Power distribution system power monitoring cloud platform based on mutual inductor

Abstract

The invention relates to the technical field of power monitoring, in particular to a power distribution system power monitoring cloud platform based on a mutual inductor, which comprises a feature acquisition module used for acquiring operation data of a power distribution system in real time and extracting feature vectors of the power distribution system during patrol monitoring; the feature vectors comprise specific values of the current and the voltage, average power of the current and the voltage, phase difference of the current and the voltage, and autocorrelation coefficients and partial correlation coefficients of the current and the voltage; the feature detection module is used for detecting the operation data corresponding to the feature vector, and obtaining a prediction probability and a prediction classification corresponding to the feature vector during detection; the classification judgment module is used for judging and identifying the prediction classification, and determining a detection processing mode of the prediction classification when a fault occurs and a probability feature of the detection processing mode; and the reliability and the stability of the power distribution system are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power monitoring, and in particular to an electric power monitoring cloud platform for a power distribution system based on a transformer. Background Art

[0002] As an important infrastructure in modern society, the power system plays a vital supporting role in industry, commerce and residents' lives. As one of the core components in the power system, the current transformer undertakes key tasks such as current measurement, protection and control, which is of great significance to the safe and stable operation of the power system. However, the traditional current transformer maintenance method has some limitations and challenges, which makes it face a series of problems in practical applications.

[0003] For example, Chinese patent publication number CN117216673A discloses a current transformer monitoring, evaluation and maintenance platform, including a multimodal sensor module, a monitoring module, a pre-training module, a state evaluation module, a maintenance decision correction module and a historical state database; the monitoring module is used to remotely receive data collected by the multimodal sensor module, and store and output the data; the historical state database stores the historical operating parameters of the current transformer and the corresponding state evaluation results; the pre-training module trains a support vector machine model according to the data in the historical state database to obtain a classifier; the state evaluation module evaluates the state of the current transformer based on the collected data; the prior art adjusts the current transformer current evaluation method at this time by judging the state category, but the prior art ignores the correlation between current and voltage, as well as the occurrence of corresponding faults, resulting in the lack of judgment on the overall state in the state evaluation result after identifying the corresponding category.

[0004] For example, Chinese patent publication number CN111475929A discloses an inversion verification method based on monitoring data of a distribution network real-life test platform, the method comprising: constructing a recording system according to the actual physical topology of the real-life test platform; obtaining voltage data and current data of all nodes; obtaining the voltage positive-sequence component, current positive-sequence component and voltage zero-sequence component and current zero-sequence component of all nodes by calculation; obtaining the positive-sequence impedance and zero-sequence impedance between all nodes by calculation; obtaining the equivalent impedance of the π model between all nodes by calculation; constructing a digital simulation circuit of the real-life test platform based on the equivalent impedance of the π model between all nodes; based on the new circuit impedance, performing fault simulation and waveform reproduction through the digital simulation circuit of the real-life test platform; the prior art performs inverse verification through the components of current and voltage and impedance, and can find the corresponding magnitude value of the current, but when the current is abnormal, its period cannot be adjusted, resulting in a decrease in the overall efficiency and accuracy of the inverse verification. Summary of the invention

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a power monitoring cloud platform for distribution system based on mutual inductor, including: a feature acquisition module, used to collect the operation data of the distribution system in real time, and extract the feature vector of the distribution system during patrol monitoring; the feature vector includes the specific value of current and voltage, the average power of current and voltage, the phase difference of current and voltage, the autocorrelation coefficient and partial correlation coefficient of current and voltage.

[0006] The feature detection module is used to detect the operating data corresponding to the feature vector and obtain the predicted probability and predicted classification corresponding to the feature vector during detection.

[0007] The classification judgment module is used to judge and identify the predicted classification and determine the detection and processing method of the predicted classification when a fault occurs and the probability characteristics of the detection and processing method.

[0008] The cycle adjustment module is used to verify the detection processing method, determine the monitoring cycle and interval cycle selected by each detection processing method, and adjust the monitoring cycle size according to the corresponding probability characteristics of the detection processing method.

[0009] The correction and maintenance module is used to correct the abnormal fluctuations of each detection and processing method when a fault occurs, determine the correction category of the corrected fault, and repair the equipment according to the correction category.

[0010] The beneficial effects of the present invention are as follows: the present invention enables the system to timely discover and handle potential faults through real-time acquisition and feature extraction functions, thereby improving monitoring efficiency; improves the accuracy and reliability of fault prediction by integrating multiple feature vectors for prediction and classification; dynamically adjusts the monitoring cycle according to the system state and fault probability characteristics, thereby reducing monitoring costs and improving monitoring efficiency; provides a scientific basis for equipment maintenance by correcting abnormal fluctuations of various detection and processing methods during faults and determining correction categories, thereby reducing maintenance costs and downtime; improves the reliability and stability of the distribution system, and ensures the safe operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0012] Figure 1 It is a system framework diagram of a power distribution system power monitoring cloud platform based on transformers.

[0013] Figure 2 It is a system flow chart of a power distribution system power monitoring cloud platform based on transformers.

[0014] Figure 3 The present invention is a flowchart of prediction and classification in a feature detection module of a power monitoring cloud platform for a power distribution system based on a transformer.

[0015] Figure 4 The invention is a flow chart of obtaining a detection processing method in a classification judgment module of a power distribution system power monitoring cloud platform based on a transformer. DETAILED DESCRIPTION

[0016] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. If no specific techniques or conditions are specified in the embodiments, the techniques or conditions described in the literature in the art or the product specifications are used.

[0017] See also Figure 1 , Figure 2 , a power distribution system power monitoring cloud platform based on mutual inductor, including: feature acquisition module, feature detection module, classification judgment module, period adjustment module, correction and maintenance module.

[0018] The feature acquisition module collects the operating data of the distribution system in real time, extracts the feature vector, and passes the extracted feature vector to the feature detection module; the feature detection module detects the operating data corresponding to the feature vector, obtains the predicted probability and predicted classification, and passes the predicted probability and predicted classification to the classification judgment module; the classification judgment module judges and identifies the predicted classification, determines the detection processing method and its probability characteristics, and passes the determined detection processing method and its probability characteristics to the cycle adjustment module; the cycle adjustment module verifies the detection processing method, determines the monitoring period and interval period, and adjusts the monitoring period according to the probability characteristics, and feeds back the adjusted monitoring period and interval period to the feature acquisition module so that data can be collected according to the new period; the correction and maintenance module corrects the abnormal fluctuations of each detection processing method during a fault, determines the correction category, performs maintenance according to the correction category, provides maintenance suggestions and / or directly controls the maintenance equipment to operate.

[0019] The feature collection module is used to collect the operating data of the distribution system in real time and extract the feature vectors of the distribution system during inspection and monitoring.

[0020] The feature detection module is used to detect the operating data corresponding to the feature vector and obtain the predicted probability and predicted classification corresponding to the feature vector during detection.

[0021] The classification judgment module is used to judge and identify the predicted classification and determine the detection and processing method of the predicted classification when a fault occurs and the probability characteristics of the detection and processing method.

[0022] The cycle adjustment module is used to verify the detection processing method, determine the monitoring cycle and interval cycle selected by each detection processing method, and adjust the monitoring cycle size according to the corresponding probability characteristics of the detection processing method.

[0023] The correction and maintenance module is used to correct the abnormal fluctuations of each detection and processing method when a fault occurs, determine the correction category of the corrected fault, and repair the equipment according to the correction category.

[0024] The operating data obtained here include the current and voltage data when the distribution system is running, and the characteristic vectors of the current and voltage data are extracted according to the current waveform and voltage waveform to obtain the corresponding changes of current and voltage when patrolling and monitoring the distribution system.

[0025] The main purpose is to conduct regular inspection and maintenance of the distribution system to prevent corresponding equipment failures, identify possible conditions after identifying the corresponding feature vectors, and express this condition in a predictive manner, such as using predictive classification and predictive probability to obtain possible faults or other problems under the current feature vector; and determine the processing methods that can be used at this time, and which detection and processing method is more suitable for processing, and adjust each monitoring cycle to adjust the monitoring frequency at this time; this monitoring cycle also needs to be used to process those parts that are considered abnormal but ineffective after tripping or inspection. These will indicate that there may be problems, but unclear faults are monitored, so as to complete the correction of faults or problems; finally, these specific faults are corrected, and the maintenance and monitoring of the platform are completed according to the corresponding correction categories.

[0026] For example, the center frequency and signal bandwidth of the operating data are obtained, and the average power and phase difference of the operating data are calculated according to the center frequency and signal bandwidth, and the average power and phase difference are used as feature information of the feature vector.

[0027] At the same time, it is necessary to calculate the autocorrelation coefficient and partial correlation coefficient of the operating data within the corresponding time, and use the autocorrelation coefficient and partial correlation coefficient as the feature information of the feature vector.

[0028] Therefore, the characteristic vector output at this time includes the specific value of the current and voltage, the average power of the current and voltage, the phase difference between the current and voltage, and the autocorrelation coefficient and partial correlation coefficient of the current and voltage.

[0029] The center frequency is represented by the frequency of the power grid, such as 50Hz or 60Hz; the signal bandwidth is represented by the range of frequency components in the current and voltage. This value can be obtained by a spectrum analyzer to obtain the center frequency and signal bandwidth of the corresponding current and voltage.

[0030] As for the autocorrelation coefficient and the partial correlation coefficient, the autocorrelation coefficient and the partial correlation coefficient of the current and voltage at this time are determined by arranging the collected current and voltage data in the form of a time series using the setting method of the lag stage.

[0031] For example, the autocorrelation coefficient can be expressed as follows: first, the operating data is converted into a time series, a lag stage is set for the time series, and the autocorrelation coefficient is calculated according to the lag stage.

[0032] ;in, represents the autocorrelation coefficient when the lag stage is k, Indicates the value of the operating data at time t. This value indicates the value of the current and voltage corresponding to time t in the time series. Represents the value of the running data at time tk, Indicates the average value of the operating data. In this case, it indicates the average value of the corresponding current and voltage in the time series. Indicates the length of the time series, the value range of t is 1 to n, Indicates the lag phase.

[0033] For the partial correlation coefficient, after calculating the autocorrelation coefficient, a recursive calculation is performed to obtain the partial correlation coefficient.

[0034] ;in, represents the partial correlation coefficient when the lag stage is k, represents the autocorrelation coefficient when the lag stage is k, represents the autocorrelation coefficient when the lag stage is kj, represents the partial correlation coefficient when the lag stage is j, represents the lag phase, and the value of j ranges from 1 to k-1; ,in represents the partial correlation coefficient when the lag stage is 1, Autocorrelation coefficient when the lag phase is 1.

[0035] After the autocorrelation coefficient is calculated, the part greater than the initial threshold is selected as the feature vector for subsequent processing. The initial threshold is set to 0.05. When the autocorrelation coefficient and partial correlation coefficient are greater than 0.05, it means that there is a significant change. When it is greater than 0.2, it means that the current and voltage at this time conform to normal changes. The purpose of selecting 0.05 is to identify the abnormality of the current distribution system, but it does not show the specific situation.

[0036] In one embodiment of the present invention, the feature detection module is used to detect the operating data corresponding to the feature vector and obtain the predicted probability and predicted classification corresponding to the feature vector during the detection.

[0037] The predicted probability and predicted category of the feature vector are the output results in the classification task, which respectively represent the confidence that a feature vector belongs to each category and the final classification result.

[0038] The predicted probability obtained at this time represents the probability value of the currently obtained feature vector relative to each classification. When obtaining the predicted probability, the spectral features of the feature vector are first extracted, and the spectral features are subjected to autocorrelation analysis and partial correlation analysis to obtain the final output predicted classification.

[0039] like Figure 3 As shown, the method for obtaining the prediction classification is: classify the feature information in the feature vector in turn, and obtain the specific values ​​of current and voltage, average power and phase difference corresponding to the first classification information in turn; the first classification information tends to obtain the operation status of current and voltage in the overall process, such as whether the current and voltage are stable, whether there are obvious fault problems, and whether the phase difference of current and voltage is normal, and the influence of the inductance in the current transformer, etc., to quantify the state of the distribution system at this time.

[0040] The second classification information corresponding to the autocorrelation coefficient and partial correlation coefficient of the current and voltage is obtained; the second classification information is used to better capture the structure of the current and voltage with respect to time changes, to determine which situation the current and voltage tend to change, so as to obtain a more accurate prediction probability.

[0041] In general, the predicted probability and predicted classification of the feature vector are obtained by comparing the feature information in the feature vector with the data set in the historical data, determining the confidence of the feature information, and selecting the part with the maximum confidence value as the predicted classification. This confidence is also regarded as the predicted probability.

[0042] In the present invention, when obtaining the confidence of feature information, the feature information is combined to obtain a comprehensive confidence, and the comprehensive confidence is divided into categories to obtain the classification that best corresponds to the current situation, and the probability values ​​of the first classification information and the second classification information are calculated respectively.

[0043] For example, the probability value of the first classification information is obtained as follows: calculate the fitting probability value corresponding to the specific value of the current and voltage, calculate the power probability value corresponding to the average power of the current and voltage, and calculate the phase probability value corresponding to the phase difference of the current and voltage, and use the comprehensive value of the fitting probability value, power probability value and phase probability value as the probability value of the first classification information.

[0044] The fitting probability value is obtained according to the change of the current and voltage in the corresponding time series by obtaining the slope value corresponding to the current and voltage and calculating the distribution of the slope value.

[0045] The slope value corresponding to the current and voltage is expressed as follows: ;in, Indicates the slope value corresponding to the current and voltage, represents the value of the running data at time t, It represents the length of the time series, and the value range of t is 1 to n. The calculated slope value is used to identify whether the current and voltage show a stable trend or a dispersed trend under normal operating conditions, so as to identify whether the distribution system is operating normally at this time.

[0046] The fitting probability value is expressed as shown below. The slope value corresponding to the current and voltage is obtained, and the fitting slope value is calculated.

[0047] ;in, represents the fitting probability value, Indicates the slope value corresponding to the current and voltage, Represents an exponential constant.

[0048] The power probability value is expressed as: the average power corresponding to the current and voltage is obtained, and the power probability value is calculated.

[0049] ;in, represents the power probability value, represents the average power corresponding to the current and voltage, , Represents the cross-validation parameter, and the set values ​​are 0.1 and -1 respectively.

[0050] The phase probability value is expressed as the ratio of the phase difference between the current and voltage to the corresponding standard phase difference in the historical data; the standard phase difference is the average value of the phase difference in the historical data.

[0051] The probability value of the first classification information is expressed as: ;in, represents the probability value of the first classification information, represents the phase probability value; Represents a probability constant, which is expressed as a value between 0 and 1. The value of the probability constant is expressed as the joint probability value of the slope value, average power, and phase difference corresponding to the current current and voltage appearing in the historical data. The joint probability value is different from the numerical values ​​corresponding to the fitting probability value, power probability value, and phase probability value calculated above. It is used to quantify whether the final classification can meet the corresponding requirements when performing comprehensive division and classification at this time. These classifications are obtained by comprehensively considering multiple conditions. For example, the joint probability value tends to consider the number of times the corresponding value appears in the population, while the fitting probability value and power probability value tend to consider the probability distribution of the corresponding value, and the phase probability value tends to consider the ratio of the corresponding value to the standard value. These values ​​will represent the comprehensive situation under different conditions, and are classified in turn according to these identified conditions to obtain more specific sub-classification categories under each category.

[0052] The second classification information is obtained based on the probability values ​​of the autocorrelation coefficient and the partial correlation coefficient of the current and voltage. The probability values ​​of the autocorrelation coefficient and the partial correlation coefficient are used as the probability values ​​of the second classification information. The probability values ​​of the autocorrelation coefficient and the partial correlation coefficient are expressed as the ratio of the number of occurrences of the corresponding autocorrelation coefficient and the partial correlation coefficient in the historical data to the total number of occurrences; the probability value of the second classification information is the product of the probability values ​​of the autocorrelation coefficient and the partial correlation coefficient.

[0053] The probability values ​​of the first classification information and the second classification information are calculated respectively, the probability values ​​of the first classification information and the second classification information are combined, and the classification with the largest probability value after the combination is selected as the output prediction classification.

[0054] The probability value obtained by combining the first classification information and the second classification information is the predicted probability corresponding to the feature vector.

[0055] The probability values ​​of the first classification information and the second classification information are combined by weighted averaging the first classification information and the second classification information, with the weights being 0.7 and 0.3 respectively.

[0056] When the combined probability value is obtained, multiple probability values ​​will be obtained according to the selected categories, and the largest probability value will be selected as the predicted category output at this time.

[0057] In one embodiment of the present invention, the classification judgment module is used to judge and identify the predicted classification, and determine the detection and processing method of the predicted classification when a fault occurs and the probability characteristics of the detection and processing method.

[0058] At this time, the detection and processing method means the processing measures adopted for a certain prediction classification after obtaining it, as well as the program flow corresponding to the processing measures; for example, when the prediction classification corresponding to the problems such as tripping, abnormal current fluctuation, abnormal current consumption, etc. is identified, the detection and processing method at this time is to alarm, record logs, and adjust the corresponding current and voltage.

[0059] The detection and processing method is obtained by determining the fault attributes corresponding to each prediction classification according to the obtained prediction classification. The fault attributes include fault type, fault cause, and processing suggestions.

[0060] The predicted classification is mapped with the fault attribute to generate a category attribute comparison table. The category attribute comparison table is used to map the information contained in the predicted classification into a comparison table to represent the possible fault problems under the current predicted classification.

[0061] According to the category attribute comparison table, the fault attributes are analyzed in turn to determine the detection and processing method under the predicted classification.

[0062] For example, the category attribute comparison table can be shown as follows.

[0063] Prediction classification: normal; fault type: none; fault cause: none; handling suggestion: no action required.

[0064] Prediction classification: overload; fault type: overload; fault cause: excessive current; handling suggestions: check the load and reduce the current.

[0065] Prediction classification: short circuit; fault type: short circuit; fault cause: circuit short circuit; handling suggestions: disconnect the power supply and check the circuit.

[0066] Prediction classification, grounding; fault type, grounding; fault cause, poor grounding; handling suggestions, check the grounding wire and ensure good grounding.

[0067] When the category attribute comparison table is obtained, we can clearly know the fault attribute that the current predicted classification will correspond to. At the same time, since the predicted classification represents the overall relative state, it is necessary to verify whether the data in the predicted classification at this time completely matches the fault attribute to know the detection and processing method to be taken.

[0068] like Figure 4 As shown, the fault attributes are analyzed in sequence according to the category attribute comparison table, and the implementation method of determining the detection processing method under the predicted classification can be as shown below.

[0069] The category attribute comparison table is constructed into a first analysis pair equal to the number of fault attributes. The first analysis pair is used to generate multiple analysis pairs from the attribute comparison table according to the number of fault attributes. Each first analysis pair includes a fault attribute and a setting rating of the fault attribute. The setting rating represents the matching degree between the current fault attribute and the predicted classification.

[0070] The matching degree between the fault attribute and the prediction classification is determined by dividing the fault by the time of occurrence of the fault, determining the duration and impact range of the fault in the fault attribute, and setting the matching degree between the fault attribute and the prediction classification according to the duration and impact range of the fault.

[0071] For example, the duration of a fault is expressed as the ratio of the duration value to the average duration value of the corresponding fault attribute, and this ratio is recorded as the index value of the duration of the fault; the impact range of the fault is expressed as the ratio of the number of system components affected by the fault to the number of all system components, and this ratio is regarded as the index value of the impact range of the fault; the two are weighted and summed to obtain the matching degree between the fault attribute and the predicted classification.

[0072] ;in, represents the matching degree between the fault attribute and the predicted classification, An indicator value indicating the duration of the current fault. Indicates the index value of the impact range of the current fault. The minimum value of the indicator indicating the duration of the fault. The minimum value of the indicator value indicating the impact range of the fault, The maximum value of the indicator value representing the duration of the fault, The maximum value of the indicator value indicating the impact range of the fault. represents the weight of the fault duration, The weight representing the impact range of the fault, Represents an exponential factor, which is a constant greater than 1 and is used to amplify the impact of the current duration and impact range. For example, the exponential factor can be 1.4 or the maximum of the ratios of the duration and impact range of the current fault to the minimum index value is selected as the exponential factor selected at this time. The maximum and minimum values ​​selected at this time are obtained by finding the values ​​of the corresponding fault attributes in the historical data. The weights used can be set to 0.6 and 0.4 in the order of duration and impact range.

[0073] The category attribute comparison table generates a second analysis pair according to the fault type. The number of the second analysis pairs is consistent with the number of existing fault types. The second analysis pairs include fault types and risk probabilities. The second analysis pairs are used to analyze the risk probabilities existing under different fault types, that is, the probability of the fault type occurring under the content included in the category attribute comparison table, so as to verify the degree of verification of the fault occurring at this time.

[0074] The risk probability is the ratio of the number of failures of the current fault type to the total number of failures, or the ratio of the number of failures of the current fault type to the total number of failures in the current category attribute comparison table. Both values ​​can reflect the relative status of the current fault when it occurs.

[0075] The category attribute comparison table is used to generate a third analysis pair according to the fault cause. The third analysis pair includes the fault cause and the fault classification probability. The fault classification probability represents the probability of the fault cause being classified into the corresponding fault type. The fault classification probability will be stored in the database. When the category attribute comparison table is mapped to the corresponding fault cause, the fault classification probability will be obtained at the same time to determine the probability of the classification of the fault cause at this time, thereby quantifying the specific fault situation at this time.

[0076] The first analysis pair, the second analysis pair, and the third analysis pair are compared with the data of fault attributes, fault types, and fault causes in the historical data in turn, and the fault support and fault confidence of the first analysis pair, the second analysis pair, and the third analysis pair are calculated respectively. When the fault support and the fault confidence are both greater than the preset threshold, the detection processing method existing in the historical data is used as the output detection processing method.

[0077] The fault support and fault confidence here represent the support and confidence of normal calculation, and the preset thresholds are set to 0.6 to obtain a list of detection processing methods that meet the preset thresholds; that is, when calculating the fault support and fault confidence of the three analysis pairs, the support and confidence of each analysis pair must reach the preset threshold, and then the corresponding historical data will be selected to obtain the corresponding detection processing method.

[0078] After outputting the detection and processing method, it is also necessary to determine the probability characteristics of the detection and processing method at this time. The probability characteristics are expressed as the duration, impact range and occurrence frequency of the fault. The duration and impact range of the fault are obtained through the first analysis pair in the category attribute comparison table. The occurrence frequency is obtained based on the number of times the fault occurs in a unit time. For example, the unit time is set to 1 week to obtain the occurrence frequency of the corresponding fault.

[0079] In one embodiment of the present invention, the cycle adjustment module is used to verify the detection processing mode, determine the monitoring cycle and interval cycle selected by each detection processing mode, and adjust the monitoring cycle size according to the probability characteristics corresponding to the detection processing mode.

[0080] At this time, the detection cycle and interval cycle are directly obtained through the detection processing method. These cycles are pre-set and will be included in the detection processing method. For example, when the values ​​of fault frequency, duration and impact range are detected to be too large or too small, the monitoring cycle is adjusted to dynamically adjust the monitoring cycle.

[0081] The monitoring cycle is the time interval between the start of monitoring and the next monitoring. In other words, it is the length of time for each monitoring operation. The monitoring cycle determines how long the system will perform a complete data collection and analysis.

[0082] The interval period refers to the frequency or interval time of data collection during the monitoring period. It determines how often the system collects data during the monitoring period.

[0083] The main focus here is on adjusting the monitoring cycle, and different interval cycles are adjusted. The interval cycle only affects the collection interval, while the monitoring cycle affects the overall processing and analysis. According to the adjustment of the monitoring cycle, the overall processing interval and prediction preparation can be adjusted synchronously, thereby improving the sensitivity of power monitoring.

[0084] For example, the method of adjusting the monitoring period size is as follows.

[0085] ;in, represents the adjusted monitoring period, Indicates the initial value of the monitoring period, represents the exponential constant, Indicates the fault frequency index value of the current fault. This index value indicates the normalized fault frequency. An indicator value indicating the duration of the current fault. Indicates the index value of the impact range of the current fault. The average value of the indicator value representing the fault frequency of the current fault, The average value of the indicator value representing the duration of the current fault, The average value of the indicator value that represents the impact range of the current fault. The average values ​​of the corresponding indicator values ​​are obtained by extracting the average values ​​from the historical data.

[0086] The final output monitoring cycle is expressed as a time length value to indicate how often a complete data analysis and processing is performed.

[0087] In one embodiment of the present invention, the correction and maintenance module is used to correct the abnormal fluctuations of each detection and processing mode when a fault occurs, determine the correction category of the corrected fault, and repair the equipment according to the correction category.

[0088] This is used to correct the current and voltage when a fault occurs. The correction method is based on the acquired detection and processing method. At the same time, the correction category indicates the fault type after the detection and processing method is integrated to indicate the repair result required for the current operating status of the power distribution system. For example, the correction category indicates that the identified fault type and fault cause are corrected, a new fault description is added, or the classified fault type is adjusted and optimized, so that the system can accurately identify and discover new fault types.

[0089] The method for correcting abnormal fluctuations of various detection and processing methods during a fault is to splice the detection and processing methods, select the one with the largest indicator value among the detection and processing methods as the splicing center, superimpose each detection and processing method with the splicing center, and output the superimposed splicing center as the maximum splicing center.

[0090] The index values ​​in the detection and processing methods here include the setting rating of fault attributes, fault type and risk probability, fault cause and fault classification probability, and the values ​​of the corresponding steps when the detection and processing methods are implemented. The splicing method at this time is to select the one with the largest comprehensive index value as the splicing center, and perform weighted averaging of all the detection and processing methods in turn to achieve superposition processing. The superimposed splicing center will be a more accurate, more robust or more reliable data representation, which can be used for subsequent decision-making, analysis or further processing.

[0091] After obtaining the maximum splicing center, the correction category is extracted based on the maximum splicing center. At this time, the indicator value of the maximum splicing center will be selected, and the maximum value of the fault type in the maximum splicing center will be used as the correction category at this time.

[0092] Finally, the whole system is corrected according to the obtained correction category to obtain the final correction category, and the equipment is repaired according to the obtained correction category.

[0093] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention and they are still covered by the protection scope of the present invention.

Claims

1. A power distribution system power monitoring cloud platform based on transformer, characterized in that: include: The feature acquisition module is used to collect the operation data of the distribution system in real time and extract the feature vector of the distribution system during inspection and monitoring; the feature vector includes the specific value of current and voltage, the average power of current and voltage, the phase difference of current and voltage, the autocorrelation coefficient and partial correlation coefficient of current and voltage; A feature detection module is used to detect the operating data corresponding to the feature vector and obtain the prediction probability and prediction classification corresponding to the feature vector during the detection; The classification judgment module is used to judge and identify the predicted classification, determine the detection and processing method of the predicted classification when a fault occurs, and the probability characteristics of the detection and processing method; The cycle adjustment module is used to verify the detection processing method, determine the monitoring cycle and interval cycle selected by each detection processing method, and adjust the monitoring cycle size according to the corresponding probability characteristics of the detection processing method; The correction and maintenance module is used to correct the abnormal fluctuations of each detection and processing method when a fault occurs, determine the correction category of the corrected fault, and repair the equipment according to the correction category.

2. The power distribution system power monitoring cloud platform based on mutual inductor according to claim 1 is characterized in that: The method of obtaining the prediction classification is as follows: classifying the characteristic information in the characteristic vector in sequence, and obtaining the specific value of the current and voltage, the average power and the first classification information corresponding to the phase difference in sequence; Obtaining second classification information corresponding to the autocorrelation coefficient and partial correlation coefficient of current and voltage; The probability values ​​of the first classification information and the second classification information are calculated respectively, the probability values ​​of the first classification information and the second classification information are combined, and the classification with the largest probability value after the combination is selected as the output prediction classification.

3. The power distribution system power monitoring cloud platform based on mutual inductor according to claim 2 is characterized in that: The probability value of the first classification information is obtained as follows: Calculate the fitting probability value corresponding to the specific value of the current and voltage, calculate the power probability value corresponding to the average power of the current and voltage, and calculate the phase probability value corresponding to the phase difference of the current and voltage, and use the comprehensive value of the fitting probability value, the power probability value and the phase probability value as the probability value of the first classification information; The probability value of the second classification information is the product of the probability values ​​of the autocorrelation coefficient and the partial correlation coefficient.

4. The power distribution system power monitoring cloud platform based on mutual inductor according to claim 3 is characterized in that: The fitting probability value is expressed as follows. The slope value corresponding to the current and voltage is obtained, and the fitting slope value is calculated: ； in, represents the fitting probability value, Indicates the slope value corresponding to the current and voltage, represents the exponential constant; The power probability value is expressed as follows: Get the average power corresponding to the current and voltage, and calculate the power probability value: ； in, represents the power probability value, represents the average power corresponding to the current and voltage, , represents the cross validation parameters; The phase probability value is expressed as the ratio of the phase difference between the current and voltage to the corresponding standard phase difference in the historical data; The probability value of the first classification information is expressed as: ； in, represents the probability value of the first classification information, represents the phase probability value; represents a probability constant.

5. The power distribution system power monitoring cloud platform based on mutual inductor according to claim 1 is characterized in that: The detection and processing method is obtained by determining the fault attributes corresponding to each prediction classification according to the obtained prediction classification, and the fault attributes include fault type, fault cause, and processing suggestion; Map the predicted classification with the fault attributes to generate a category attribute comparison table; According to the category attribute comparison table, the fault attributes are analyzed in turn to determine the detection and processing method under the predicted classification.

6. The power distribution system power monitoring cloud platform based on mutual inductor according to claim 5 is characterized in that: According to the category attribute comparison table, the fault attributes are analyzed in turn, and the detection and processing methods under the prediction classification are determined as follows: The category attribute comparison table is used to construct first analysis pairs equal to the number of fault attributes, each first analysis pair includes a fault attribute and a setting rating of the fault attribute, and the setting rating represents the matching degree between the current fault attribute and the predicted classification; The category attribute comparison table generates second analysis pairs according to the fault type, the number of the second analysis pairs is consistent with the number of existing fault types, and the second analysis pairs include the fault type and the risk probability; The category attribute comparison table generates a third analysis pair according to the fault cause, the third analysis pair including the fault cause and the fault classification probability; The first analysis pair, the second analysis pair, and the third analysis pair are compared with the data of fault attributes, fault types, and fault causes in the historical data in turn, and the fault support and fault confidence of the first analysis pair, the second analysis pair, and the third analysis pair are calculated respectively. When the fault support and the fault confidence are both greater than the preset thresholds, the detection processing method existing in the historical data is used as the output detection processing method.

7. The power distribution system power monitoring cloud platform based on mutual inductor according to claim 6 is characterized in that: The matching degree between fault attributes and predicted classification is expressed as: ； in, represents the matching degree between the fault attribute and the predicted classification, An indicator value indicating the duration of the current fault. Indicates the index value of the impact range of the current fault. The minimum value of the indicator indicating the duration of the fault. The minimum value of the indicator value indicating the impact range of the fault, The maximum value of the indicator value representing the duration of the fault, The maximum value of the indicator value indicating the impact range of the fault. represents the weight of the fault duration, The weight of the impact range of the fault, Represents the exponential factor.

8. The power distribution system power monitoring cloud platform based on mutual inductor according to claim 7 is characterized in that: Probabilistic characteristics are expressed as the duration, impact scope and frequency of occurrence of the fault.

9. The power distribution system power monitoring cloud platform based on mutual inductor according to claim 7 is characterized in that: The way to adjust the monitoring period size is as follows: ； in, represents the adjusted monitoring period, Indicates the initial value of the monitoring period, represents the exponential constant, The index value indicating the fault frequency of the current fault, The average value of the indicator value representing the fault frequency of the current fault, The average value of the indicator value representing the duration of the current fault, Indicates the average value of the indicator value of the impact range of the current fault.

10. The power distribution system power monitoring cloud platform based on mutual inductor according to claim 1, characterized in that: The method for correcting the abnormal fluctuation of each detection and processing method during a fault is: The detection processing methods are spliced, the one with the largest index value among the detection processing methods is selected as the splicing center, each detection processing method is superimposed with the splicing center, and the superimposed splicing center is output as the maximum splicing center; The maximum value of the fault type in the largest splicing center is taken as the correction category at this time.

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