A detection method and device for medium and high voltage DC power supply for mine

Abstract

The present application relates to the technical field of fault detection, and specifically to a detection method and device for a medium- and high-voltage direct current power supply for mining, which specifically includes: constructing a comprehensive impact index at each acquisition moment based on the load current and load voltage of a high-voltage direct current power supply when multiple high-power production equipment are running in parallel during mining, calculating the intensity of the harmonic component action at each acquisition moment based on the difference between the comprehensive impact indexes at different acquisition moments, determining the power supply reliability coefficient at each acquisition moment, and then using a LOF abnormality detection algorithm to perform abnormal fault detection on the load current at the moment when the power supply is unreliable. In the present application, only the LOF value of the load current at the moment when the power supply is unreliable is calculated, so as to avoid the LOF abnormality detection algorithm from calculating the LOF value of the load current at all moments, thereby solving the problem of the slow running speed of the LOF abnormality detection algorithm and improving the efficiency of abnormal fault detection on the high-voltage direct current power supply for mining.

Description

Technical Field

[0001] The present application relates to the technical field of fault detection, and in particular to a method and device for detecting a medium- and high-voltage direct current power supply for mining. Background Art

[0002] With the development of industrial and mining enterprises, while mining resources are being developed, the safety of high-voltage power supply in mines has attracted more and more attention. High-voltage DC power supply is a power supply with huge power used for voltage and current stabilization. Since high-voltage DC power supply energy storage has high stability, high-voltage DC power supply is widely used in high-voltage power supply systems in mines. However, the mining process often requires a large number of high-power production equipment and consumes a large amount of electricity resources, which can easily cause the high-voltage DC power supply to be damaged due to long-term high-load operation, resulting in abnormal failures in the use of high-voltage DC power supply.

[0003] With the continuous development of machine learning technology, the load current and load voltage of high-voltage DC power supplies are monitored in real time, and the abnormal faults in the use of high-voltage DC power supplies can be accurately identified by using anomaly detection technology. For example, the common LOF anomaly detection algorithm (Local outlier factor, LOF) can accurately identify abnormal faults in the use of high-voltage DC power supplies. However, the traditional LOF anomaly detection algorithm needs to traverse the entire data set to calculate the LOF value of each sample point, which makes the LOF anomaly detection algorithm run slowly, resulting in low efficiency in abnormal fault detection of mining high-voltage DC power supplies. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a detection method and device for a medium and high voltage DC power supply for mining. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for detecting a medium- and high-voltage DC power supply for a mine, the method comprising the following steps:

[0006] Collect the load current and load voltage at each moment during the operation of the high-voltage DC power supply, and construct a load current monitoring sequence and a load voltage monitoring sequence;

[0007] Based on the data changes in the load current monitoring sequence and the load voltage monitoring sequence, a comprehensive impact index of each acquisition moment is constructed;

[0008] Based on the difference between the comprehensive impact indexes at different collection times, the harmonic component action intensity at each collection time is calculated, and the power supply reliability coefficient at each collection time is determined;

[0009] The power supply unreliable moment is obtained based on the power supply reliability coefficient combined with the clustering algorithm, and the high-voltage DC power supply anomaly detection is performed based on the abnormal degree of load current at the moment of unreliable power supply.

[0010] In one embodiment, the process of obtaining the comprehensive impact index is as follows:

[0011] Based on the load current time series, a current long-term characteristic sequence of each load current is constructed; based on the load voltage monitoring sequence, the same acquisition method as the current long-term characteristic sequence is adopted to obtain the voltage long-term characteristic sequence of each load voltage;

[0012] The long-term high load characteristic value of the load current at each acquisition moment is calculated based on the data discreteness and overall value size in the long-term characteristic sequence of current; the load severity characteristic value of the load voltage at each acquisition moment is calculated based on the data change in the long-term characteristic sequence of voltage;

[0013] The fusion value of the long-term high load characteristic value of the load current at each acquisition moment and the load severity characteristic value of the load voltage is used as the comprehensive impact index at each acquisition moment.

[0014] In one embodiment, the process of acquiring the long-term characteristic sequence of the current is as follows:

[0015] A sliding window of each load current is set with each load current in the load current monitoring sequence as the center; and a sequence composed of all load currents in the sliding window is used as a current long-term characteristic sequence of each load current.

[0016] In one embodiment, the process of obtaining the long-term high load characteristic value is as follows:

[0017] Calculate the mean and dispersion of all elements in the long-term characteristic sequence of the load current at each acquisition moment, and record them as the first current mean and the first dispersion respectively;

[0018] The long-term high-load characteristic value of the load current at each acquisition moment is positively correlated with the first current mean value, and negatively correlated with the first dispersion.

[0019] In one embodiment, the process of obtaining the load severity characteristic value is as follows:

[0020] The extreme difference value in the voltage long-term characteristic sequence of the load voltage at each acquisition moment is calculated; the mean value of all elements in the voltage long-term characteristic sequence of the load voltage at each acquisition moment is calculated and recorded as the first voltage mean value; the load severity characteristic value of the load voltage at each acquisition moment is positively correlated with the extreme difference value and negatively correlated with the first voltage mean value.

[0021] In one embodiment, the process of obtaining the intensity of the harmonic component is as follows:

[0022] A sequence composed of comprehensive influence indexes of a preset first number of collection moments closest to any collection moment is used as a parallel influence feature sequence of the any collection moment;

[0023] The difference between any two adjacent elements in the parallel influence feature sequence at each acquisition moment is calculated, which is recorded as the first difference; the mean of all elements in the parallel influence feature sequence at each acquisition moment is calculated, which is recorded as the second mean; the intensity of the harmonic component at each acquisition moment is positively correlated with the second mean, and negatively correlated with the first difference.

[0024] In one embodiment, the power supply reliability coefficient is a calculation result of an exponential function with a natural constant as the base and a negative number of the intensity of the harmonic component at each acquisition moment as the exponent.

[0025] In one embodiment, the process of obtaining the time when power supply is unreliable is:

[0026] The power supply reliability coefficients of all collection moments are used as the input of the clustering algorithm, and each cluster is output; the mean of all elements in each cluster is calculated and recorded as the third mean, and the collection moment corresponding to all elements in the cluster with the smallest third mean is taken as the power supply unreliable moment.

[0027] In one embodiment, the high-voltage DC power supply abnormality detection based on the abnormality of the load current at the time when the power supply is unreliable is specifically:

[0028] The load current at all times when power supply is unreliable is used as the input of the abnormality detection algorithm, and the output is the abnormal value of each abnormal load current; the average of the abnormal values ​​of all abnormal load currents is used as the abnormal fault metric value. If the abnormal fault metric value is greater than the abnormal fault judgment threshold, an abnormal fault occurs in the high-voltage DC power supply; otherwise, no abnormal fault occurs in the high-voltage DC power supply.

[0029] In a second aspect, an embodiment of the present application also provides a detection device for a medium- and high-voltage direct current power supply for mining, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0030] The embodiments of the present application have at least the following beneficial effects:

[0031] The present application realizes the detection of abnormal faults occurring during the operation of a high-voltage DC power supply for mining by using a LOF abnormal detection algorithm. Compared with the traditional LOF abnormal detection algorithm, which needs to traverse the entire data set to calculate the LOF value of each sample point, the LOF abnormal detection algorithm runs slowly, resulting in the problem of low efficiency in abnormal fault detection of the high-voltage DC power supply for mining. The present application combines the severity of the load generated by the high-voltage DC power supply for mining when multiple high-power production equipment runs in parallel during mining to construct a comprehensive impact index at each acquisition moment, calculate the impact of harmonic components, construct a power supply reliability coefficient and extract the moment of unreliable power supply, and then use the LOF abnormal detection algorithm to perform abnormal fault detection on the load current at the moment of unreliable power supply. In the present application, only the LOF value of the load current at the moment of unreliable power supply is calculated to avoid the LOF abnormal detection algorithm from calculating the LOF value of the load current at all moments, thereby solving the problem of slow operation of the LOF abnormal detection algorithm and improving the efficiency of abnormal fault detection of the high-voltage DC power supply for mining. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 A flowchart of a method for detecting a medium- and high-voltage DC power supply for mining provided in one embodiment of the present application;

[0034] Figure 2 This is a schematic diagram of k-means clustering results;

[0035] Figure 3 Schematic diagram of the process of obtaining the comprehensive impact index. DETAILED DESCRIPTION

[0036] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the detection method and device of a medium and high voltage DC power supply for mining proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0037] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0038] The following is a detailed description of a method and device for detecting a medium- and high-voltage DC power supply for mining provided by the present application in conjunction with the accompanying drawings.

[0039] See also Figure 1 , which shows a flow chart of the steps of a detection method for a medium- and high-voltage DC power supply for mining provided by an embodiment of the present application, the method comprising the following steps:

[0040] Step S1, using a sensor to measure the load current and load voltage during the operation of the high-voltage direct current power supply, and performing preprocessing to obtain a load current monitoring sequence and a load voltage monitoring sequence.

[0041] The purpose of this scheme is to improve the efficiency of abnormal fault detection of high-voltage DC power supply for mining by increasing the speed of LOF abnormal detection algorithm, which is conducive to timely repair of abnormal faults caused by high-voltage DC power supply, thereby avoiding safety hazards in the mining process.

[0042] (1) During mining, current sensors and voltage sensors are used to collect the load current and load voltage of the high-voltage DC power supply in real time. In this embodiment, the collection time interval is 1 second and the collection time length is 30 minutes. It should be noted that the collection time interval and the collection time length can be set by the implementer, and this embodiment does not impose any specific restrictions.

[0043] (2) Preprocess the collected load current and load voltage:

[0044] A sequence of all load currents collected in ascending time order is used as a load current time series, and a sequence of all load voltages collected in ascending time order is used as a load voltage time series.

[0045] (3) In order to avoid the influence of external interference signals on data acquisition, the load current time series and the load voltage time series are respectively used as the input of the adaptive filtering algorithm, and the output of the adaptive filtering algorithm is respectively used as the load current monitoring sequence and the load voltage monitoring sequence, wherein the adaptive filtering algorithm is a well-known technology, and the specific process is not repeated here. It should be noted that this embodiment only provides a filtering algorithm for denoising, and the implementer may also use other filtering algorithms for denoising, and this embodiment does not make specific restrictions.

[0046] Step S2, constructing a comprehensive impact index at each acquisition moment based on data changes in the load current monitoring sequence and the load voltage monitoring sequence.

[0047] Generally speaking, the mining process often requires a large number of high-power production equipment. When a large number of high-power production equipment are running at the same time, a large amount of power resources are required, which will cause the high-voltage DC power supply to be in a high-load state for a long time, that is, the load current of the high-voltage DC power supply is at a high level for a long time. In order to improve the efficiency of abnormal fault detection of high-voltage DC power supplies for mines, it is necessary to analyze the long-term high-load characteristics of high-voltage DC power supplies during operation.

[0048] (1) Based on the load current time series and the load voltage time series, the long-term characteristic sequence of each load current and the long-term characteristic sequence of each load voltage are constructed respectively:

[0049] In order to analyze the long-term load characteristics of the high-voltage DC power supply at different acquisition times, each load current in the load current monitoring sequence is set as the center. A window of a certain size is used as a sliding window of each load current in the load current monitoring sequence; a sequence composed of all load currents in the sliding window of each load current in ascending time order is used as a current long-term characteristic sequence of each load current;

[0050] It should be noted that when the load currents in the sliding window are less than 31, the load currents in the sliding window are completed by using the mean filling method;

[0051] Similarly, for each load voltage in the load voltage monitoring sequence, the same acquisition method as the current long-term characteristic sequence is adopted to obtain the voltage long-term characteristic sequence of each load voltage in the load voltage monitoring sequence.

[0052] (2) Based on the data discreteness and overall value in the long-term characteristic sequence of current, the long-term high load characteristic value of the load current at each acquisition moment is calculated:

[0053] Calculate the mean and dispersion of all elements in the long-term characteristic sequence of the load current at each acquisition moment, and record them as the first current mean and the first dispersion respectively;

[0054] A long-term high-load characteristic value of the load current at each acquisition moment is determined based on the first current mean value and the first dispersion, wherein the long-term high-load characteristic value of the load current at each acquisition moment is positively correlated with the first current mean value and negatively correlated with the first dispersion.

[0055] Preferably, as an embodiment of the present application, the first discreteness may be the variance of all load currents in the current long-term characteristic sequence of the load current at each acquisition moment; the preset error parameter, it should be noted that the error parameter implementer can set it by himself, in this embodiment, the error parameter is set to 1, and the sum of the first discreteness and the error parameter is calculated, and the long-term high load characteristic value of the load current at each acquisition moment may be the ratio of the first current mean to the sum;

[0056] In other embodiments of the present application, the first dispersion may be the standard deviation of all load currents in the long-term characteristic sequence of the load current at each acquisition moment.

[0057] The larger the first current mean value is, the more it can reflect that the load current is at a higher level at this time, and the stronger the high load characteristic of the high-voltage DC power supply is at this time. At the same time, the smaller the first discreteness is, the stronger the concentration of the load current in the local range is, that is, all load currents in the local range are close to the same level at this time. Therefore, when the load current is at a higher level at this time, and all load currents in the local range are close to the same level at this time, it can be explained that the high-voltage DC power supply for mining is in a long-term high load state at this time, and the longer-term high load characteristic value is larger.

[0058] (3) Calculate the load severity characteristic value of the load voltage at each acquisition moment based on the data changes in the long-term characteristic sequence of voltage:

[0059] Generally speaking, the deeper the high-voltage DC power supply is in a high-load operating state, the higher the load current is, and the greater the voltage drop characteristic generated by the higher level of load current through the load line is, that is, the load voltage shows a decreasing characteristic, and the stronger the load voltage decrease characteristic is, the heavier the load state of the high-voltage DC power supply is.

[0060] Calculate the extreme value of the long-term characteristic sequence of the load voltage at each acquisition moment;

[0061] Calculate the mean of all elements in the long-term characteristic sequence of the load voltage at each acquisition moment, and record it as the first voltage mean;

[0062] A load severity characteristic value of the load voltage at each acquisition moment is determined based on the extreme difference value and the first voltage mean value, wherein the load severity characteristic value of the load voltage at each acquisition moment is positively correlated with the extreme difference value and negatively correlated with the first voltage mean value.

[0063] Preferably, as an embodiment of the present application, the load severity characteristic value of the load voltage at each acquisition moment may be a ratio of the extreme difference value to the first voltage mean value;

[0064] In other embodiments of the present application, an exponential function with a natural constant as the base and a negative number of the first voltage mean as the exponent is calculated and recorded as a first exponential function; the load severity characteristic value of the load voltage at each acquisition moment can be the product of the extreme value and the first exponential function.

[0065] The larger the range value in the long-term characteristic sequence of voltage, the stronger the characteristic of the decrease in load voltage in the local range, that is, the greater the voltage drop effect. At the same time, the smaller the first voltage mean value, which reflects that the average level of load voltage is lower after the voltage drop effect occurs in the load circuit in the high-voltage DC power supply. To a certain extent, it shows that the higher the severity of the load state of the high-voltage DC power supply, the greater the load severity characteristic value.

[0066] (4) Calculate the comprehensive impact index at each collection time based on the long-term high load characteristic value and the load severity characteristic value:

[0067] During the mining process, when the high-voltage DC power supply is in a high-load state for a long time, and the severity of the load state is higher, the high-voltage DC power supply will be in a serious load state for a long time because multiple high-power production equipment require a large amount of electricity resources when running at the same time during the mining process. Therefore, the greater the characteristic of the high-voltage DC power supply being in a serious load state for a long time, the more it can be explained that high-power production equipment in the mine is running at the same time, and the high-voltage DC power supply is more likely to have an abnormal failure at this time.

[0068] The fusion value of the long-term high load characteristic value of the load current at each acquisition moment and the load severity characteristic value of the load voltage at each acquisition moment is taken as the comprehensive impact index of each acquisition moment, wherein the comprehensive impact index of each acquisition moment is positively correlated with the long-term high load characteristic value of the load current at each acquisition moment and the load severity characteristic value of the load voltage at each acquisition moment.

[0069] Preferably, as an embodiment of the present application, the comprehensive impact index at each acquisition moment may be the product of the long-term high load characteristic value of the load current at each acquisition moment and the load severity characteristic value of the load voltage at each acquisition moment;

[0070] In other embodiments of the present application, the comprehensive impact index at each acquisition moment may be the sum of the normalized value of the long-term high load characteristic value of the load current at each acquisition moment and the normalized value of the load severity characteristic value of the load voltage at each acquisition moment.

[0071] The larger the long-term high load characteristic value and the larger the load severity characteristic value, to a certain extent, it indicates that the high-voltage DC power supply is in a high-load state for a long time, and the higher the severity of the load state, the greater the impact of the simultaneous operation of high-power production equipment on the high-voltage DC power supply during mining operations, and the greater the comprehensive impact index, that is, the more likely the high-voltage DC power supply is to have an abnormal failure.

[0072] Step S3, calculating the harmonic component action intensity at each collection moment based on the difference between the comprehensive impact indexes at different collection moments, and determining the power supply reliability coefficient at each collection moment based on the harmonic component action intensity at each collection moment.

[0073] In a mine, the long-term operation of multiple high-power production equipment at the same time will affect the reliability of the mine high-voltage DC power supply. For example, when multiple high-power production equipment is running for a long time at the same time, a strong harmonic component is generated. The strong harmonic component enters the high-voltage DC power supply system through the line, causing the quality of the high-voltage DC power supply to fluctuate. At this time, the reliability of the power supply is low, which is prone to safety hazards. Therefore, in order to analyze the reliability of the mine high-voltage DC power supply, it is necessary to continue the analysis based on the comprehensive impact index.

[0074] (1) A set consisting of a preset first number of collection moments closest to any collection moment is used as a neighboring moment set of the any collection moment. It should be noted that the value of the first number can be set by the implementer. In this embodiment, the first number is set to 30; a sequence consisting of the comprehensive influence indexes of all collection moments in the neighboring moment set of each collection moment in ascending time order is used as a parallel influence feature sequence of each collection moment.

[0075] (2) Calculate the intensity of the harmonic component at each acquisition moment based on the difference between adjacent elements in the parallel impact feature sequence:

[0076] Calculate the difference between any two adjacent elements in the parallel influence feature sequence at each acquisition moment, and record it as the first difference;

[0077] Calculate the mean of all elements in the parallel influence feature sequence at each acquisition moment, recorded as the second mean;

[0078] The harmonic component intensity at each acquisition moment is determined based on all the first differences and the second mean values ​​in the parallel impact feature sequence at each acquisition moment, wherein the harmonic component intensity at each acquisition moment is positively correlated with the second mean value and negatively correlated with the first difference.

[0079] Preferably, as an embodiment of the present application, the first difference may be the absolute value of the difference between any two adjacent elements in the parallel influence feature sequence at each acquisition moment; the average value of all the first differences in the parallel influence feature sequence at each acquisition moment is calculated, and the sum of the average value of the first differences and the preset error parameter is used as the denominator, and the second mean value is used as the numerator; the intensity of the harmonic component at each acquisition moment may be the ratio of the numerator to the denominator; the role of the error parameter is to avoid the denominator being 0;

[0080] In other embodiments of the present application, the first difference may be the square of the difference between any two adjacent elements in the parallel impact feature sequence at each acquisition moment.

[0081] The larger the second mean value is and the smaller the difference between adjacent elements in the parallel influence characteristic sequence is, the more likely it is that multiple large-scale mining production equipment will operate in parallel and continuously at the overall level within the local time range. During this process, each large-scale mining production equipment generates harmonic components, and multiple large-scale mining production equipment generate harmonic components at the same time, resulting in a superposition effect of harmonic components. The greater the intensity of the harmonic component, the more likely it is to damage the high-voltage DC power supply.

[0082] (3) The higher the intensity of the harmonic component, the more likely it is to damage the high-voltage DC power supply, thereby affecting the reliability of the high-voltage DC power supply. Therefore, the power supply reliability coefficient at each collection time is determined based on the intensity of the harmonic component at each collection time, where the power supply reliability coefficient at each collection time is negatively correlated with the intensity of the harmonic component at each collection time.

[0083] Preferably, as an embodiment of the present application, the power supply reliability coefficient at each acquisition moment may be a calculation result of an exponential function with a natural constant as the base and a negative number of the intensity of the harmonic component at each acquisition moment as the exponent;

[0084] In other embodiments of the present application, the power supply reliability coefficient at each collection moment may be the inverse of the intensity of the harmonic component at each collection moment.

[0085] The greater the intensity of the harmonic component, the greater the intensity of the harmonic component generated by the parallel operation of multiple high-power production equipment in mineral resource development. At this time, it is more likely to affect the quality of the high-voltage DC power supply, resulting in reduced reliability of the high-voltage DC power supply, and the smaller the power supply reliability coefficient.

[0086] Step S4, obtaining the time of unreliable power supply based on the power supply reliability coefficient in combination with the clustering algorithm, and performing high-voltage DC power supply abnormality detection based on the abnormality degree of the load current at the time of unreliable power supply.

[0087] By increasing the running speed of the LOF anomaly detection algorithm, the efficiency of abnormal fault detection for mining high-voltage DC power supplies is improved.

[0088] (1) Based on the power supply reliability coefficient and clustering algorithm, the unreliable power supply time is obtained:

[0089] The power supply reliability coefficients at all acquisition moments are used as the input of the k-means clustering algorithm. In this embodiment, the number of clusters is 2. It should be noted that the number of clusters can be set by the implementer, and this embodiment does not impose any specific restrictions. The metric distance for clustering is Euclidean distance, and the output of the k-means clustering algorithm is 2 clusters, where the k-means clustering algorithm is a well-known technology, and the specific process will not be repeated; for the clustering of power supply reliability coefficients, this embodiment of the application only provides a clustering method, and there are many existing clustering methods. The implementer can also use other clustering algorithms for clustering, and this application does not impose any specific restrictions.

[0090] The schematic diagram of k-means clustering results is as follows Figure 2 shown.

[0091] The mean of all elements in each cluster is calculated and recorded as the third mean. Since the reliability of the mining high-voltage DC power supply represented by the elements in the cluster with a smaller third mean is lower, the collection time corresponding to all elements in the cluster with the smallest third mean is taken as the unreliable power supply time.

[0092] (2) Detection of abnormality of high-voltage DC power supply based on the abnormality of load current at the time when power supply is unreliable:

[0093] In order to realize abnormal fault detection of mining high-voltage DC power supply, the load current at all times of unreliable power supply is used as the input of LOF abnormal detection algorithm, wherein the distance neighborhood parameter is taken as 10, and the output of LOF abnormal detection algorithm is the LOF value of each abnormal load current, wherein LOF abnormal detection algorithm is a well-known technology, and the specific process will not be repeated; it should be noted that the implementer of the distance neighborhood parameter can set it by himself, and this embodiment does not make specific restrictions.

[0094] The larger the LOF value of the abnormal load current, the higher the degree of abnormality of the abnormal load current. At this time, the mine high-voltage DC power supply is more likely to have an abnormal fault. Therefore, the average of the LOF values ​​of all abnormal load currents is used as the abnormal fault metric value. When the abnormal fault metric value is greater than the abnormal fault determination threshold, it is determined that the high-voltage DC power supply has an abnormal fault, and the abnormal fault caused by the high-voltage DC power supply should be repaired. It should be noted that the implementer of the abnormal fault determination threshold can set it by himself. In this embodiment, the abnormal fault determination threshold is set to 2. If the abnormal fault metric value is less than the abnormal fault determination threshold, it is determined that the high-voltage DC power supply has no abnormal fault and does not need to be repaired.

[0095] The schematic diagram of the process of obtaining the comprehensive impact index is as follows: Figure 3 shown.

[0096] Based on the same inventive concept as the above method, an embodiment of the present application also provides a detection device for a medium- and high-voltage direct current power supply for mining, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods for detecting a medium- and high-voltage direct current power supply for mining when executing the computer program.

[0097] In summary, the embodiment of the present application provides a detection method for a medium and high voltage DC power supply for mining. The present application realizes the detection of abnormal faults occurring during the operation of the high voltage DC power supply for mining by using the LOF abnormal detection algorithm. Compared with the traditional LOF abnormal detection algorithm, it is necessary to traverse the entire data set to calculate the LOF value of each sample point, which makes the LOF abnormal detection algorithm run slowly, resulting in the problem of low efficiency in abnormal fault detection of the high voltage DC power supply for mining. The present application combines the severity of the load generated by the high voltage DC power supply for mining when multiple high-power production equipment runs in parallel during mining to construct a comprehensive impact index for each acquisition moment, calculate the impact of harmonic components, construct a power supply reliability coefficient and extract the moment of unreliable power supply, and then use the LOF abnormal detection algorithm to perform abnormal fault detection on the load current at the moment of unreliable power supply. In the present application, only the LOF value of the load current at the moment of unreliable power supply is calculated, so as to avoid the LOF abnormal detection algorithm from calculating the LOF value of the load current at all times, solve the problem of slow operation of the LOF abnormal detection algorithm, and improve the efficiency of abnormal fault detection of the high voltage DC power supply for mining.

[0098] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0099] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0100] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for detecting a medium- and high-voltage DC power supply for mining, characterized in that: The method comprises the following steps: Collect the load current and load voltage at each moment during the operation of the high-voltage DC power supply, and construct a load current monitoring sequence and a load voltage monitoring sequence; Based on the data changes in the load current monitoring sequence and the load voltage monitoring sequence, a comprehensive impact index of each acquisition moment is constructed; Based on the difference between the comprehensive impact indexes at different collection times, the harmonic component action intensity at each collection time is calculated, and the power supply reliability coefficient at each collection time is determined; The power supply unreliable moment is obtained based on the power supply reliability coefficient combined with the clustering algorithm, and the abnormality degree of the load current at the moment of unreliable power supply is used to detect the abnormality of the high-voltage DC power supply; The process of obtaining the comprehensive impact index is as follows: Based on the load current time series, a current long-term characteristic sequence of each load current is constructed; based on the load voltage monitoring sequence, the same acquisition method as the current long-term characteristic sequence is adopted to obtain the voltage long-term characteristic sequence of each load voltage; The long-term high load characteristic value of the load current at each acquisition moment is calculated based on the data discreteness and overall value size in the long-term characteristic sequence of current; the load severity characteristic value of the load voltage at each acquisition moment is calculated based on the data change in the long-term characteristic sequence of voltage; The fusion value of the long-term high load characteristic value of the load current at each acquisition moment and the load severity characteristic value of the load voltage is used as the comprehensive impact index at each acquisition moment; The process of obtaining the long-term high load characteristic value is as follows: Calculate the mean and dispersion of all elements in the long-term characteristic sequence of the load current at each acquisition moment, and record them as the first current mean and the first dispersion respectively; The long-term high-load characteristic value of the load current at each acquisition moment is positively correlated with the first current mean value, and negatively correlated with the first dispersion; The process of obtaining the load severity characteristic value is as follows: Calculate the extreme difference value in the voltage long-term characteristic sequence of the load voltage at each acquisition moment; calculate the mean value of all elements in the voltage long-term characteristic sequence of the load voltage at each acquisition moment, recorded as the first voltage mean value; the load severity characteristic value of the load voltage at each acquisition moment is positively correlated with the extreme difference value, and negatively correlated with the first voltage mean value; The process of obtaining the intensity of the harmonic component is as follows: A sequence composed of comprehensive influence indexes of a preset first number of collection moments closest to any collection moment is used as a parallel influence feature sequence of the any collection moment; The difference between any two adjacent elements in the parallel influence feature sequence at each acquisition moment is calculated, recorded as the first difference; the mean of all elements in the parallel influence feature sequence at each acquisition moment is calculated, recorded as the second mean; the intensity of the harmonic component at each acquisition moment is positively correlated with the second mean, and negatively correlated with the first difference; The power supply reliability coefficient is a calculation result of an exponential function with a natural constant as the base and the negative number of the intensity of the harmonic component at each acquisition moment as the exponent.

2. A method for detecting a medium- and high-voltage DC power supply for mining as claimed in claim 1, characterized in that: The acquisition process of the current long-term characteristic sequence is as follows: A sliding window of each load current is set with each load current in the load current monitoring sequence as the center; and a sequence composed of all load currents in the sliding window is used as a current long-term characteristic sequence of each load current.

3. A method for detecting a medium- and high-voltage direct current power supply for mining as claimed in claim 1, characterized in that: The process of obtaining the time when power supply is unreliable is as follows: The power supply reliability coefficients of all collection moments are used as the input of the clustering algorithm, and each cluster is output; the mean of all elements in each cluster is calculated and recorded as the third mean, and the collection moment corresponding to all elements in the cluster with the smallest third mean is taken as the power supply unreliable moment.

4. A method for detecting a medium- and high-voltage DC power supply for mining as claimed in claim 1, characterized in that: The abnormality detection of the high-voltage DC power supply based on the abnormality degree of the load current at the time when the power supply is unreliable is specifically as follows: The load current at all times when power supply is unreliable is used as the input of the abnormality detection algorithm, and the output is the abnormal value of each abnormal load current; the average of the abnormal values ​​of all abnormal load currents is used as the abnormal fault metric value. If the abnormal fault metric value is greater than the abnormal fault judgment threshold, an abnormal fault occurs in the high-voltage DC power supply; otherwise, no abnormal fault occurs in the high-voltage DC power supply.

5. A detection device for a medium- and high-voltage DC power supply for mining, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

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