each time period based on the abnormal phase sequence. For example, in step S460, the calculation formula for obtaining the fault complexity value corresponding to each time period based on the abnormal phase sequence can be as follows:

[0109]

[0110] Among them, C a represents the fault complexity value of the ath time period; p a Indicates the overall deviation rate of voltage in the ath time period; z a represents the number of phase types contained in the abnormal phase sequence in the a-th time period; l a represents the number of sequence segments in the abnormal phase sequence of the ath time period; L a is the duration of the ath time period.

[0111] In this embodiment, the mean of all maximum voltage deviation rates within a time period is calculated as the overall voltage deviation rate for that time period. A clustering algorithm ensures that the time series imbalance at all times within each time period is similar. However, the abnormal phases at all times within the same time period may not be completely similar. That is, the abnormal phases within each time period vary. For example, if the three-phase voltages are denoted as 1, 2, and 3, the distribution of 1, 2, and 3 within the abnormal phase sequence may vary. They may occur in every phase, in only one or two phases, and the continuity of each phase may also vary. Each abnormal phase represents a fault type, thus varying the fault complexity within each time period. In other words, in this embodiment, the minimum number of phase types included in the abnormal phase sequence is 1 and the maximum is 3. In this embodiment, one or more consecutive phases within the abnormal phase sequence are denoted as a sequence segment. For example, if an abnormal phase sequence is 1221123, the number of sequence segments in this abnormal phase sequence is 5, with the segments being 1, 22, 11, 2, and 3, respectively. The number of sequence segments in other abnormal phase sequences is similar and will not be listed in detail later.

[0112] In this embodiment, a greater overall voltage deviation rate within a time period, more abnormal phases, and more sequence segments in the abnormal phase sequence indicate a more complex initial fault in the time period, and a greater corresponding fault complexity value.

[0113] Step S470: Acquire the fault severity value based on the imbalance change value and the fault complexity value.

[0114] In the embodiment of the present application, the fault severity value may be obtained based on the imbalance change value and the fault complexity value in any reasonable manner. For example, the fault severity value may be the sum or product of the imbalance change value and the fault complexity value.

[0115] It should be noted that within a certain time period, initially only one phase or two phases may be abnormal, and the subsequent addition of a third phase indicates that the fault is gradually worsening. Alternatively, all three phases may have abnormalities from the beginning, alternating between them, also indicating that the abnormality is more serious. Therefore, the distribution of abnormal phases can also be combined to determine the severity of the fault within each time period. Based on this, in a specific embodiment of the present application, step S470, based on the imbalance change value and the fault complexity value, obtains the fault severity value, which can include steps S471 to S473.

[0116] Step S471: Acquire the target phase based on the abnormal phase sequence.

[0117] In this embodiment, the target phase is the phase that appears the most times in the abnormal phase sequence.

[0118] Step S472: Based on the target phase, obtain the maximum continuous number and the maximum interval number from the abnormal phase sequence.

[0119] In this embodiment, the maximum continuous number is the maximum number of consecutive appearances of the target phase in the abnormal phase sequence. The maximum interval number is the maximum number of other phases between two adjacent target phases. For example, if a certain abnormal phase sequence is 112211231, since 1 appears 5 times, 2 appears 3 times, and 3 appears 1 time, the target phase is 1. There are only 22 sequence segments between the first 11 sequence segment and the second 11 sequence segment, and there are 2 sequence segments and 3 sequence segments between the second 11 sequence segment and the last 1 sequence segment. In other words, the maximum continuous number and the maximum interval number are both 2. The target phases, maximum continuous numbers, and maximum interval numbers of sequence segments in other abnormal phase sequences are similar and will not be listed in detail later.

[0120] Step S473: Obtain the fault severity value based on the maximum continuous number, the maximum interval number, the imbalance change value and the fault complexity value.

[0121] In an embodiment of the present application, the fault severity value can be obtained based on the maximum continuous number, the maximum number of intervals, the imbalance change value, and the fault complexity value in any reasonable manner. For example, in step S473, the calculation formula for obtaining the fault severity value based on the maximum continuous number, the maximum number of intervals, the imbalance change value, and the fault complexity value can be as follows:

[0122] D a =B a ×C a ×q a ×exp(-m a )

[0123] Where D a Indicates the fault severity value in the ath time period; B a Indicates the change in the unbalance degree of the three-phase voltage in the ath time period; C a represents the fault complexity value of the ath time period; q a Indicates the maximum number of intervals in the a-th time period; m a Indicates the maximum continuous number in the a-th time period.

[0124] Step S500: Based on the fault severity value, adjust the sample subset size of the isolation forest algorithm.

[0125] It's important to understand that if a load bank fault causes changes in three-phase voltage and timing imbalance, the severity of each fault can vary. For example, a fault might become increasingly severe over time, or it might be relatively minor and not progressively worsen over time. In other words, a fault might remain stable. Therefore, the severity of the load bank fault over the entire fault cycle must be determined based on the severity of each time period.

[0126] In this embodiment, a larger fault severity value for the current time period indicates a more severe fault occurring in the load bank. In this case, when monitoring faults occurring in the load bank using the isolation forest algorithm, the sample subset size can be reduced to ensure that the isolation forest algorithm accurately captures data features. A smaller fault severity value for the current time period indicates a less severe fault occurring in the load bank. In this case, when monitoring faults occurring in the load bank using the isolation forest algorithm, the sample subset size can be increased to improve detection efficiency.

[0127] In an embodiment of the present application, any reasonable method can be used to adjust the sample subset size of the isolation forest algorithm based on the fault severity value. For example, step S500, adjusting the sample subset size of the isolation forest algorithm based on the fault severity value, includes steps S510 to S530.

[0128] Step S510: Obtain a severity sequence based on the fault severity value corresponding to each time period.

[0129] As can be seen from the previous section, a fault severity value can be obtained based on the time series imbalance of each time period, and multiple fault severity values ​​can form a severity sequence. It is easy to understand that if the fault becomes more severe, the fault severity values ​​will increase over time.

[0130] Step S520: Based on the severity sequence, obtain a severity change value.

[0131] As previously mentioned, if the severity of a fault increases, the individual fault severity values ​​in the severity sequence also increase. If the individual fault severity values ​​in the severity sequence remain relatively stable, this indicates that the load bank fault has also remained stable and has not worsened. Based on this, a straight line can be drawn for the change in fault severity, with time on the horizontal axis and the fault severity value on the vertical axis. The slope of this line, denoted as k, represents the overall fault change trend over the observation period. A larger k indicates an increase in fault severity, while a smaller k indicates a relatively stable or less severe fault.

[0132] In an embodiment of the present application, the slope k can be directly used as the severity change value. In another embodiment of the present application, in step S520, based on the severity sequence, the calculation formula for obtaining the severity change value can be as follows:

[0133]

[0134] Where F represents the severity change value of the load bank; represents the mean value of the fault severity value of the load box in each time period; k represents the change trend of the fault severity value of the load box, which is the slope mentioned above; t(D d,max ) represents the time when the maximum fault severity value appears in the severity sequence, t(D d,max ) value is larger, indicating that the maximum fault severity occurs later, indicating that the fault has been getting worse, the change trend is larger, and the later the most serious data appears, the greater the fault severity is, and the corresponding severity change value is larger.

[0135] Step S530: adjusting the sample subset size of the isolation forest algorithm based on the severity change value.

[0136] In an embodiment of the present application, when the isolation forest algorithm is preset to be used for load box abnormality monitoring, the initial sample subset size can be 256. When the fault severity is relatively small, the size of the sample subset can be appropriately increased to improve the detection efficiency. When the fault severity is relatively large, the size of the sample subset needs to be reduced to ensure that the data features can be accurately captured.

[0137] Therefore, the adjusted sample subset size can be expressed as:

[0138] M=256-256×norm(F)

[0139] M represents the size of the adjusted sample subset, and norm(F) indicates the use of a normalization function to normalize the load bank's fault severity to the range [0, 1]. The larger the fault severity, the smaller the adjusted sample subset, and vice versa. The value of 256 × norm(F) is rounded down.

[0140] Step S600: Based on the sample subset size, an isolation forest algorithm is used to monitor the operating faults of the load box.

[0141] It's important to note that using the isolation forest algorithm to monitor load bank operational faults based on sample subset size is a mature technology and will not be discussed in detail here. Using the isolation forest algorithm to detect anomalies in acquired data allows for the timely identification of three-phase AC load bank operational faults and the implementation of timely action, ensuring the safe and stable operation of the power system.

[0142] The embodiment of the operation fault monitoring method for a three-phase AC load box proposed in the present application selects an appropriate sample subset size based on the characteristics of the acquired power monitoring data, and uses the isolation forest algorithm to perform anomaly detection on the load box data. This can avoid the phenomenon that the sample subset is too small and cannot capture the data characteristics of a serious fault, and can also avoid the phenomenon that the sample subset is too large and the detection efficiency is low. The operation fault monitoring method for a three-phase AC load box proposed in the present application can monitor the operation status of the three-phase AC load box in the wind power generation system in real time, can accurately determine whether the load box is in normal working condition, promptly detect the operation fault of the load box, prevent electrical accidents, ensure the safe and stable operation of the equipment, reduce the occurrence of downtime due to load box operation faults, and improve work efficiency.

[0143] After introducing the embodiment of the operation fault monitoring method for the three-phase AC load box proposed in this application, the operation fault monitoring system for the three-phase AC load box proposed in this application is introduced below. Figure 2 As shown, the operation fault monitoring system 10 for a three-phase AC load box includes:

[0144] The collector 11 is used to obtain power monitoring data of the load box; the power monitoring data at least includes the time-series voltage value of each phase in the load box;

[0145] The server 12 is configured to obtain a timing imbalance of the three-phase voltage of the load box based on the power monitoring data;

[0146] and, based on the timing imbalance, obtaining an initial abnormal time;

[0147] and obtaining a fault severity value based on each time sequence imbalance after the initial abnormal moment; wherein the fault severity value is at least used to characterize the fault severity of the load box;

[0148] and, adjusting a sample subset size of an isolation forest algorithm based on the fault severity value;

[0149] And, based on the sample subset size, an isolation forest algorithm is used to monitor the operation fault of the load box.

[0150] As a specific embodiment of the present application, the server 12 is further used to determine that the moment corresponding to the timing imbalance is the initial abnormal moment if the timing imbalance is greater than or equal to a first preset value and all timing imbalances within a time period of a second preset value length before the timing imbalance are less than the first preset value.

[0151] As a specific embodiment of the present application, the server 12 is further configured to cluster each time series imbalance degree to obtain a plurality of clusters; each cluster corresponds to a time period;

[0152] And, based on each cluster, obtaining an imbalance change value corresponding to each time period; the imbalance change value is at least used to characterize the degree of fluctuation of the time series imbalance in the time period corresponding to each cluster;

[0153] And, based on the imbalance change value, the fault severity value is obtained.

[0154] As a specific embodiment of the present application, the server 12 obtains the calculation formula of the imbalance change value corresponding to each time period based on each cluster as follows:

[0155]

[0156] Among them, B a Indicates the change in the unbalance degree of the three-phase voltage in the ath time period; represents the mean of all time series imbalances in the ath time period; A1 represents the time series imbalance at the initial abnormal moment; j a Indicates the number of extreme points of time series imbalance in the ath time period; t a Indicates the total duration of the a-th time period; ΔA a It represents the range of all time series imbalances in the a-th time period; norm() represents the normalization function, which is used to map the values ​​in the brackets to the interval [0, 1].

[0157] As a specific embodiment of the present application, the server 12 is further configured to obtain the maximum abnormal phase corresponding to each time sequence based on the imbalance degree of each time sequence; the maximum abnormal phase is the phase with the largest voltage deviation rate in the corresponding time sequence;

[0158] and, based on each maximum abnormal phase, obtaining an abnormal phase sequence corresponding to each time period;

[0159] And, based on the abnormal phase sequence, obtaining a fault complexity value corresponding to each time period; the fault complexity value is at least used to characterize the irregularity of each abnormal phase in the abnormal phase sequence changing over time;

[0160] And, based on the imbalance change value and the fault complexity value, the fault severity value is obtained.

[0161] As a specific embodiment of the present application, the server 12 obtains the fault complexity value corresponding to each time period based on the abnormal phase sequence using the following calculation formula:

[0162]

[0163] Among them, C a represents the fault complexity value of the ath time period; p a Indicates the overall deviation rate of voltage in the ath time period; z a represents the number of phase types contained in the abnormal phase sequence in the a-th time period; l a represents the number of sequence segments in the abnormal phase sequence of the ath time period; L a is the duration of the ath time period.

[0164] As a specific embodiment of the present application, the server 12 is further configured to obtain a target phase based on the abnormal phase sequence; the target phase is the phase that appears the most times in the abnormal phase sequence;

[0165] And, based on the target phase, a maximum continuous number and a maximum interval number are obtained from the abnormal phase sequence; the maximum continuous number is the maximum number of consecutive appearances of the target phase in the abnormal phase sequence; the maximum interval number is the maximum number of other phases existing between two adjacent target phases;

[0166] And, the fault severity value is obtained based on the maximum continuous number, the maximum interval number, the imbalance change value and the fault complexity value.

[0167] As a specific embodiment of the present application, the server 12 obtains the fault severity value based on the maximum continuous number, the maximum interval number, the imbalance change value, and the fault complexity value using the following calculation formula:

[0168] D a =B a ×C a ×q a ×exp(-m a )

[0169] Where D a Indicates the fault severity value in the ath time period; B a Indicates the change in the unbalance degree of the three-phase voltage in the ath time period; C a represents the fault complexity value of the ath time period; q a Indicates the maximum number of intervals in the a-th time period; m a Indicates the maximum continuous number in the a-th time period.

[0170] As a specific embodiment of the present application, the server 12 is further configured to obtain a severity sequence based on the fault severity value corresponding to each time period;

[0171] and, based on the severity sequence, obtaining a severity change value;

[0172] and, adjusting a sample subset size of an isolation forest algorithm based on the severity change value.

[0173] The embodiment of the operation fault monitoring system for a three-phase AC load box proposed in the present application selects an appropriate sample subset size based on the characteristics of the acquired power monitoring data and uses the isolation forest algorithm to detect anomalies in the load box data. This can avoid the phenomenon that the sample subset is too small and cannot capture the data characteristics of a serious fault, and can also avoid the phenomenon that the sample subset is too large and the detection efficiency is low. The operation fault monitoring system for a three-phase AC load box proposed in the present application can monitor the operation status of the three-phase AC load box in the wind power generation system in real time, accurately determine whether the load box is in normal working condition, promptly detect the operation fault of the load box, prevent electrical accidents, ensure the safe and stable operation of the equipment, reduce the occurrence of downtime due to load box operation faults, and improve work efficiency.

[0174] It should be understood that computer-readable storage media in this application include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory, static random access memory, dynamic random access memory, other types of random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory or other memory technology, read-only compact disc read-only memory, digital versatile disc or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media such as modulated data signals and carrier waves.

[0175] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0176] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the methods, devices and equipment described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0177] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0178] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of the present embodiment according to actual needs.

[0179] In addition, the functional modules in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into a module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0180] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0181] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrations. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disk), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0182] Although the embodiments of the present application have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions, and alterations may be made to these embodiments without departing from the principles of the present application.

Claims

1. A method for monitoring operating faults of a three-phase AC load bank, characterized in that: include: Obtain power monitoring data from the load box; The power monitoring data includes at least the time sequence voltage value of each phase in the load box; Based on the power monitoring data, obtaining the time sequence imbalance of the three-phase voltage of the load box; Based on the timing imbalance, obtaining the initial abnormal time; Obtaining a fault severity value based on each time sequence imbalance after the initial abnormal moment; the fault severity value is at least used to characterize the fault severity of the load box; Adjusting a sample subset size of an isolation forest algorithm based on the fault severity value; Based on the sample subset size, an isolation forest algorithm is used to monitor the operation fault of the load box; The obtaining of a fault severity value based on each time sequence imbalance after the initial abnormal moment includes: Cluster each time series imbalance to obtain multiple clusters; each cluster corresponds to a time period; Based on each cluster, an imbalance change value corresponding to each time period is obtained; the imbalance change value is at least used to characterize the degree of fluctuation of the time series imbalance in the time period corresponding to each cluster; Obtaining the fault severity value based on the imbalance change value; The calculation formula for obtaining the imbalance change value corresponding to each time period based on each cluster is as follows: in, Indicates the change in the unbalance degree of the three-phase voltage in the ath time period; represents the mean value of all time series imbalances in the a-th time period; Indicates the timing imbalance at the initial abnormal moment; Indicates the number of extreme points of time series imbalance in the ath time period; Indicates the total duration of the a-th time period; Indicates the range of all time series imbalances in the ath time period; Represents a normalization function, which is used to map the values ​​in the brackets to the range [0, 1].

2. The method for monitoring operation failure of a three-phase AC load box according to claim 1, characterized in that: The obtaining of the initial abnormal moment based on the timing imbalance includes: if the timing imbalance is greater than or equal to a first preset value, and all timing imbalances within a time period of a second preset value length before the timing imbalance are less than the first preset value, determining that the moment corresponding to the timing imbalance is the initial abnormal moment.

3. The method for monitoring operation failure of a three-phase AC load box according to claim 1, characterized in that: The acquiring the fault severity value based on the imbalance change value includes: Based on the imbalance degree of each time sequence, the maximum abnormal phase corresponding to each time sequence is obtained; the maximum abnormal phase is the phase with the largest voltage deviation rate in the corresponding time sequence; Based on each maximum abnormal phase, obtain the abnormal phase sequence corresponding to each time period; Based on the abnormal phase sequence, a fault complexity value corresponding to each time period is obtained; the fault complexity value is at least used to characterize the irregularity of each abnormal phase in the abnormal phase sequence over time; The fault severity value is obtained based on the imbalance change value and the fault complexity value.

4. The method for monitoring operation failure of a three-phase AC load box according to claim 3, characterized in that: The calculation formula for obtaining the fault complexity value corresponding to each time period based on the abnormal phase sequence is as follows: in, represents the fault complexity value of the ath time period; It represents the overall deviation rate of voltage in the ath time period; represents the number of phase types contained in the abnormal phase sequence in the a-th time period; represents the number of sequence segments in the abnormal phase sequence of the a-th time period; is the duration of the ath time period.

5. The method for monitoring operation failure of a three-phase AC load box according to claim 3, characterized in that: The acquiring the fault severity value based on the imbalance change value and the fault complexity value includes: Based on the abnormal phase sequence, a target phase is obtained; the target phase is the phase that appears the most times in the abnormal phase sequence; Based on the target phase, a maximum continuous number and a maximum interval number are obtained from the abnormal phase sequence; the maximum continuous number is the maximum number of consecutive occurrences of the target phase in the abnormal phase sequence; the maximum interval number is the maximum number of other phases between two adjacent target phases; The fault severity value is obtained based on the maximum continuous number, the maximum interval number, the imbalance change value, and the fault complexity value.

6. The method for monitoring operation failure of a three-phase AC load box according to claim 5, characterized in that: The calculation formula for obtaining the fault severity value based on the maximum continuous number, the maximum interval number, the imbalance change value, and the fault complexity value is as follows: Where, Indicates the fault severity value in the ath time period; Indicates the change in the unbalance degree of the three-phase voltage in the ath time period; represents the fault complexity value of the ath time period; Indicates the maximum number of intervals in the a-th time period; Indicates the maximum continuous number in the a-th time period.

7. The method for monitoring operation failure of a three-phase AC load box according to claim 5, characterized in that: The adjusting the sample subset size of the isolation forest algorithm based on the fault severity value includes: Obtain a severity sequence based on the fault severity value corresponding to each time period; Based on the severity sequence, obtaining a severity change value; Based on the severity change value, the sample subset size of the isolation forest algorithm is adjusted.

8. An operation fault monitoring system for a three-phase AC load box, characterized in that: include: The collector is used to obtain power monitoring data of the load box; the power monitoring data at least includes the time-series voltage value of each phase in the load box; The server is configured to obtain a timing imbalance degree of the three-phase voltage of the load box based on the power monitoring data; and, based on the timing imbalance, obtaining an initial abnormal time; and, obtaining a fault severity value based on each timing imbalance after the initial abnormal moment; The fault severity value is at least used to represent the severity of the fault of the load box; The obtaining of a fault severity value based on each time sequence imbalance after the initial abnormal moment includes: Cluster each time series imbalance to obtain multiple clusters; each cluster corresponds to a time period; Based on each cluster, an imbalance change value corresponding to each time period is obtained; the imbalance change value is at least used to characterize the degree of fluctuation of the time series imbalance in the time period corresponding to each cluster; Obtaining the fault severity value based on the imbalance change value; The calculation formula for obtaining the imbalance change value corresponding to each time period based on each cluster is as follows: in, Indicates the change in the unbalance degree of the three-phase voltage in the ath time period; represents the mean value of all time series imbalances in the a-th time period; Indicates the timing imbalance at the initial abnormal moment; Indicates the number of extreme points of time series imbalance in the ath time period; Indicates the total duration of the a-th time period; Indicates the range of all time series imbalances in the ath time period; Represents a normalization function, which is used to map the values ​​in the brackets to the range of [0, 1]; and, adjusting a sample subset size of an isolation forest algorithm based on the fault severity value; And, based on the sample subset size, an isolation forest algorithm is used to monitor the operation fault of the load box.

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