Remote electric energy meter fault detection and identification system and method

Abstract

The invention relates to the technical field of fault detection, and discloses a remote electric energy meter fault detection and identification system and method, and the system comprises a data collection module, a data integration module, a data inspection module, a feature extraction module, a fault analysis module, and a fault alarm module. According to the method, multiple electric energy meter target data are collected and integrated into a corresponding data set, and a complete and ordered data basis is provided for analysis; abnormal values are recognized and replaced by calculating the average value and the standard deviation, the data quality is improved, and the accurate analysis result is guaranteed; key parameters covering multiple aspects are accurately extracted from a data set, and a powerful basis is provided for fault analysis; from metering, hardware and communication fault dimensions, according to comparison of extracted parameters and threshold values, fault types and positions are accurately judged in multiple directions; the fault alarm module remotely transmits an analysis result to a monitoring center, and generates an alarm message containing detailed information such as an electric energy meter number, a fault type, a parameter value, a threshold value and the like when a fault occurs, thereby facilitating timely maintenance by maintenance personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and in particular to a remote electric energy meter fault detection and identification system and method. Background Art

[0002] In the operation and management of power systems, accurate measurement and stable operation of remote energy meters are crucial. With the continuous expansion and increasing complexity of power networks, monitoring and fault detection of energy meters face many challenges.

[0003] Traditional methods for fault detection of electric energy meters often have limitations. On the one hand, data collection is not comprehensive enough, and may only focus on a few parameters, and fail to obtain complete information on the operating status of the electric energy meter, resulting in some potential faults being difficult to detect. For example, relying solely on simple voltage or current readings cannot fully reflect the working conditions of the electric energy meter in terms of active power, reactive power, frequency, and other aspects. On the other hand, in the data analysis link, there is a lack of effective outlier processing mechanisms. Because the electric energy meter is affected by multiple factors such as a complex electromagnetic environment, the collected data may contain outliers. If these outliers cannot be accurately identified and processed, it will seriously interfere with subsequent fault judgments and cause deviations in fault diagnosis results.

[0004] At the same time, most of the existing fault analysis methods are not accurate and systematic enough. For different types of faults such as metering failures, hardware failures and communication failures, it is not possible to conduct a comprehensive analysis from a multi-dimensional parameter perspective. This may lead to the inability to accurately distinguish the type of fault, such as it is difficult to accurately determine whether it is a basic metering component failure, a power metering failure, or a voltage or current related hardware failure, or a communication link failure, etc., so that targeted maintenance measures cannot be taken in a timely and effective manner, affecting the reliability of power supply and the accuracy of metering, and increasing power operation costs and management difficulties. Therefore, a more comprehensive, accurate and efficient remote electric energy meter fault detection and identification system and method is needed to solve these problems. Summary of the invention

[0005] The purpose of the present invention is to provide a remote electric energy meter fault detection and identification system and method, which solves the technical problems raised in the background technology.

[0006] The purpose of the present invention can be achieved by the following technical solutions: A remote electric energy meter fault detection and identification system, comprising: A data collection module is used to collect various target data from a remote electric energy meter, including real-time voltage data, real-time current data, active power data, reactive power data, electric energy meter operating frequency data, power supply voltage ripple data, and communication delay data; The data integration module is used to classify and organize the collected target data; the classification and organization method is to group the data of the same type into data sets; The data inspection module is used to perform outlier inspection on the data set, detect outliers in different types of data sets through outlier inspection, and then replace the outliers to obtain a new data set; Feature extraction module, used to extract key parameters from the data set after outlier check processing; The fault analysis module is used to perform fault analysis based on the key parameters extracted by the feature extraction module, and determine whether the electric energy meter has a metering fault, a hardware fault, or a communication fault through fault analysis; The fault alarm module is used to send the fault analysis results of the fault analysis module to the monitoring center through the remote communication network and display them to relevant maintenance personnel.

[0007] As a further solution of the present invention: in the data integration module, the data set is as follows: Voltage data set V = {v 1 , v 2 , ...v n}; Current data set K = {k 1 , k 2 , ...k n}; Active power data set P = {p 1 , p 2 , ... p n}; Reactive power data set Q = {q 1 ,q 2 , ... q n}; Frequency data set F = {f 1 , f 2 , ...f n}; Ripple data set R={r 1 , r 2 , ...r n}; Communication delay dataset D = {d 1 , d 2 , ...d n}; Among them, v i Indicates the voltage value obtained at different acquisition times, i=1, 2, ... n, n is the number of acquisition points, k i represents the current value obtained at different acquisition times, p i Indicates the power value obtained at different acquisition times, f iRepresents the frequency value obtained at different acquisition times, r i Indicates the ripple value obtained at different acquisition times, d i Indicates the communication delay values ​​obtained at different collection times.

[0008] As a further solution of the present invention: the abnormal value check processing method is as follows: StepA1, select the voltage data set; Then calculate all v i The average value Pv; At the same time, calculate all v i The standard deviation of Bv; StepA2: The v collected at each collection time i Combine Pv and Bv to analyze the corresponding v at each collection time i Whether it is an outlier; If v i -Pv>α×Bv, then the corresponding v i Mark as outlier; If v i -Pv≤α×Bv, then the corresponding v i Mark as outlier; Among them, α is the preset multiple value; StepA3: Extract all v except outliers i , and calculate its average value, and then use its average value as the replacement value of the outlier; After replacing the corresponding abnormal values ​​with replacement values, a new voltage data set is formed; In this way, outlier check processing is performed on data sets composed of different types of data, and new data sets are obtained.

[0009] As a further solution of the present invention: the extraction method of the feature extraction module is as follows: Step 1. Voltage parameter extraction: Step 1.1, voltage effective value calculation: The voltage effective value is calculated by summing the squares of all voltage values ​​in the voltage data set, taking the average and then taking the square root; The formula is: Where V rms is the effective value of voltage; Step 1.2, voltage peak calculation: By extracting the maximum value from all voltage values ​​in the voltage data set and recording it as the voltage peak value V peak ; Step 1.3, voltage change slope calculation: By calculating the ratio of the voltage difference to the time difference at each adjacent moment, the average value of the ratio is calculated and recorded as the voltage change slope; The formula is: In the formula, K V is the voltage change slope, t i is the timestamp of the corresponding collection time node; Step 2. Current parameter extraction Step 2.1, current effective value calculation: The effective value of the current is calculated by summing the squares of all current values ​​in the current data set, taking the average and then taking the square root; The formula is: In the formula, K rms is the effective value of voltage; Step 2.2, Calculation of the ratio of current peak value to effective value: By extracting the maximum value from all current values ​​in the current data set and recording it as the current peak value K peak ; Then the peak current K peak Divide by the voltage RMS K rms , calculate the ratio of the current peak value to the effective value; The formula is: In the formula, R K is the ratio of the current peak value to the effective value; Step 2.3, calculation of current phase difference change rate: By calculating the ratio of the current phase angle difference to the time difference at each adjacent moment, the average value of the ratio is calculated, and the average value is recorded as the current phase difference change rate; The formula is: In the formula, is the voltage change slope, t i is the timestamp of the corresponding collection time node; Step 3. Power parameter extraction: Step 3.1, calculation of the ratio of active power to reactive power: The ratio of active power to reactive power is obtained by dividing the average value of all active power values ​​in the active power data set P by the average value of all reactive power values ​​in the reactive power data set Q; The formula is: In the formula, R PQ is the ratio of active power to reactive power, P0 is the average value of all active power values ​​in the active power data set P, and Q0 is the average value of all reactive power values ​​in the reactive power data set Q; Step 3.2, calculation of active power fluctuation rate: By calculating the ratio of the standard deviation of all active powers in the active power data set P to the average value, and recording the ratio as the active power fluctuation rate; The formula is: In the formula, is the active power fluctuation rate corresponding to the active power data set P; Step 3.3, calculation of reactive power fluctuation rate: By calculating the ratio of the standard deviation of all reactive powers in the reactive power data set Q to the average value, and recording the ratio as the reactive power fluctuation rate; The formula is: In the formula, is the reactive power fluctuation rate corresponding to the reactive power data set Q; Step 4. Extraction of working frequency parameters of electric energy meter: By calculating the ratio of the standard deviation of all frequencies in the frequency data set F to the mean value, and recording the ratio as the frequency stability; The formula is: In the formula, S F is the frequency stability corresponding to the frequency data set F, and F0 is the average value of all frequencies in the frequency data set F; Step 5. Extraction of power supply voltage ripple parameters: In the ripple data set R, extract the ripple parameter with the largest value and record it as the ripple voltage peak value V rpp ; At the same time, the DC voltage component is obtained and marked as V dc ; Then the ripple factor is calculated by dividing the ripple voltage peak by the DC voltage component; The formula is: In the formula, K R is the ripple factor; Step 6. Communication delay parameter extraction: By calculating the mean and standard deviation of all communication delay values ​​in the communication delay data set F; The formula is: Where D0 is the average value of all communication delay values ​​in the communication delay data set F, is the standard deviation of all communication delay values ​​in the communication delay dataset F.

[0010] As a further solution of the present invention: In a voltage signal containing multiple frequency components or complex waveforms, the DC voltage component refers to the voltage portion of the signal that does not change periodically over time. In simple terms, if the voltage signal is regarded as being generated by a DC power supply and an AC power supply connected in series, then the voltage value corresponding to the DC power supply is the DC voltage component.

[0011] As a further solution of the present invention: the fault analysis method is as follows: Step C1, measurement failure analysis: Step C1.1, judging whether the basic metering element is faulty by comparing whether the difference between the effective value of voltage and the rated voltage, and the effective value of current and the rated current exceeds the corresponding preset error threshold; If |V rms -V rated |>VY and |K rms -K rated |>If any one of KY is true, it is judged that the electric energy meter has a basic metering element failure; Otherwise, it is judged that there is no basic metering component failure in the electric energy meter; Among them, V rated is the rated voltage of the energy meter, VY is the preset voltage error threshold, K rated is the rated current, KY is the current error threshold; Step C1.2, determine the power metering failure by analyzing the ratio of active power to reactive power and the fluctuation rate of active power and reactive power respectively; If | R PQ -R0 PQ |>RY PQ , > and > If at least one of the following conditions holds true, it is determined that the electric energy meter has a power measurement fault; If | R PQ -R0 PQ |>RY PQ , > and > If all of them are not true, it is judged that the electric energy meter does not have power measurement failure; Among them, R0 PQ is the normal ratio of active power to reactive power, RY PQ is the preset ratio difference threshold, is the preset active power fluctuation rate threshold, is the reactive power fluctuation rate threshold; Step C2, hardware failure analysis: StepC2.1, if V peak >VY peak and K V >KY V If all of them are true, it is determined that the electric energy meter has a voltage-related hardware fault; Otherwise, it is determined that the electric energy meter does not have voltage-related hardware failure; Among them, VY peak is the preset voltage peak threshold, KY V is the preset voltage change slope threshold Step C2.2, if R K >RY K and > If all of them are true, it is determined that the electric energy meter has a current-related hardware fault; Otherwise, it is judged that there is no current-related hardware fault; Among them, RY K is the preset current peak value to effective value ratio threshold, is the preset current phase difference change rate threshold; Step C2.3, if S F >SY F , it is judged that there is a fault in the internal clock or frequency module of the electric energy meter; Otherwise, it is determined that there is no fault in the internal clock or frequency generation module of the electric energy meter; Among them, SY F is the preset frequency stability threshold; Step C2.4, if K R >KY R , it is judged that the power module of the electric energy meter is faulty; Otherwise, it is judged that there is no power module fault in the electric energy meter; Among them, KY R is the preset ripple factor threshold; StepC3. Communication failure analysis: By measuring the mean and standard deviation of the communication delay, it is determined whether the communication link is faulty; If D0>D0Y and > If all of them are true, it is judged that there is a communication link failure in the electric energy meter; Otherwise, it is determined that there is no communication link failure in the electric energy meter; Where D0Y is the preset communication delay mean threshold, is the preset communication delay standard deviation threshold.

[0012] As a further solution of the present invention: when the fault analysis module detects a fault, the fault alarm module will be quickly activated and generate an alarm message containing detailed fault information; Among them, the alarm message content includes the identification number of the electricity meter, the fault type corresponding to the metering fault, hardware fault, and communication fault, specific parameter values ​​such as the effective value of voltage and the effective value of current, and the fault-related parameter values ​​corresponding to the corresponding thresholds.

[0013] A remote electric energy meter fault detection and identification method, the method is implemented by the remote electric energy meter fault detection and identification system, the method comprises the following steps: Step 1: Data Collection Collect various target data from remote power meters; Step 2: Data Integration Classify and organize the collected target data, and group the data of the same type into data sets; Step 3: Data Check Perform outlier check on the data set to detect outliers in different types of data sets, then replace the outliers and get a new data set; Step 4: Feature extraction Extract key parameters from the dataset processed by outlier check; Step 5: Fault Analysis Perform fault analysis based on the key parameters extracted by the feature extraction module to determine whether the electric energy meter has metering faults, hardware faults, and communication faults. Step 6: Fault Alarm The fault analysis results of the fault analysis module are sent to the monitoring center through the remote communication network and displayed to relevant maintenance personnel.

[0014] Beneficial effects of the present invention: 1. Comprehensive data collection and integration: It can collect a variety of target data from remote energy meters, including real-time voltage, current, active power, reactive power, energy meter operating frequency, power supply voltage ripple, communication delay data, etc. The data integration module combines the same type of data into a data set, providing a comprehensive and orderly data foundation for subsequent analysis and processing, which is conducive to a comprehensive understanding of the operating status of the energy meter.

[0015] 2. Accurate outlier processing: The data inspection module can perform outlier inspection on the data set. By calculating the mean and standard deviation, and combining the preset multiple value to determine the outlier, and then replace it. This method can effectively eliminate abnormal interference in the data, improve data quality, and make subsequent data-based analysis results more accurate and reliable.

[0016] 3. Accurate and effective extraction of key parameters: The feature extraction module can accurately extract key parameters from the processed data set, covering the calculation of parameters such as voltage, current, power, operating frequency of the energy meter, power supply voltage ripple and communication delay. These parameters provide a strong basis for fault analysis. For example, the characteristics of the energy meter under different working conditions are fully characterized through parameters such as voltage RMS, peak value, change slope, current RMS, peak-to-RMS ratio, phase difference change rate, etc.

[0017] 4. Accurate fault analysis: The fault analysis module performs fault analysis based on the extracted key parameters, and can accurately determine whether the electric energy meter has metering faults, hardware faults, and communication faults. In metering fault analysis, the difference between the effective value and the rated value of voltage and current, as well as the ratio and fluctuation rate of active power and reactive power are compared to make judgments; in hardware fault analysis, the voltage and current related hardware, internal clock or frequency generation module, power supply module and other different parts are judged based on the comparison of corresponding parameters with thresholds; in communication fault analysis, the mean and standard deviation of communication delay are used to judge whether the communication link is faulty. This multi-dimensional and accurate fault analysis method can quickly locate the fault type and location.

[0018] 5. Timely and effective fault alarm: The fault alarm module can send the fault analysis results to the monitoring center through the remote communication network to show it to the maintenance personnel. When a fault is detected, it can quickly start and generate an alarm message containing detailed fault information, which helps maintenance personnel to take targeted maintenance measures in time, reduce the losses and impacts caused by the fault of the energy meter, and improve the efficiency and quality of the operation and maintenance of the energy meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings.

[0020] Figure 1 The present invention is a system block diagram of a remote electric energy meter fault detection and identification system.

[0021] Figure 2 The present invention is a flowchart of a remote electric energy meter fault detection and identification method. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Embodiment 1 See also Figure 1 and Figure 2 As shown, the present invention is a remote electric energy meter fault detection and identification system and method, comprising: A data collection module for collecting various target data from remote electric energy meters; Target data includes: Real-time voltage data: key data used to reflect the voltage status of the energy meter, which can reflect the voltage level of the circuit to which the energy meter is connected in real time; Real-time current data: Similar to voltage data, current data is crucial to understanding the working status of the energy meter, as it is directly related to the current in the circuit; Active power data: Active power is the power part of electric energy actually consumed, and is one of the important indicators for measuring user power consumption; Reactive power data: Reactive power reflects the power part of the circuit that is used to establish electromagnetic fields and other non-actual work, which is of great significance for analyzing the power characteristics of the circuit; Energy meter operating frequency data: The operating frequency of the energy meter determines the accuracy of its internal timing, measurement and other related functions. The stability of the frequency directly affects the overall performance of the energy meter; Power supply voltage ripple data: Power supply voltage ripple is an important reflection of power supply quality. Large ripple may interfere with the normal operation of the energy meter. in, The target data collection time interval is set to T seconds, and the value of T is determined comprehensively based on the performance of the electric energy meter and the communication conditions; For example, when the processing capability of the energy meter is strong and the communication network is relatively stable, T can be set to a relatively small value, such as 5 seconds; on the contrary, if the performance of the energy meter is limited or the communication conditions are poor, T may need to be set to a larger value, such as 10 seconds. Such a design can ensure data validity while avoiding excessive burden on the energy meter and the communication network. The data integration module is used to classify and organize the collected target data; the classification and organization method is to group the data of the same type into data sets; The dataset consists of the following: Voltage data set V = {v 1 , v 2, ...v n}; Current data set K = {k 1 , k 2 , ...k n}; Active power data set P = {p 1 , p 2 , ... p n}; Reactive power data set Q = {q 1 ,q 2 , ... q n}; Frequency data set F = {f 1 , f 2 , ...f n}; Ripple data set R={r 1 , r 2 , ...r n}; Among them, v i Indicates the voltage value obtained at different acquisition times, i=1, 2, ... n, n is the number of acquisition points, k i represents the current value obtained at different acquisition times, p i Indicates the power value obtained at different acquisition times, f i Represents the frequency value obtained at different acquisition times, r i Indicates the ripple value obtained at different acquisition times; Feature extraction module, used to extract key parameters from the data set after outlier check processing; The specific method is as follows: Step 1. Voltage parameter extraction: Step 1.1, voltage effective value calculation: The voltage effective value is calculated by summing the squares of all voltage values ​​in the voltage data set, taking the average and then taking the square root; The formula is: Where V rms is the effective value of voltage; In this embodiment, the effective value of voltage is a measure of the value of the equivalent DC voltage of voltage in one cycle, which can more accurately reflect the actual effect of voltage on the electric energy meter and the load; Step 1.2, voltage peak calculation: By extracting the maximum value from all voltage values ​​in the voltage data set and recording it as the voltage peak value V peak ; In this embodiment, the voltage peak value is of great significance for determining whether the voltage has abnormal spikes or the like; Step 1.3, voltage change slope calculation: By calculating the ratio of the voltage difference to the time difference at each adjacent moment, the average value of the ratio is calculated and recorded as the voltage change slope; The formula is: In the formula, K V is the voltage change slope, t i is the timestamp of the corresponding collection time node; Step 2. Current parameter extraction Step 2.1, current effective value calculation: The effective value of the current is calculated by summing the squares of all current values ​​in the current data set, taking the average and then taking the square root; The formula is: In the formula, K rms is the effective value of voltage; In this embodiment, the effective value of current is a measure of the value of the equivalent direct current of the current in one cycle, which is of great significance for analyzing the current characteristics in the circuit and the current measurement accuracy of the electric energy meter; Step 2.2, Calculation of the ratio of current peak value to effective value: By extracting the maximum value from all current values ​​in the current data set and recording it as the current peak value K peak ; Then the peak current K peak Divide by the voltage RMS K rms , calculate the ratio of the current peak value to the effective value; The formula is: In the formula, R K is the ratio of the current peak value to the effective value; In this embodiment, the ratio can reflect the waveform characteristics of the current, and different types of loads and circuit faults may cause the ratio to change. For example, in the case of a nonlinear load, the ratio may deviate from a normal range; Step 2.3, calculation of current phase difference change rate: By calculating the ratio of the current phase angle difference to the time difference at each adjacent moment, the average value of the ratio is calculated, and the average value is recorded as the current phase difference change rate; The formula is: In the formula, is the voltage change slope, t i is the timestamp of the corresponding collection time node; In this embodiment, the current phase difference change rate can reflect the change of the current phase over time, which is important for analyzing the power factor change, three-phase imbalance and other problems in the circuit; Step 3. Power parameter extraction: Step 3.1, calculation of the ratio of active power to reactive power: The ratio of active power to reactive power is obtained by dividing the average value of all active power values ​​in the active power data set P by the average value of all reactive power values ​​in the reactive power data set Q; The formula is: In the formula, R PQ is the ratio of active power to reactive power, P0 is the average value of all active power values ​​in the active power data set P, and Q0 is the average value of all reactive power values ​​in the reactive power data set Q; In this embodiment, the ratio of active power to reactive power is of great significance for evaluating the power characteristics of the circuit and the power measurement accuracy of the electric energy meter. Under different load types and circuit conditions, the ratio has a specific range. Exceeding the normal range may indicate a fault in the power measurement. Step 3.2, calculation of active power fluctuation rate: By calculating the ratio of the standard deviation of all active powers in the active power data set P to the average value, and recording the ratio as the active power fluctuation rate; The formula is: In the formula, is the active power fluctuation rate corresponding to the active power data set P; In this embodiment, the active power fluctuation rate reflects the fluctuation degree of the active power within a period of time. A larger fluctuation rate may indicate that there are unstable factors in the circuit or that the power metering element of the electric energy meter is faulty.

[0024] Step 3.3, calculation of reactive power fluctuation rate: By calculating the ratio of the standard deviation of all reactive powers in the reactive power data set Q to the average value, and recording the ratio as the reactive power fluctuation rate; The formula is: In the formula, is the reactive power fluctuation rate corresponding to the reactive power data set Q; In this embodiment, the reactive power fluctuation rate is used to measure the fluctuation of reactive power, which plays an important role in analyzing circuit problems related to reactive power and electric energy meter failures; Step 4. Extraction of working frequency parameters of electric energy meter: By calculating the ratio of the standard deviation of all frequencies in the frequency data set F to the mean value, and recording the ratio as the frequency stability; The formula is: In the formula, S F is the frequency stability corresponding to the frequency data set F, and F0 is the average value of all frequencies in the frequency data set F; In this embodiment, frequency stability is a key parameter to measure the stability of the working frequency of the electric energy meter, which reflects the fluctuation of the frequency over a period of time. A higher frequency stability is essential to ensure the timing accuracy and measurement accuracy of the electric energy meter. If the frequency stability exceeds the normal range, it may indicate that the internal clock or frequency generation module of the electric energy meter is faulty; Step 5. Extraction of power supply voltage ripple parameters: In the ripple data set R, extract the ripple parameter with the largest value and record it as the ripple voltage peak value V rpp ; At the same time, the DC voltage component is obtained and marked as V dc ; Then the ripple factor is calculated by dividing the ripple voltage peak by the DC voltage component; The formula is: In the formula, K R is the ripple factor; Among them, in a voltage signal containing multiple frequency components or complex waveforms, the DC voltage component refers to the voltage part of the signal that does not change periodically over time. Simply put, if the voltage signal is regarded as being generated by a DC power supply and an AC power supply connected in series, then the voltage value corresponding to the DC power supply is the DC voltage component; In this embodiment, the ripple coefficient can intuitively reflect the magnitude of the power supply voltage ripple; a larger ripple coefficient may interfere with the normal operation of the electric energy meter and even damage the hardware of the electric energy meter; A fault analysis module is used to perform fault analysis based on key parameters extracted by the feature extraction module; The specific method is as follows: Step C1, measurement failure analysis: Step C1.1, judging whether the basic metering element is faulty by comparing whether the difference between the effective value of voltage and the rated voltage, and the effective value of current and the rated current exceeds the corresponding preset error threshold; If |V rms -V rated |>VY and |K rms -K rated |>If any one of KY is true, it is judged that the electric energy meter has a basic metering element failure; Otherwise, it is judged that there is no basic metering component failure in the electric energy meter; Among them, V rated is the rated voltage of the energy meter, VY is the preset voltage error threshold, K rated is the rated current, KY is the current error threshold; In this embodiment, when the effective value of voltage or current deviates from the rated value and exceeds the allowable range, it is likely that there is a problem with the voltage and current measuring elements inside the electric energy meter, such as a decrease in the accuracy of the voltage transformer and the current transformer.

[0025] Step C1.2, determine the power metering failure by analyzing the ratio of active power to reactive power and the fluctuation rate of active power and reactive power respectively; If | R PQ -R0 PQ |>RY PQ , > and > If at least one of the following conditions holds true, it is determined that the electric energy meter has a power measurement fault; If | R PQ -R0 PQ |>RY PQ , > and > If all of them are not true, it is judged that the electric energy meter does not have power measurement failure; Among them, R0 PQ is the normal ratio of active power to reactive power, RY PQ is the preset ratio difference threshold, is the preset active power fluctuation rate threshold, is the reactive power fluctuation rate threshold; In this embodiment, when the ratio deviates from the normal range or the fluctuation rate of both the active power and the reactive power is too large, it may indicate that there is a fault in the power metering part of the electric energy meter, such as a power chip failure or a related circuit connection problem.

[0026] Step C2, hardware failure analysis: StepC2.1, if V peak >VY peak and K V >KY V If all of them are true, it is determined that the electric energy meter has a voltage-related hardware fault; Otherwise, it is determined that the electric energy meter does not have voltage-related hardware failure; Among them, VY peak is the preset voltage peak threshold, KY V is the preset voltage change slope threshold In this embodiment, voltage-related hardware failures include but are not limited to voltage transformer problems. When the voltage peak is too high and the voltage change slope is abnormally large, it may be that voltage-related hardware such as the voltage transformer has a fault, such as insulation damage, core saturation, etc., resulting in abnormal voltage signals; Step C2.2, if R K >RY K and > If all of them are true, it is determined that the electric energy meter has a current-related hardware fault; Otherwise, it is judged that there is no current-related hardware fault; Among them, RY K is the preset current peak value to effective value ratio threshold, is the preset current phase difference change rate threshold; In this embodiment, current-related hardware failures include but are not limited to current transformer failures. When the current peak value to effective value ratio and the current phase difference change rate exceed the normal threshold, it may be that current-related hardware such as the current transformer has a fault, such as turn-to-turn short circuit, abnormal core magnetization curve, etc.; Step C2.3, if S F >SY F , it is judged that there is a fault in the internal clock or frequency module of the electric energy meter; Otherwise, it is determined that there is no fault in the internal clock or frequency generation module of the electric energy meter; Among them, SY F is the preset frequency stability threshold; In this embodiment, the frequency stability exceeding the threshold value indicates that the internal clock or frequency generation module of the electric energy meter may have a fault, such as crystal oscillator aging, frequency adjustment circuit failure, etc., which may affect the timing and measurement accuracy of the electric energy meter; Step C2.4, if K R >KY R , it is judged that the power module of the electric energy meter is faulty; Otherwise, it is judged that there is no power module fault in the electric energy meter; Among them, KY Ris the preset ripple factor threshold; In this embodiment, when the ripple coefficient is too large, it means that the power supply voltage ripple is too large, which may be caused by damage to the filter capacitor in the power module, failure of the voltage stabilizing circuit, etc., which will interfere with the normal operation of the energy meter.

[0027] This embodiment can collect a variety of target data from the remote electric energy meter through the data collection module, covering key information such as real-time voltage, current, active power, reactive power, electric energy meter operating frequency, power supply voltage ripple, etc., and can reasonably determine the data collection time interval according to the electric energy meter performance and communication conditions, which can not only ensure the validity of the data, but also avoid excessive burden on the electric energy meter and the communication network. Afterwards, the data integration module is used to classify and organize the collected data to form different types of data sets for subsequent analysis and processing; the feature extraction module can extract key parameters from the data set after the abnormal value check and processing, and perform detailed parameter calculations for voltage, current, power, electric energy meter operating frequency, power supply voltage ripple, etc., such as voltage effective value, peak value, change slope, current effective value, peak-to-effective value ratio, phase difference change rate, etc. These accurately extracted parameters can more accurately reflect the working status and characteristics of various aspects of the electric energy meter, and provide a strong basis for fault analysis; the fault analysis module performs fault analysis based on the extracted key parameters, and can accurately determine whether the electric energy meter has a fault and the specific type of fault by comparing with the preset threshold value for different types of faults such as metering faults and hardware faults. For example, it can determine basic metering component faults, power metering faults, voltage-related hardware faults, current-related hardware faults, electric energy meter internal clock or frequency generation module faults, power module faults, etc., which helps to quickly locate and solve various problems that may occur in the electric energy meter.

[0028] Embodiment 2 As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from that of the first embodiment only in that the present embodiment further includes: Data checking module, used to check and process outliers on the data set; The specific method is as follows: StepA1, select the voltage data set; Then calculate all v i The average value Pv; At the same time, calculate all v i The standard deviation of Bv; StepA2: The v collected at each collection time i Combine Pv and Bv to analyze the corresponding v at each collection time i Whether it is an outlier; If v i -Pv>α×Bv, then the corresponding vi Mark as outlier; If v i -Pv≤α×Bv, then the corresponding v i Mark as outlier; Among them, α is the preset multiple value; StepA3: Extract all v except outliers i , and calculate its average value, and then use its average value as the replacement value of the outlier; After replacing the corresponding abnormal values ​​with replacement values, a new voltage data set is formed; In this way, outlier check processing is performed on data sets composed of different types of data, and new data sets are obtained.

[0029] Compared with the first embodiment, this embodiment adds a data inspection module, which can perform outlier inspection on different types of data sets. By calculating the mean and standard deviation of the data set, and combining the preset multiple value to judge and mark the outliers, the calculated replacement value is used to replace the outliers to obtain a new data set. This can effectively eliminate the interference of outliers in the data and improve the accuracy of subsequent data-based analysis, feature extraction, fault judgment and other operations.

[0030] Embodiment 3 As the third embodiment of the present invention, when the present application is implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments, and the difference between the technical solution of this embodiment and the first and second embodiments is that in this embodiment: Target data also includes: Communication delay data: Since remote energy meters need to exchange data with monitoring centers through communication networks, communication delay data can reflect the quality and stability of the communication link, and plays a key role in timely detection of communication failures. The dataset is composed as follows: Communication delay dataset D = {d 1 , d 2 , ...d n}, d i Indicates the communication delay values ​​obtained at different acquisition times; The extraction method of the feature extraction module also includes communication delay parameter extraction: By calculating the mean and standard deviation of all communication delay values ​​in the communication delay data set F; The formula is: Where D0 is the average value of all communication delay values ​​in the communication delay data set F, is the standard deviation of all communication delay values ​​in the communication delay dataset F; In this embodiment, the mean and standard deviation of the communication delay can fully reflect the quality and stability of the communication link; the mean represents the average communication delay, and the standard deviation reflects the fluctuation degree of the communication delay; when the mean or standard deviation is too large, it may indicate that there is a fault in the communication link, such as network congestion, communication equipment failure, etc. The fault analysis of the fault analysis module also includes communication fault analysis: By measuring the mean and standard deviation of the communication delay, it is determined whether the communication link is faulty; If D0>D0Y and > If all of them are true, it is judged that there is a communication link failure in the electric energy meter; Otherwise, it is determined that there is no communication link failure in the electric energy meter; Where D0Y is the preset communication delay mean threshold, is the preset communication delay standard deviation threshold; In this embodiment, there is a fault in the communication link, which may be caused by network equipment failure, communication line damage, or communication protocol abnormality, resulting in data transmission problems.

[0031] Based on the first and second embodiments, this embodiment further improves data collection and adds communication delay data. The communication delay data set composed of the communication delay data can reflect the quality and stability of the communication link. The communication delay data is extracted by the feature extraction module, and its average value and standard deviation are calculated. The fault analysis module can accurately determine whether there is a fault in the communication link based on these parameters. This enables the entire system to not only detect and analyze various faults of the electric energy meter itself, but also to promptly discover communication faults, and fully guarantee the normal operation of the remote electric energy meter system.

[0032] Embodiment 4 As the fourth embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the second embodiment and the third embodiment, the difference between the present embodiment and the first embodiment, the second embodiment and the third embodiment is that the present embodiment further includes: The fault alarm module is used to send the fault analysis results of the fault analysis module to the monitoring center through the remote communication network and display them to the relevant maintenance personnel, so that the maintenance personnel can quickly obtain the fault information and take corresponding maintenance measures; When the fault analysis module detects a fault, the fault alarm module will quickly start and generate an alarm message containing detailed fault information; Among them, the alarm message content is rich and comprehensive, mainly including the identification number of the electricity meter, the fault type corresponding to the metering failure, hardware failure, and communication failure, specific parameter values ​​such as the effective value of voltage and the effective value of current, and the fault-related parameter values ​​corresponding to the corresponding thresholds.

[0033] Compared with the previous embodiment, the newly added fault alarm module in this embodiment can quickly send the fault analysis results of the fault analysis module to the monitoring center through the remote communication network to display to the relevant maintenance personnel. When a fault is detected, an alarm message containing detailed fault information can be generated, such as the identification number of the electric energy meter, the specific fault type, the relevant parameter values ​​and thresholds, etc., so that the maintenance personnel can quickly obtain the fault information and take corresponding maintenance measures, which effectively improves the timeliness and pertinence of fault handling.

[0034] Embodiment 5 As the fifth embodiment of the present invention, when the present application is specifically implemented, compared with the first, second, third and fourth embodiments, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned first, second, third and fourth embodiments.

[0035] This embodiment combines the solutions of the above-mentioned embodiments one, two, three and four for implementation, integrating the advantages of each embodiment, and realizing a complete functional chain from comprehensive data collection, abnormal value processing, multi-faceted fault analysis to timely alarm transmission of fault information, which can more comprehensively, efficiently and accurately detect, identify and process faults in the remote electric energy meter system, and ensure the stable operation of the remote electric energy meter.

[0036] The present invention also provides a remote electric energy meter fault detection and identification method, which is implemented by the remote electric energy meter fault detection and identification system, and includes the following steps: Step 1: Data Collection Collect various target data from remote power meters; Step 2: Data Integration Classify and organize the collected target data, and group the data of the same type into data sets; Step 3: Data Check Perform outlier check on the data set to detect outliers in different types of data sets, then replace the outliers and get a new data set; Step 4: Feature extraction Extract key parameters from the dataset processed by outlier check; Step 5: Fault Analysis Perform fault analysis based on the key parameters extracted by the feature extraction module to determine whether the electric energy meter has metering faults, hardware faults, and communication faults. Step 6: Fault Alarm The fault analysis results of the fault analysis module are sent to the monitoring center through the remote communication network and displayed to relevant maintenance personnel.

[0037] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0038] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A remote electric energy meter fault detection and identification system, characterized in that: include: A data collection module is used to collect various target data from a remote electric energy meter, including real-time voltage data, real-time current data, active power data, reactive power data, electric energy meter operating frequency data, power supply voltage ripple data, and communication delay data; Data integration module, used to classify and organize the collected target data; The classification and sorting method is to group the data of the same type into data sets; The data inspection module is used to perform outlier inspection on the data set, detect outliers in different types of data sets through outlier inspection, and then replace the outliers to obtain a new data set; Feature extraction module, used to extract key parameters from the data set after outlier check processing; The fault analysis module is used to perform fault analysis based on the key parameters extracted by the feature extraction module, and determine whether the electric energy meter has a metering fault, a hardware fault, or a communication fault through fault analysis; The fault alarm module is used to send the fault analysis results of the fault analysis module to the monitoring center through the remote communication network and display them to relevant maintenance personnel.

2. A remote electric energy meter fault detection and identification system according to claim 1, characterized in that: The feature extraction module extracts voltage parameters in the following way: Step 1.1, voltage effective value calculation: The voltage effective value is calculated by summing the squares of all voltage values ​​in the voltage data set, taking the average and then taking the square root, and recording it as V rms ; Step 1.2, voltage peak calculation: By extracting the maximum value from all voltage values ​​in the voltage data set and recording it as the voltage peak value V peak ; Step 1.3, voltage change slope calculation: By calculating the ratio of the voltage difference to the time difference at each adjacent moment, the average value of the ratio is calculated and recorded as the voltage change slope K. V .

3. A remote electric energy meter fault detection and identification system according to claim 2, characterized in that: The feature extraction module extracts current parameters in the following way: Step 2.1, current effective value calculation: The effective value of the current is calculated by summing the squares of all current values ​​in the current data set, taking the average and then taking the square root, and recording it as K rms ; Step 2.2, Calculation of the ratio of current peak value to effective value: By extracting the maximum value among all current values ​​in the current data set and recording it as the current peak value; Then, the ratio of the current peak value to the effective value is calculated by dividing the current peak value by the voltage effective value; and recorded as R K ; Step 2.3, calculation of current phase difference change rate: By calculating the ratio of the current phase angle difference to the time difference at each adjacent moment, the average value of the ratio is calculated and recorded as the current phase difference change rate. .

4. A remote electric energy meter fault detection and identification system according to claim 3, characterized in that: The feature extraction module extracts power parameters in the following way: Step 3.1, calculation of the ratio of active power to reactive power: The ratio of active power to reactive power is obtained by dividing the average of all active power values ​​in the active power data set P by the average of all reactive power values ​​in the reactive power data set Q and recorded as R PQ ; Step 3.2, calculation of active power fluctuation rate: By calculating the ratio of the standard deviation of all active powers in the active power data set P to the average value, and recording this ratio as the active power fluctuation rate ; Step 3.3, calculation of reactive power fluctuation rate: By calculating the ratio of the standard deviation of all reactive powers in the reactive power data set Q to the average value, the ratio is recorded as the reactive power fluctuation rate .

5. A remote electric energy meter fault detection and identification system according to claim 4, characterized in that: The feature extraction module extracts the operating frequency parameters of the electric energy meter in the following manner: By calculating the ratio of the standard deviation of all frequencies in the frequency data set F to the average value, and recording this ratio as the frequency stability S F .

6. A remote electric energy meter fault detection and identification system according to claim 5, characterized in that: The feature extraction module extracts the power supply voltage ripple parameters in the following manner: In the ripple data set R, extract the ripple parameter with the largest value and record it as the ripple voltage peak value V rpp ; At the same time, the DC voltage component is obtained and marked as V dc ; The ripple factor is then calculated by dividing the ripple voltage peak by the DC voltage component and is labeled K. R .

7. A remote electric energy meter fault detection and identification system according to claim 6, characterized in that: The feature extraction module extracts the communication delay parameters in the following manner: By calculating the mean and standard deviation of all communication delay values ​​in the communication delay data set F, and recording them as D0 and .

8. A remote electric energy meter fault detection and identification system according to claim 7, characterized in that: The fault analysis method is as follows: Step C1, measurement failure analysis: Step C1.1, judging whether the basic metering element is faulty by comparing whether the difference between the effective value of voltage and the rated voltage, and the effective value of current and the rated current exceeds the corresponding preset error threshold; If |V rms -V rated |>VY and |K rms -K rated |>If any one of KY is true, it is judged that the electric energy meter has a basic metering element failure; Among them, V rated is the rated voltage of the energy meter, VY is the preset voltage error threshold, K rated is the rated current, KY is the current error threshold; Step C1.2, determine the power metering failure by analyzing the ratio of active power to reactive power and the fluctuation rate of active power and reactive power respectively; If | R PQ -R0 PQ |>RY PQ , > and > If at least one of the following conditions holds true, it is determined that the electric energy meter has a power measurement fault; Among them, R0 PQ is the normal ratio of active power to reactive power, RY PQ is the preset ratio difference threshold, is the preset active power fluctuation rate threshold, is the reactive power fluctuation rate threshold; Step C2, hardware failure analysis: StepC2.1, if V peak >VY peak and K V >KY V If all of them are true, it is determined that the electric energy meter has a voltage-related hardware fault; Among them, VY peak is the preset voltage peak threshold, KY V is the preset voltage change slope threshold Step C2.2, if R K >RY K and > If all of them are true, it is determined that the electric energy meter has a current-related hardware fault; Among them, RY K is the preset current peak value to effective value ratio threshold, is the preset current phase difference change rate threshold; Step C2.3, if S F >SY F , it is judged that there is a fault in the internal clock or frequency module of the electric energy meter; Among them, SY F is the preset frequency stability threshold; Step C2.4, if K R >KY R , it is judged that the power module of the electric energy meter is faulty; Among them, KY R is the preset ripple factor threshold; StepC3. Communication failure analysis: By measuring the mean and standard deviation of the communication delay, it is determined whether the communication link is faulty; If D0>D0Y and > If all of them are true, it is judged that there is a communication link failure in the electric energy meter; Where D0Y is the preset communication delay mean threshold, is the preset communication delay standard deviation threshold.

9. A remote electric energy meter fault detection and identification system according to claim 1, characterized in that: In the data integration module, the data sets are as follows: Voltage data set V = {v1, v2, ... v n }; Current data set K = {k1, k2, ... k n }; Active power data set P = {p1, p2, ... p n }; Reactive power data set Q = {q1, q2, ... q n }; Frequency data set F = {f1, f2, ... f n }; Ripple data set R={r1, r2, …, r n }; Communication delay data set D = {d1, d2, ... d n }; Among them, v i Indicates the voltage value obtained at different acquisition times, i=1, 2, ... n, n is the number of acquisition points, k i represents the current value obtained at different acquisition times, p i Indicates the power value obtained at different acquisition times, f i Represents the frequency value obtained at different acquisition times, r i Indicates the ripple value obtained at different acquisition times, d i Indicates the communication delay values ​​obtained at different acquisition times; The abnormal value check processing method of the data check module is as follows: StepA1, select the voltage data set; Then calculate all v i The average value Pv; At the same time, calculate all v i The standard deviation of Bv; StepA2: The v collected at each collection time i Combine Pv and Bv to analyze the corresponding v at each collection time i Whether it is an outlier; If v i -Pv>α×Bv, then the corresponding v i Mark as outlier; If v i -Pv≤α×Bv, then the corresponding v i Mark as outlier; Among them, α is the preset multiple value; StepA3: Extract all v except outliers i , and calculate its average value, and then use its average value as the replacement value of the outlier; After replacing the corresponding abnormal values ​​with replacement values, a new voltage data set is formed; In this way, outlier check processing is performed on data sets composed of different types of data, and new data sets are obtained.

10. A remote electric energy meter fault detection and identification method, characterized in that: The method is implemented by a remote electric energy meter fault detection and identification system as described in any one of claims 1-9.

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