A remote control system for a generator set installed in a low-voltage distribution cabinet

Abstract

The present invention relates to the field of generator set control technology, and specifically to a generator set remote control system loaded in a low-voltage power distribution cabinet. The system includes a data acquisition module for acquiring data; a generator set instantaneous interference analysis module, which is used to obtain the interference factor at the moment according to the relevant changes in the output current data and the load current data; a generator set time period interference analysis module, which is used to determine the interference period according to the interference factor combined with the output current stability, and analyze the interference duration at the interference moment; an untrustworthy weight analysis module, which is used to obtain the untrustworthy weight in combination with the interference situation of the interference period corresponding to the moment and the load of the generator set; a filtering and control module, which is used to filter and perform control analysis based on the untrustworthy weight. The present invention comprehensively reflects the trustworthiness of the total current data in the time sequence through the different interference situations of multiple generator sets in the time sequence, so that the filtered data is more accurate, and the accuracy and reliability of the control analysis after filtering are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of generator set control, and in particular to a generator set remote control system installed in a low-voltage power distribution cabinet. Background Art

[0002] In large facilities or mission-critical environments, multiple generator sets are operated in parallel in low-voltage distribution cabinets to provide higher total output power and redundant backup. The remote control system for generator sets installed in low-voltage distribution cabinets is designed to achieve remote monitoring and control of generator sets. Through such a remote control system, operators can monitor the operating status of the generator sets in real time at a location far away from the generator sets and perform remote operations when necessary. This not only improves the convenience and safety of operation, but also helps to improve the operating efficiency and reliability of the generator sets.

[0003] When multiple generator sets are running, the monitoring signals may be interfered with due to electromagnetic interference generated by surrounding electrical equipment or power lines. In addition, environmental factors such as temperature, humidity or dust, as well as remote signal transmission, may also cause interference to different generator sets under the monitoring of the generator set. If the interference level analysis is inaccurate, the collected generator set monitoring data will be inaccurate, thereby affecting the accuracy of subsequent remote control of the generator set. Summary of the invention

[0004] In order to solve the technical problem that the inaccurate interference degree analysis in the prior art will lead to inaccurate monitoring data of the collected generator set, thereby affecting the accuracy of the subsequent remote control of the generator set, the purpose of the present invention is to provide a remote control system for the generator set loaded in a low-voltage distribution cabinet, and the technical scheme adopted is as follows:

[0005] The present invention provides a remote control system for a generator set installed in a low-voltage power distribution cabinet, the system comprising: a data acquisition module, for acquiring total output current data and total load current data of the low-voltage power distribution cabinet at each sampling moment in a historical period, and output current data and load ratio of each generator set in the low-voltage power distribution cabinet;

[0006] The generator set instantaneous interference analysis module is used to obtain the associated components of each generator set through the correlation between the components of the output current data and the total load current data in the historical period; and obtain the interference factor at each sampling moment according to the matching degree between the associated components and the output current data at each sampling moment;

[0007] The generator set period interference analysis module is used to obtain the divided period for each generator set according to the similarity of the interference factor at the sampling time; obtain the interference degree of each divided period through the change distribution of the interference factor and the output current data in each divided period, and the correlation degree between the associated component and the output current data; obtain the interference period according to the interference degree of the divided period; obtain the interference duration of each generator set according to the duration distribution of the interference period on the generator set and the interference degree;

[0008] The untrustworthy weight analysis module is used to obtain the untrustworthy weight at each sampling moment based on the distribution of each interference period corresponding to all generator sets at each sampling moment, combined with the load proportion and interference duration of the generator set corresponding to the interference period, as well as the interference duration and duration distribution in the interference period;

[0009] The filtering and control module is used to filter the total output current data based on the untrustworthy weight at each sampling moment, and obtain the filtered current data to input into the controller for control.

[0010] Furthermore, the method for obtaining the associated component includes:

[0011] For any generator set, the output current data of the generator set in the historical period is subjected to empirical mode decomposition to obtain the IMF component of the generator set;

[0012] Calculate the Pearson correlation coefficient between each IMF component and the total load current data of the generator set in the historical period to obtain the correlation of each IMF component;

[0013] The IMF component with the maximum correlation is taken as the associated component of the generator set.

[0014] Furthermore, the method for obtaining the interference factor includes:

[0015] For any generator set, the associated components and output current data of the generator set in the historical period are matched using the DTW algorithm to obtain matching pairs at each sampling time;

[0016] The number of corresponding matching pairs at each sampling moment is taken as the matching value at each sampling moment;

[0017] Calculate the distance between each matching pair at each sampling time and find the average to obtain the matching distance at each sampling time;

[0018] The interference factor at each sampling moment is obtained by combining the matching value and matching distance at each sampling moment.

[0019] Furthermore, the method for obtaining the divided time periods includes:

[0020] For any generator set, the sampling moments are clustered based on the size of the interference factor at the sampling moment of the generator set to obtain clusters; and the time period consisting of continuous sampling moments in each cluster is used as the division period.

[0021] Furthermore, the method for obtaining the interference degree includes:

[0022] For any divided time period of any generator set, the Pearson correlation coefficient between the associated component and the output current data in the divided time period is calculated as the correlation credibility of the divided time period;

[0023] Calculate the coefficient of variation of the output current data in the divided time period to obtain the interference significance index of the divided time period;

[0024] The mean value of the interference factors at all sampling moments in the divided time period is used as the interference distribution index of the divided time period;

[0025] The interference degree of the divided time period is obtained by combining the relevant credibility, interference significance index and interference distribution index of the divided time period.

[0026] Furthermore, the method for obtaining the interference period includes:

[0027] For any generator set, the divided time periods in which the interference degree of the generator set is greater than the preset interference threshold are recorded as initial interference time periods; and the continuous adjacent initial interference time periods in the generator set are merged to form interference time periods.

[0028] Furthermore, the method for obtaining the interference duration includes:

[0029] For any generator set, the maximum duration of the interference period in the generator set is taken as the maximum duration of the generator set;

[0030] The average of all interference degrees of the generator set corresponding to all interference periods is taken as the comprehensive interference degree of the generator set;

[0031] The ratio between the total duration of all interference periods in the generator set and the duration of the historical periods is taken as the comprehensive interference duration ratio of the generator set;

[0032] The interference duration of the generator set is obtained by combining the comprehensive interference degree, comprehensive interference duration ratio and maximum duration of the generator set.

[0033] Furthermore, the method for obtaining the untrustworthy weight includes:

[0034] The sampling time corresponding to the interference period of all generator sets is taken as the interference time; the preset weight value is taken as the untrustworthy weight of all non-interference time;

[0035] For any interference moment, among all interference periods of all generator sets, the interference period with the interference moment is taken as the analysis period of the interference moment;

[0036] Combine the number proportion and load ratio and value of the generator sets corresponding to all analysis periods at the interference moment to obtain the interference impact index of the generator sets at the interference moment;

[0037] According to the interference degree and duration distribution of each analysis period at the interference moment, combined with the interference duration of the generator set corresponding to the analysis period, the unreliability of each analysis period is obtained; combined with the unreliability of all analysis periods at the interference moment, the unreliability significant index of the interference moment is obtained;

[0038] The untrustworthy weight of the interference moment is obtained by combining the interference impact index and the untrustworthy significant index of the generator set at the interference moment.

[0039] Furthermore, the unreliability of each analysis period is obtained according to the interference degree and duration distribution of each analysis period at the interference moment, combined with the interference duration of the generator set corresponding to the analysis period, including:

[0040] For any analysis period of the interference moment, the product of the duration of the analysis period and the interference duration of the generator set corresponding to the analysis period is taken as the interference significance of the analysis period;

[0041] The proportion of the interference significance of the analysis period in the sum of the interference significances of all analysis periods at the interference moment is taken as the interference significance proportion of the analysis period;

[0042] Calculate the mean of all interference levels during the analysis period to obtain the average interference level during the analysis period;

[0043] The unreliability of the analysis period is obtained by combining the average interference degree and the significant interference proportion of the analysis period.

[0044] Furthermore, filtering the total output current data based on the untrustworthy weight at each sampling moment to obtain filtered current data and inputting it into the controller for control includes:

[0045] The untrustworthy weights at each sampling moment are negatively correlated and normalized to obtain the filtering weights at each sampling moment; based on the filtering weights at each sampling moment, the total output current data of the historical period is filtered using a weighted moving average algorithm to obtain filtered current data;

[0046] The filtered current data is input into the PID controller and the control instruction is output.

[0047] The present invention has the following beneficial effects:

[0048] The present invention considers that the interference of the total output current is affected by the common interference of multiple generator sets, and determines the associated components to reflect the output current situation without interference through the relevant influence of the change of the load current data in the time period on the output current component of each generator set, so that the interference factor at each sampling moment is obtained through the associated components and the output current in the subsequent time period, and more accurately reflects the interference situation of each generator set at each moment. And considering the continuous inclusion of different interferences of multiple generator sets, which leads to the complexity of the interference situation of the total output current, each generator set is analyzed in the time period, and after the time period is initially divided according to the interference factor, the output current stability of the generator set in the time period is combined with the interference factor, and the interference degree of the time period is analyzed and the interference time period is determined, and the interference situation of each generator set in the time sequence is characterized. Further, the interference duration of each generator set is determined through the comprehensive interference degree of the interference time period on the generator set, and the different interference situations of different generator sets are reflected through the time sequence continuous analysis of the interference influence of the generator set. Through the superposition of different interference periods, combined with the interference conditions of the corresponding interference period at each sampling moment, as well as the load ratio and interference duration of the corresponding generator set, the interference analysis of the total output current at each moment is made more reliable, and the untrustworthy weight is obtained to reflect the untrustworthiness of the total output current data of the comprehensive influence of multiple generator sets at each sampling moment, so that the filtering of the total output current data is more accurate, and then the controller analysis and control based on the filtered current data is more reliable. The present invention analyzes the different interference conditions of multiple generator sets in the timing, comprehensively reflects the interference degree of the total output current data at each moment, makes the filtered data more accurate, and improves the accuracy and reliability of the control analysis of the filtered data. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 A structural diagram of a remote control system for a generator set installed in a low-voltage power distribution cabinet provided by one embodiment of the present invention;

[0051] Figure 2 A flow chart of a method for obtaining an untrustworthy weight provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a remote control system for a generator set installed in a low-voltage distribution cabinet according to the present invention, 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.

[0053] 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 invention belongs.

[0054] The following is a detailed description of a specific scheme of a remote control system for a generator set installed in a low-voltage power distribution cabinet provided by the present invention in conjunction with the accompanying drawings.

[0055] See also Figure 1 , which shows a structural diagram of a generator set remote control system installed in a low-voltage distribution cabinet provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a generator set instantaneous interference analysis module 102, a generator set time period interference analysis module 103, an untrustworthy weight analysis module 104 and a filtering and control module 105.

[0056] The data acquisition module 101 is used to acquire the total output current data and total load current data of the low-voltage distribution box at each sampling moment in the historical period, as well as the output current data and load ratio of each generator set in the low-voltage distribution box.

[0057] In the embodiment of the present invention, since the multiple generator sets in the low-voltage distribution cabinet are operated in parallel, the load distribution of each generator set is fixed, that is, the load ratio borne by each generator set is constant, so the load ratio allocated to each generator set is known.

[0058] During the operation of the low-voltage distribution cabinet, remote monitoring is required to respond to load changes and grid demands during operation. In the embodiment of the present invention, control and adjustment are performed based on the current situation. However, multiple generator sets may be subject to different environmental interference situations, which makes the interference situation in the total output current complicated. Therefore, current sensors are installed to collect real-time total output current data and total load current data during the operation of the low-voltage distribution cabinet, as well as the output current data of each generator set.

[0059] In a specific implementation of an embodiment of the present invention, the historical period for monitoring and analysis is set to 3 hours, the total output current data within every three hours is filtered and then the controller is used to analyze and regulate the operation status, and the sampling frequency of the collected data is set to once per second, that is, the time interval between the sampling moments is 1 second. The implementer can adjust the specific value according to different specific implementation scenarios.

[0060] Among them, the total output current data of the low-voltage distribution cabinet at each sampling moment is the total amount of output current data of all generator sets at each sampling moment, and the total load current of the low-voltage distribution cabinet at each sampling moment is the sum of the currents consumed by each load device corresponding to the low-voltage distribution cabinet at each sampling moment.

[0061] The generator set instantaneous interference analysis module 102 is used to obtain the associated components of each generator set through the correlation between the components of the output current data and the total load current data in the historical period; and obtain the interference factor at each sampling moment according to the matching degree between the associated components and the output current data at each sampling moment.

[0062] Since the total output current data of the low-voltage distribution cabinet is subject to interference, it is a comprehensive summary of the interference conditions of each generator set, and the different levels of interference of different generator sets may also affect the comprehensive situation. Direct analysis will mask the detailed characteristics of the interference conditions of individual generator sets. Therefore, it is necessary to analyze the interference conditions of each generator set separately.

[0063] First, the interference situation at a single moment is characterized to reflect the noise interference impact on each generator set at each sampling moment. Since each generator set shares the total load in a certain proportion, the change trend of the total load current should be similar to the change trend of the output current of each generator set. Therefore, the correlation between the component and the total load current data can be used to preliminarily screen the ideal current change components, and the interference situation of the output current can be further analyzed through the ideal current change components to obtain the interference factor at each sampling moment.

[0064] Therefore, preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the associated component includes:

[0065] For any generator set, empirical mode decomposition is used on the output current data of the generator set in the historical period to obtain the IMF component of the generator set. By performing signal decomposition on the current data sequence in time series to obtain multiple components, the possible current changes under ideal interference-free conditions can be analyzed.

[0066] When the output current data is in an ideal interference-free situation, if the total load current increases, the output current of all units will increase at the same ratio. Similarly, if the total load current decreases, the output current of all units will decrease at the same ratio. That is, the change trend of the output current data of each generator set will be similar to the change trend of the total load current data.

[0067] The Pearson correlation coefficient of each IMF component and the total load current data of the generator set in the historical period is calculated to obtain the correlation of each IMF component. The correlation between the data sequences in the time series is calculated. The larger the Pearson correlation coefficient, the more positively correlated the component is with the total load current data. Finally, the IMF component with the maximum correlation is used as the associated component of the generator set. The associated component represents the distribution of the output current data of the generator set without interference.

[0068] It should be noted that the calculation of Empirical Mode Decomposition (EMD) and Pearson correlation coefficient are both well-known technical means well known to those skilled in the art, and will not be elaborated here.

[0069] The distribution difference between the associated component and the original output current data of the generator set can reflect the interference situation, and the interference impact can be quantified by the degree of deviation of data matching at each sampling moment.

[0070] Preferably, in some possible implementations of the embodiments of the present invention, the method for acquiring the interference factor at the sampling time includes:

[0071] For any generator set, the DTW algorithm is used to match the associated components and output current data of the generator set in the historical period to obtain the matching pairs at each sampling moment. In the DTW algorithm, the matching between two time series is not a simple one-to-one relationship, but allows an element in one sequence to match multiple elements in another sequence. Therefore, the matching pairs at each sampling moment are not unique.

[0072] For example, for the sequence A{A1, A2, A3}, B{B1, B2, B3}, when the matching pairs after matching through the DTW algorithm are {A1, B1}{A1, B2}{A2, B3}{A3, B3} respectively, then there are 2 groups of matching pairs {A1, B1}{A1, B2} at the corresponding time of A1, and there are 2 groups of matching pairs {A2, B3}{A3, B3} at the corresponding time of B3.

[0073] Furthermore, the number of corresponding matching pairs at each sampling moment is taken as the matching value at each sampling moment. The more matching pairs there are, the more likely interference may occur in the timing, causing the matching to shift. Conversely, when the matching pairs are only one-to-one matches, the distribution in the timing is more consistent.

[0074] Furthermore, the distance between each matching pair at each sampling moment is calculated and the average is taken to obtain the matching distance at each sampling moment. The distance between the matching pairs reflects the degree of difference between the data at the sampling moment, and the difference in multiple matching pairs is quantified by taking the average to obtain the matching distance at each sampling moment. The larger the matching distance, the more significant the difference at this time.

[0075] Finally, the interference factor of each sampling moment is obtained by combining the matching value and matching distance at each sampling moment. In the embodiment of the present invention, the product of the normalized value of the matching value at each sampling moment and the matching distance is used as the interference factor of each sampling moment. The higher the matching value and the larger the matching distance, the higher the dissimilarity of the matching and the more significant the interference effect. As an example, the expression of the interference factor is:

[0076] ; In the formula, Expressed as The first generator set The interference factor at each sampling moment is Expressed as The first generator set The matching value of the sampling time, Expressed as The first generator set The matching distance at each sampling moment, It represents a normalization function. It should be noted that normalization is a technical means well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0077] It should be noted that the DTW algorithm is a technical means well known to those skilled in the art and will not be further described here.

[0078] The generator group period interference analysis module 103 is used to obtain the divided time periods for each generator group based on the similarity of the interference factors at the sampling time; obtain the interference degree of each divided time period through the change distribution of the interference factor and the output current data in each divided time period, and the correlation degree between the associated component and the output current data; obtain the interference period according to the interference degree of the divided time period; obtain the interference duration of each generator group according to the duration distribution of the interference period on the generator group and the size of the interference degree.

[0079] Since interference usually causes periodic changes, the overall interference analysis in the time period is used to reflect the interference situation of each generator set. First, the local continuity of the interference is considered, and the preliminary time period division is carried out through the approximate situation of the interference factor in the timing, and the divided time periods are further analyzed.

[0080] Preferably, in an embodiment of the present invention, the method for obtaining the divided time periods includes:

[0081] For any generator set, the sampling moments are clustered based on the size of the interference factor at the sampling moment of the generator set to obtain cluster clusters. The sampling moments are divided into different cluster clusters according to the similarity of the interference factor. In an embodiment of the present invention, the clustering algorithm can adopt the K-means clustering algorithm. The clustering process is a technical means well known to those skilled in the art and will not be described in detail here.

[0082] Further, by obtaining time periods through clustering clusters, the continuity of time series is guaranteed while the interference factors are approximated. Therefore, the time period composed of continuous sampling moments in each clustering cluster is used as the divided time period to obtain the preliminary divided time period. The interference degree of each divided time period is further analyzed as a whole, and the significant interference is corrected by the stable change of the output current data. The more unstable the change, the greater the interference impact, which means that the interference degree is more serious.

[0083] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the interference degree includes:

[0084] For any divided time period of any generator set, the Pearson correlation coefficient between the associated component and the output current data in the divided time period is calculated as the correlation credibility of the divided time period. Under the segmented analysis, the more correlated the overall change trend between the associated component and the output current data is, the closer the change of the output current data in the time period is to the ideal situation. At this time, the specific interference analysis is closer to the actual interference situation, and therefore the credibility of the interference analysis is greater.

[0085] Further, the coefficient of variation of the output current data in the divided time period is calculated to obtain the interference significance index of the divided time period, wherein the coefficient of variation reflects the degree of discreteness of a set of data, and the larger the coefficient of variation, the higher the degree of discreteness of the data, and the higher the degree of discreteness of the output current data in the divided time period, the more obvious the interference performance, that is, the larger the interference significance index. The coefficient of variation is a well-known technology well known to those skilled in the art, and the coefficient of variation is the ratio of the standard deviation of a set of data to the mean value, which will not be elaborated here.

[0086] Furthermore, the mean value of the interference factors at all sampling moments in the divided time period is used as the interference distribution index of the divided time period. The larger the overall interference factor in the time period, the greater the interference impact.

[0087] Finally, the interference degree of the divided time period is obtained by combining the relevant credibility, interference significance index and interference distribution index of the divided time period. In an embodiment of the present invention, the product of the relevant credibility, interference significance index and interference distribution index of the divided time period is normalized to obtain the interference degree of the divided time period. The greater the relevant credibility, the higher the credibility of the interference analysis. The greater the interference significance index, the more significant the impact caused by the interference. The greater the interference distribution index, the greater the interference impact. Therefore, the greater the interference degree, the more serious the interference in the divided time period. As an example, the expression of the interference degree is:

[0088] ; In the formula, Expressed as The first generator set The interference degree of each divided time period, Expressed as The first generator set The interference significance index of each time period is Expressed as The first generator set Interference distribution index for each time period, Represents the normalization function.

[0089] The interference period can be further screened and determined by the interference degree in the divided time period, and the period with interference can be screened out for subsequent interference analysis. In the embodiment of the present invention, the interference period is obtained according to the interference degree of the divided time period, including:

[0090] For any generator set, the time period in which the interference degree of the generator set is greater than the preset interference threshold is recorded as the initial interference period. In the embodiment of the present invention, the preset interference threshold is set to 0.3. When the interference degree is greater than the preset interference threshold, it indicates that there is obvious interference in the time period, and the initial interference period is selected. The specific value can be adjusted by the implementer.

[0091] Further, the continuous adjacent initial interference time periods in the generator set are merged, and the divided time periods are updated to form interference time periods.

[0092] Based on the distribution of the overall interference period in the timing of each generator set, the degree to which each generator set is continuously affected by the interference can be analyzed. The more serious the continuous interference impact on the generator set, the greater the impact will be considered in the subsequent comprehensive analysis.

[0093] Preferably, in some possible implementations of the embodiments of the present invention, obtaining the interference duration of each generator set according to the duration distribution of the interference period on the generator set and the magnitude of the interference degree includes:

[0094] First, for any generator set, the maximum duration of the interference period in the generator set is taken as the maximum duration of the generator set. The longer the maximum duration of the interference period corresponding to the generator set is, the better the degree of continuous interference is, and the more serious the interference to the generator set is.

[0095] Secondly, the average of all interference degrees of the generator set corresponding to all interference time periods is taken as the comprehensive interference degree of the generator set, and the overall interference degree of the generator set is reflected through the size distribution of all interference degrees.

[0096] Furthermore, the ratio between the total duration of all interference periods in the generator set and the duration of the historical periods is used as the comprehensive interference duration ratio of the generator set. The proportion of the total interference duration reflects the duration of the generator being affected by the interference.

[0097] Finally, the interference duration of the generator set is obtained by combining the comprehensive interference degree, comprehensive interference duration ratio and maximum duration of the generator set. In the embodiment of the present invention, the product of the comprehensive interference degree, comprehensive interference duration ratio and maximum duration of the generator set is used as the interference duration of the generator set, and is combined in the form of multiplication. When the comprehensive interference degree and comprehensive interference duration are larger, it means that the degree of interference to the generator set is greater and more serious. When the maximum duration is larger, it means that the continuity of the degree of interference to the generator set is better. Therefore, the greater the interference duration, the greater the comprehensive degree of continuous interference to the generator set in the time period.

[0098] The untrustworthy weight analysis module 104 is used to obtain the untrustworthy weight at each sampling moment based on the distribution of each interference period corresponding to all generator sets at each sampling moment, combined with the load ratio and interference duration of the generator set corresponding to the interference period, and the interference duration and duration distribution in the interference period.

[0099] Based on the interference period distribution of all generator sets, the credibility of the current data at each sampling moment is analyzed. The more serious the overall generator set interference distribution at the sampling moment, the less credible the total output current data at this moment, and the more likely it is that it needs to be corrected during subsequent filtering.

[0100] When analyzing at each sampling moment, the distribution of interference time periods is combined with the load share of the generator set to reflect the degree of influence on the initial proportional distribution of the total output current. The duration of the interference period corresponding to the generator set and the interference degree are analyzed based on the duration of the interference to obtain the unreliable weight. The unreliable situation of the total output current data is characterized by comprehensively analyzing the interference conditions of multiple generator sets.

[0101] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the untrustworthy weight is described in Figure 2 , which shows a flow chart of a method for obtaining an untrustworthy weight provided by an embodiment of the present invention, the method comprising the following steps:

[0102] S401: The sampling time corresponding to the interference period of all generator sets is taken as the interference time; and the preset weight value is taken as the untrustworthy weight of all non-interference time.

[0103] First, the moments corresponding to the interference periods of the generator sets under the sampling moments are screened out as interference moments. The interference moments exist in at least one interference period of a generator set, and an unreliable interference situation analysis is required.

[0104] As for the non-interference moment, it means that there is no interference period of the generator set at this sampling moment, so the total output current data at this sampling moment is completely reliable, and the preset weight value is used as the unreliable weight of the non-interference moment. In an embodiment of the present invention, the preset weight value is set to zero, and its value is adjusted by the implementer.

[0105] S402: For any interference moment, among all interference time periods of all generator sets, the interference time period in which the interference moment exists is used as the analysis time period of the interference moment.

[0106] Since the interference moment may not only exist in the interference period of one generator set, but may also be affected by the common interference of the interference periods of multiple generator sets, it is necessary to analyze each interference period corresponding to the interference moment, and use the interference period existing at the interference moment as the analysis period of the interference moment for subsequent analysis.

[0107] S403: Obtain the interference impact index of the generator set at the interference moment by combining the number ratio and load ratio and value of the generator sets corresponding to all analysis periods at the interference moment.

[0108] From the analysis of the proportion of generator sets corresponding to the interference moment, the higher the proportion of quantity and load ratio, the more aspects of the total output current data at this moment are affected by the interference, and the lower the credibility of the total output current data corresponding to the interference moment.

[0109] In the embodiment of the present invention, the ratio of the number of generator sets corresponding to all analysis periods at the interference moment to the total number of all generator sets is used as the number ratio at the interference moment. The sum of the load proportions of the generator sets corresponding to all analysis periods at the interference moment is used as the load proportion sum value at the interference moment.

[0110] Finally, the product of the number proportion and the load proportion and value at the interference moment is taken as the interference impact index of the generator set at the interference moment. The higher the number proportion and the larger the load proportion and value, the more the interference moment is affected by the generator set. Therefore, the larger the generator set interference impact index is, the higher the degree of unreliability of the interference moment is.

[0111] S404: According to the interference degree and duration distribution of each analysis period at the interference moment, combined with the interference duration of the generator set corresponding to the analysis period, the unreliability of each analysis period is obtained; combined with the unreliability of all analysis periods at the interference moment, the unreliability significance index of the interference moment is obtained.

[0112] Secondly, the interference degree of each analysis period corresponding to the interference moment is analyzed in detail. The heavier the interference degree in the analysis period corresponding to the interference moment, the higher the unreliability of this interference moment. Therefore, the unreliability is analyzed from the interference degree based on the length and interference degree of the analysis period, combined with the adjustment of the overall interference duration of the generator set.

[0113] Preferably, in an embodiment of the present invention, the method for obtaining unreliability includes:

[0114] First, for any analysis period at the moment of interference, the product of the length of the analysis period and the interference duration of the generator set corresponding to the analysis period is taken as the interference significance of the analysis period. The longer the continuous interference of the generator set itself, the longer the analysis period is, which means that the interference brought by this analysis period is more stable, and the impact on the total current data is more significant and real.

[0115] Furthermore, the proportion of the interference significance of the analysis period in the sum of the interference significance values ​​of all analysis periods at the interference moment is taken as the interference significance proportion of the analysis period. The comprehensive proportion of the interference degree of each analysis period at the interference moment is reflected by the significant proportion of all interference significances at the interference moment.

[0116] Furthermore, the mean of all interference degrees in the analysis period is calculated to obtain the average interference degree of the analysis period. Since the analysis period, that is, the interference period, is the period after the divided period is updated, there may be multiple interference degrees in each analysis period. The mean calculation is used to reflect the distribution of interference degrees in the analysis period. The larger the average interference degree, the greater the degree of interference in this analysis period.

[0117] Finally, the unreliability of the analysis period is obtained by combining the average interference degree and the significant interference proportion of the analysis period. In an embodiment of the present invention, the product of the average interference degree and the significant interference proportion is used as the unreliability of the analysis period. The larger the average interference degree and the significant interference proportion are, the more significant and serious the interference situation in the analysis period is, and the higher the unreliability is.

[0118] Combined with the unreliability of all analysis time periods at the interference moment, an unreliable significant index of the interference moment is obtained. In an embodiment of the present invention, the sum of the unreliability of all analysis time periods at the interference moment is used as the unreliable significant index of the interference moment. The unreliability of all analysis time periods is comprehensively considered to reflect the degree of unreliability of the interference moment.

[0119] S405: Combining the interference impact index and the untrustworthy significant index of the generator set at the interference moment, obtaining the untrustworthy weight at the interference moment.

[0120] By comprehensively analyzing the influence of the generator group participation at the interference moment and the influence of the interference degree during the interference period, the untrustworthy weight of the interference moment is obtained. In the embodiment of the present invention, the product of the generator group interference influence index and the untrustworthy significant index at the interference moment is used as the untrustworthy weight of the interference moment. The larger the untrustworthy weight, the greater the influence of the interference on the total output current data at the corresponding sampling moment, and the more untrustworthy the data. As an example, the expression of the untrustworthy weight is:

[0121] ; In the formula, Expressed as The untrustworthy weight of the sampling moment, Expressed as The proportion of the number of sampling moments, Expressed as The load ratio and value at each sampling moment, Expressed as The total number of analysis periods for sampling moments, Expressed as At the sampling time The significant proportion of interference in the analysis period, Expressed as The sampling time The average interference level during the analysis period, Expressed as The sampling time The unreliability of the analysis period, Expressed as The unreliable and significant indicators at the sampling time, Expressed as The interference impact index of the generator set at each sampling moment.

[0122] The filtering and regulating module 105 is used to filter the total output current data based on the untrustworthy weight at each sampling moment, and obtain the filtered current data to input into the controller for control.

[0123] After obtaining the untrustworthy weights of each sampling moment in the historical period, filtering by weight can obtain more accurate filtered data, thereby making the control analysis more reliable.

[0124] In the embodiment of the present invention, the untrustworthy weight at each sampling moment is negatively correlated and normalized to obtain the filtering weight at each sampling moment. The smaller the untrustworthy weight is, the more important the proportion of the corresponding data is during filtering. As an example, the expression of the filtering weight is:

[0125] ; In the formula, Expressed as The filter weights at sampling time, Expressed as The untrustworthy weight of the sampling moment, Represents the normalization function.

[0126] Based on the filtering weight at each sampling moment, the total output current data of the historical period is filtered using a weighted moving average algorithm to obtain filtered current data. It should be noted that the method of filtering using the weighted moving average algorithm is a formula technology well known to technicians in this field and will not be described in detail here.

[0127] Finally, the filtered current data is input into the PID controller, which outputs control instructions to complete the remote control of the generator set in the low-voltage distribution cabinet. The PID controller adjusts the output power of the generator set by outputting control instructions to respond to load changes and grid demand, thereby maintaining the voltage and frequency within a predetermined range.

[0128] In summary, the present invention considers that the interference of the total output current is affected by the common interference of multiple generator sets, and determines the associated components to reflect the output current situation without interference through the relevant influence of the change of the load current data in the time period on the output current component of each generator set, so that the interference factor at each sampling moment is obtained through the associated components and the output current in the future, which more accurately reflects the interference situation of each generator set at each moment. And considering the continuous mixing of different interferences of multiple generator sets, which leads to the complexity of the interference situation of the total output current, each generator set is analyzed in the time period, and after the time period is initially divided according to the interference factor, the output current stability of the generator set in the time period is combined with the interference factor to analyze the interference degree of the time period and determine the interference period, characterizing the interference situation of each generator set in the time sequence, and further determining the interference duration of each generator set through the comprehensive interference degree of the interference period on the generator set, and reflecting the different interference situations of different generator sets through the time sequence continuous analysis of the interference influence of the generator set. Through the superposition of different interference periods, combined with the interference conditions of the corresponding interference period at each sampling moment, as well as the load ratio and interference duration of the corresponding generator set, the interference analysis of the total output current at each moment is made more reliable, and the untrustworthy weight is obtained to reflect the untrustworthiness of the total output current data of the comprehensive influence of multiple generator sets at each sampling moment, so that the filtering of the total output current data is more accurate, and then the controller analysis and control based on the filtered current data is more reliable. The present invention analyzes the different interference conditions of multiple generator sets in the timing, comprehensively reflects the interference degree of the total output current data at each moment, makes the filtered data more accurate, and improves the accuracy and reliability of the control analysis of the filtered data.

[0129] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. 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.

[0130] The various embodiments in this specification 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.

Claims

1. A remote control system for a generator set installed in a low-voltage distribution cabinet, characterized in that: The system comprises: A data acquisition module is used to obtain the total output current data and total load current data of the low-voltage distribution box at each sampling time in the historical period, as well as the output current data and load ratio of each generator set in the low-voltage distribution box; The generator set instantaneous interference analysis module is used to obtain the associated components of each generator set through the correlation between the components of the output current data and the total load current data in the historical period; and obtain the interference factor at each sampling moment according to the matching degree between the associated components and the output current data at each sampling moment; The generator set period interference analysis module is used to obtain the divided period for each generator set according to the similarity of the interference factor at the sampling time; obtain the interference degree of each divided period through the change distribution of the interference factor and the output current data in each divided period, and the correlation degree between the associated component and the output current data; obtain the interference period according to the interference degree of the divided period; obtain the interference duration of each generator set according to the duration distribution of the interference period on the generator set and the interference degree; The untrustworthy weight analysis module is used to obtain the untrustworthy weight at each sampling moment based on the distribution of each interference period corresponding to all generator sets at each sampling moment, combined with the load proportion and interference duration of the generator set corresponding to the interference period, as well as the interference duration and duration distribution in the interference period; A filtering and control module is used to filter the total output current data based on the untrustworthy weight at each sampling moment, and obtain the filtered current data to input into the controller for control; The method for obtaining the associated component includes: For any generator set, the output current data of the generator set in the historical period is subjected to empirical mode decomposition to obtain the IMF component of the generator set; Calculate the Pearson correlation coefficient between each IMF component and the total load current data of the generator set in the historical period to obtain the correlation of each IMF component; The IMF component with the largest correlation is taken as the correlation component of the generator set; The method for obtaining the untrustworthy weight includes: The sampling time corresponding to the interference period of all generator sets is taken as the interference time; the preset weight value is taken as the untrustworthy weight of all non-interference time; For any interference moment, among all interference periods of all generator sets, the interference period with the interference moment is taken as the analysis period of the interference moment; Combine the number proportion and load ratio and value of the generator sets corresponding to all analysis periods at the interference moment to obtain the interference impact index of the generator sets at the interference moment; According to the interference degree and duration distribution of each analysis period at the interference moment, combined with the interference duration of the generator set corresponding to the analysis period, the unreliability of each analysis period is obtained; combined with the unreliability of all analysis periods at the interference moment, the unreliability significant index of the interference moment is obtained; The untrustworthy weight of the interference moment is obtained by combining the interference impact index and the untrustworthy significant index of the generator set at the interference moment.

2. According to claim 1, a remote control system for a generator set installed in a low-voltage distribution cabinet is characterized in that: The method for obtaining the interference factor includes: For any generator set, the associated components and output current data of the generator set in the historical period are matched using the DTW algorithm to obtain matching pairs at each sampling time; The number of corresponding matching pairs at each sampling moment is taken as the matching value at each sampling moment; Calculate the distance between each matching pair at each sampling time and find the average to obtain the matching distance at each sampling time; The interference factor at each sampling moment is obtained by combining the matching value and matching distance at each sampling moment.

3. According to claim 1, a remote control system for a generator set installed in a low-voltage power distribution cabinet is characterized in that: The method for obtaining the divided time periods includes: For any generator set, the sampling moments are clustered based on the size of the interference factor at the sampling moment of the generator set to obtain clusters; and the time period consisting of continuous sampling moments in each cluster is used as the division period.

4. According to claim 1, a remote control system for a generator set installed in a low-voltage power distribution cabinet is characterized in that: The method for obtaining the interference degree includes: For any divided time period of any generator set, the Pearson correlation coefficient between the associated component and the output current data in the divided time period is calculated as the correlation credibility of the divided time period; Calculate the coefficient of variation of the output current data in the divided time period to obtain the interference significance index of the divided time period; The mean value of the interference factors at all sampling moments in the divided time period is used as the interference distribution index of the divided time period; The interference degree of the divided time period is obtained by combining the relevant credibility, interference significance index and interference distribution index of the divided time period.

5. According to claim 1, a remote control system for a generator set installed in a low-voltage power distribution cabinet is characterized in that: The method for obtaining the interference period includes: For any generator set, the divided time periods in which the interference degree of the generator set is greater than the preset interference threshold are recorded as initial interference time periods; and the continuous adjacent initial interference time periods in the generator set are merged to form interference time periods.

6. According to claim 1, a remote control system for a generator set installed in a low-voltage power distribution cabinet is characterized in that: The method for obtaining the interference duration includes: For any generator set, the maximum duration of the interference period in the generator set is taken as the maximum duration of the generator set; The average of all interference degrees of the generator set corresponding to all interference periods is taken as the comprehensive interference degree of the generator set; The ratio between the total duration of all interference periods in the generator set and the duration of the historical periods is taken as the comprehensive interference duration ratio of the generator set; The interference duration of the generator set is obtained by combining the comprehensive interference degree, comprehensive interference duration ratio and maximum duration of the generator set.

7. According to claim 1, a remote control system for a generator set installed in a low-voltage power distribution cabinet is characterized in that: The unreliability of each analysis period is obtained according to the interference degree and duration distribution of each analysis period at the interference moment, combined with the interference duration of the generator set corresponding to the analysis period, including: For any analysis period of the interference moment, the product of the duration of the analysis period and the interference duration of the generator set corresponding to the analysis period is taken as the interference significance of the analysis period; The proportion of the interference significance of the analysis period in the sum of the interference significances of all analysis periods at the interference moment is taken as the interference significance proportion of the analysis period; Calculate the mean of all interference levels during the analysis period to obtain the average interference level during the analysis period; The unreliability of the analysis period is obtained by combining the average interference degree and the significant interference proportion of the analysis period.

8. According to claim 1, a remote control system for a generator set installed in a low-voltage power distribution cabinet is characterized in that: The method of filtering the total output current data based on the untrustworthy weight at each sampling moment, obtaining the filtered current data and inputting it into the controller for control, comprises: The untrustworthy weights at each sampling moment are negatively correlated and normalized to obtain the filtering weights at each sampling moment; based on the filtering weights at each sampling moment, the total output current data of the historical period is filtered using a weighted moving average algorithm to obtain filtered current data; The filtered current data is input into the PID controller and the control instruction is output.

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