CN118536046B - Method, device, electric energy meter and medium for identifying abnormal jump of electric energy meter sampling signal - Google Patents
Method, device, electric energy meter and medium for identifying abnormal jump of electric energy meter sampling signal Download PDFInfo
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Abstract
本发明公开了一种电能表采样信号异常跳变识别方法、装置、电能表及介质，涉及电能表技术领域。该方法中，获取连续采集的至少两个采样周期的电信号；然后对采样周期的电信号进行时间节点对齐处理，使得相邻两个新周期内各时间节点上均对应一个采样点；在对齐处理后，获取相邻两个新周期的相同时间节点的目标采样点对应的电信号幅值之间的差值；在检测到差值大于阈值的情况下，确定目标采样点存在异常跳变。该方法中利用了周期信号的固有特性（即如果不考虑其他因素的影响，每个周期对应位置的采样数据幅值完全相等，并且其值在时间上以一定的周期重复出现）实现了对电能表采样信号异常跳变的较准确地检测。
The present invention discloses a method, device, electric energy meter and medium for identifying abnormal jumps in sampling signals of electric energy meters, and relates to the technical field of electric energy meters. In the method, an electric signal of at least two sampling cycles of continuous acquisition is obtained; then the electric signal of the sampling cycle is subjected to time node alignment processing, so that each time node in two adjacent new cycles corresponds to a sampling point; after the alignment processing, the difference between the electric signal amplitudes corresponding to the target sampling points of the same time node in the two adjacent new cycles is obtained; when it is detected that the difference is greater than a threshold, it is determined that the target sampling point has an abnormal jump. The method utilizes the inherent characteristics of periodic signals (that is, if the influence of other factors is not considered, the amplitudes of the sampling data at the corresponding positions of each cycle are completely equal, and their values repeat in time at a certain period) to achieve a more accurate detection of abnormal jumps in the sampling signals of the electric energy meter.
Description
Technical Field
The invention relates to the technical field of electric energy meters, in particular to an electric energy meter sampling signal abnormal jump identification method and device, an electric energy meter and a medium.
Background
The electric energy meter is used as core equipment of electric power metering, and the quality of a sampling signal directly influences the accuracy of electric power metering. Along with the intelligent development of the power system and the refinement requirement of the electric market trade, higher requirements are provided for the accuracy and stability of the sampling signals of the electric energy meter. However, in actual operation, the sampling signal of the electric energy meter may be affected by various factors, such as power grid fluctuation, transient load change, electromagnetic interference, equipment aging, etc., so that abnormal jump occurs in the sampling signal, thereby affecting fairness and accuracy of electric energy metering.
The related method for identifying abnormal jump of the sampling signal of the electric energy meter mainly comprises the following steps: threshold comparison, statistical analysis, methods employing signal filtering and mutation detection techniques, and methods employing pattern recognition and machine learning techniques. However, the threshold comparison method and the statistical analysis method have the defects of inflexible set threshold and weak anti-interference capability; meanwhile, the statistical analysis method and the method adopting the signal filtering and mutation detection technology have the defect of response lag; and methods employing signal filtering and abrupt change detection techniques and machine learning techniques have the disadvantage of large resource consumption.
Therefore, the method for identifying the abnormal jump of the sampling signal of the electric energy meter, which is more accurate, has strong adaptability and low resource consumption, is a technical problem which needs to be solved by the person in the field.
Disclosure of Invention
The invention aims to provide a method and a device for identifying abnormal jump of a sampling signal of an electric energy meter, the electric energy meter and a medium, and aims to solve the technical problems of inflexible threshold value, weak anti-interference capability, lag response and high resource consumption in the process of identifying the abnormal jump of the sampling signal of the electric energy meter.
In order to solve the technical problems, the invention provides a method for identifying abnormal jump of a sampling signal of an electric energy meter, which comprises the following steps:
acquiring continuously acquired electrical signals of at least two sampling periods;
performing time node alignment processing on the electric signals of the sampling period; after alignment treatment, each time node in two adjacent new periods corresponds to one sampling point;
After alignment processing, obtaining a difference value between the amplitude values of the electric signals corresponding to the target sampling points of the same time nodes of two adjacent new periods;
Under the condition that the difference value is detected to be larger than a threshold value, determining that abnormal jump exists in the target sampling point; the threshold value is determined by the initial threshold value and/or the sum of the average values of historical difference values between the amplitude values of the electric signals corresponding to the sampling points of the same time node in a historical new period, and the historical new period is a new period before the new period where the target sampling point is located.
Preferably, the performing time node alignment processing on the electrical signal in the sampling period includes:
Acquiring the number of sampling points in the sampling period;
judging whether the number of the sampling points in the sampling period is an integer or not;
If yes, determining that the time nodes of the electric signals of the two adjacent sampling periods are aligned;
If not, combining the preset number of sampling periods to serve as the new periods, and determining that the electrical signal time nodes of two adjacent new periods are aligned; the preset number is the minimum value of all the target numbers, and the number of the sampling points after the sampling periods of the target numbers are combined is an integer.
Preferably, the acquiring the number of sampling points in the sampling period includes:
Acquiring a sampling rate and a power grid frequency;
Acquiring the ratio of the sampling rate to the grid frequency;
the ratio is taken as the number of sampling points in the sampling period.
Preferably, determining the initial threshold comprises:
Acquiring an amplitude value difference value when the power grid data normally fluctuates; the method comprises the steps that a preset factor is considered to determine the condition that power grid data belong to normal fluctuation, wherein the preset factor at least comprises one of seasonal factors and time-period factors;
and taking the amplitude difference value as the initial threshold value.
Preferably, before determining that the target sampling point has an abnormal jump, the method includes:
judging whether the difference value is continuously detected to be larger than the threshold value within a preset time length;
If yes, entering the step of determining that the target sampling point has abnormal jump;
If not, determining that the target sampling point is normal transient fluctuation.
Preferably, after the determining that the target sampling point has an abnormal jump, the method further includes:
And sending prompt information for representing suspending using the current metering, and/or sending prompt information for representing detecting abnormal jump of the signal to a user, and/or recording the detected abnormal jump condition.
Preferably, after the acquiring the electrical signals of at least two sampling periods continuously acquired, before the performing the time node alignment processing on the electrical signals of the sampling periods, the method further includes:
and storing the electric signals of the sampling periods according to the time sequence.
In order to solve the technical problems, the invention provides an abnormal jump identification device for a sampling signal of an electric energy meter, which comprises the following components:
the first acquisition module is used for acquiring the electric signals of at least two sampling periods which are continuously acquired;
the alignment processing module is used for performing time node alignment processing on the electric signals in the sampling period; after alignment treatment, each time node in two adjacent new periods corresponds to one sampling point;
the second acquisition module is used for acquiring the difference value between the electric signal amplitudes corresponding to the target sampling points of the same time nodes of two adjacent new periods after the alignment processing;
The determining module is used for determining that the target sampling point has abnormal jump under the condition that the difference value is detected to be larger than a threshold value; the threshold value is determined by the initial threshold value and/or the sum of the average values of historical difference values between the amplitude values of the electric signals corresponding to the sampling points of the same time node in a historical new period, and the historical new period is a new period before the new period where the target sampling point is located.
In order to solve the above technical problems, the present invention provides an electric energy meter, including:
A memory for storing a computer program;
and the processor is used for realizing the step of the method for identifying the abnormal jump of the sampling signal of the electric energy meter when executing the computer program.
In order to solve the technical problems, the invention provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program realizes the steps of the method for identifying abnormal jump of the sampling signal of the electric energy meter when being executed by a processor.
The invention provides a method for identifying abnormal jump of a sampling signal of an electric energy meter, which comprises the following steps: acquiring continuously acquired electrical signals of at least two sampling periods; then, carrying out time node alignment processing on the electric signals in the sampling periods, so that each time node in two adjacent new periods corresponds to one sampling point; after alignment processing, obtaining a difference value between the amplitude values of the electric signals corresponding to the target sampling points of the same time nodes of two adjacent new periods; and under the condition that the difference value is detected to be larger than the threshold value, determining that the target sampling point has abnormal jump.
The invention has the beneficial effects that: firstly, the method utilizes the inherent characteristics of the periodic signals (namely, if the influence of other factors is not considered, the amplitude of the sampled data at the corresponding position of each period is completely equal and the value repeatedly appears in a certain period in time) to realize more accurate detection of the abnormal jump of the sampled signal of the electric energy meter; and secondly, determining the threshold value by the initial threshold value and/or the sum of the average values of the historical difference values between the electric signal amplitudes corresponding to the sampling points of the same time node in the historical new period, wherein the historical new period is a new period before the new period where the target sampling point is located. Because the average value of the historical difference values between the amplitudes of the new periods before the new period of the target sampling point can change along with the difference of the new periods, the threshold value in the invention is dynamically changed, the set threshold value is more flexible and more accords with the characteristics of the electric signal corresponding to the sampling point of the new period, namely the determined threshold value is more accurate, the abnormal condition can be captured accurately, the normal fluctuation change can be filtered, and the accuracy of identifying the abnormal jump of the sampling signal is improved; thirdly, compared with the method adopting a statistical analysis method and adopting a signal filtering and mutation detection technology, the method provided by the invention does not need to carry out statistical analysis and trend prediction, and can identify abnormal jump in time; in addition, compared with the method adopting the signal filtering and mutation detection technology and the machine learning technology, the method provided by the invention does not need to carry out a large number of sample training, and reduces the consumption of calculation resources.
In addition, the invention also provides an electric energy meter sampling signal abnormal jump identification device, an electric energy meter and a computer readable storage medium, which have the same or corresponding technical characteristics as the electric energy meter sampling signal abnormal jump identification method, and have the same effects as the above.
Drawings
For a clearer description of embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described, it being apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
FIG. 1 is a schematic diagram of a threshold comparison method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a jump signal according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a signal filtering and mutation detection method according to an embodiment of the present invention;
fig. 4 is a flowchart of a method for identifying abnormal jump of a sampling signal of an electric energy meter according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a periodic signal characteristic according to an embodiment of the present invention;
FIG. 6 is a block diagram of an apparatus for identifying abnormal transitions of a sampling signal of an electric energy meter according to an embodiment of the present invention;
Fig. 7 is a block diagram of an electric energy meter according to another embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without making any inventive effort are within the scope of the present invention.
The invention aims to provide a method and a device for identifying abnormal jump of a sampling signal of an electric energy meter, the electric energy meter and a medium, and aims to solve the technical problems of inflexible threshold value, weak anti-interference capability, lag response and high resource consumption in the process of identifying the abnormal jump of the sampling signal of the electric energy meter.
The electric energy meter is used as core equipment of electric power metering, and the quality of a sampling signal directly influences the accuracy of electric power metering. The existing electric energy meter sampling signal abnormal jump identification technology mainly comprises the following steps:
Threshold comparison method: fig. 1 is a schematic diagram of a threshold comparison method according to an embodiment of the present invention. Fig. 1 is a signal sampled at a sampling rate Fn, with upper red lines and lower red lines manually set thresholds. And by setting reasonable upper and lower limit thresholds of the voltage and current sampling signals, when the sampling signals exceed the threshold range, the abnormal jump is judged. The method is visual and simple, but has higher requirement on the selection of the threshold value, and can cause erroneous judgment if the threshold value is too strict, and can miss the detection of the real abnormality if the threshold value is too loose.
Statistical analysis: and carrying out statistical analysis based on the historical data, calculating parameters such as standard deviation, variation coefficient and the like of the sampling signals, and considering that abnormal jump exists when the deviation between the real-time sampling value and the predicted value of the statistical model exceeds a preset threshold value. Fig. 2 is a schematic diagram of a jump signal according to an embodiment of the present invention. In fig. 2, there is a signal anomaly jump at one sampling point between 200 and 300. This approach is suitable for long-term stable grid environments, but may be insufficiently sensitive to transient disturbances.
Signal filtering and mutation detection technology: fig. 3 is a schematic diagram of a signal filtering and mutation detection method according to an embodiment of the present invention. In fig. 3, the abscissa indicates sampling points, the ordinate indicates amplitudes, the blue line indicates a noisy signal, and the orange line indicates a filtered signal. The sampled signal is processed using digital signal processing techniques, such as moving average filtering, kalman filtering, etc., and then signal anomalies are identified in conjunction with a mutation detection algorithm, such as the cumulative and control map algorithm (Cumulative Sum Control Chart Algorithm, CUSUM), page, etc. The method gives consideration to the dynamic characteristics and stability of signals to a certain extent, but has higher requirements on parameter setting and instantaneity of algorithms.
Pattern recognition and machine learning techniques: the training model is used for identifying the difference between the normal sampling signal and the abnormal jump signal by using machine learning methods such as a neural network, a support vector machine and the like, so that automatic abnormal detection is realized. This approach has strong generalization and self-learning capabilities, but requires a large number of samples to train and consumes significant computational resources.
The related electric energy meter sampling signal abnormal jump identification technology has the following defects:
The threshold setting is inflexible: some current identification schemes rely on preset thresholds to judge whether signals are abnormal or not, but the power grid in the actual operation environment fluctuates in a complex way, and a single threshold is difficult to adapt to various conditions, so that erroneous judgment or missed judgment is easy to cause.
Hysteresis of response to bursty anomalies: part of the methods focus on statistical analysis and trend prediction, and may not react fast enough to sudden large-amplitude jumps, failing to capture transient anomalies in time.
The anti-jamming capability is weak: the signal distortion processing effect on electromagnetic interference, harmonic waves and other nonlinear loads possibly is poor, and real load change and abnormal jump cannot be effectively distinguished.
Lack of intelligent analysis: many traditional methods are based on simple rules or fixed models, have poor adaptability to complex and changeable signal behaviors, and cannot fully utilize advanced technologies such as big data and machine learning to perform deep analysis and intelligent recognition.
The resource consumption is large: when processing and identifying abnormal jump in real time, part of complex algorithm may consume more computing resource and storage resource, which is unfavorable for large-scale popularization and application.
Therefore, the invention provides a more accurate, efficient, intelligent and strong-adaptability electric energy meter sampling signal abnormal jump identification scheme.
It is worth noting that the method for identifying abnormal jump of the sampling signal provided by the embodiment of the invention is generally applied to related products such as intelligent electric energy meters, multifunctional electric energy meters, electric power metering management systems and the like. The following are specific application scenarios:
An intelligent electric energy meter: in the novel intelligent electric energy meter, the recognition scheme of the abnormal jump of the sampling signal can be integrated in hardware design and software algorithm, the voltage and current sampling signals are monitored and analyzed in real time, and when the abnormal jump occurs, the novel intelligent electric energy meter can quickly respond, so that the metering accuracy is ensured and false alarm is prevented.
Multifunctional electric energy meter: the electric energy meter not only has a basic electric energy metering function, but also can integrate the functions of data communication, electric energy quality analysis and the like. In such products, the sampling signal abnormal jump recognition technology can help the electric energy meter to better handle complex working conditions such as power grid fluctuation, harmonic pollution and the like, and improves the stability and reliability of electric energy metering.
In order to better understand the aspects of the present invention, the present invention will be described in further detail with reference to the accompanying drawings and detailed description. Fig. 4 is a flowchart of a method for identifying abnormal jump of a sampling signal of an electric energy meter according to an embodiment of the present invention, as shown in fig. 4, where the method includes:
s10: acquiring continuously acquired electrical signals of at least two sampling periods;
S11: performing time node alignment processing on the electric signals in the sampling period; after alignment treatment, each time node in two adjacent new periods corresponds to one sampling point;
s12: after alignment processing, obtaining a difference value between the amplitude values of the electric signals corresponding to the target sampling points of the same time nodes of two adjacent new periods;
S13: and under the condition that the difference value is detected to be larger than the threshold value, determining that the target sampling point has abnormal jump.
The acquired electrical signals of the sampling period are not limited, and the electrical signals of at least two sampling periods continuously acquired are acquired in the embodiment of the invention because the difference between the magnitudes of the electrical signals of two adjacent periods (sampling periods, or new periods mentioned later) on the same time node is compared so as to detect whether abnormal jump exists. The electrical signal specifically includes periodic signals such as voltage and current. In order to facilitate the comparative analysis of the front and rear periodic signals, in implementation, after acquiring the continuously acquired electrical signals of at least two sampling periods, before performing the time node alignment processing on the electrical signals of the sampling periods, the method further includes: and storing the electric signals of each sampling period according to the time sequence.
After the continuously acquired electrical signals of at least two sampling periods are acquired, the electrical signals of the sampling periods are subjected to time node alignment processing. The alignment processing mode is not limited, so long as it can ensure that each time node in two adjacent new periods corresponds to one sampling point after the alignment processing. In one embodiment, performing a time node alignment process on the electrical signal of the sampling period includes:
Acquiring the number of sampling points in a sampling period;
Judging whether the number of sampling points in the sampling period is an integer or not;
if yes, determining that the time nodes of the electric signals of the two adjacent sampling periods are aligned;
If not, combining the preset number of sampling periods to serve as new periods, and determining that the electrical signal time nodes of two adjacent new periods are aligned; the preset number is the minimum value of all the target numbers, and the number of the sampling points after the sampling periods of all the target numbers are combined is an integer.
Wherein, the number of sampling points in the sampling period is obtained comprises:
Acquiring a sampling rate and a power grid frequency;
Acquiring the ratio of the sampling rate to the power grid frequency;
the ratio is taken as the number of sampling points in the sampling period.
According to the sampling rate and the grid frequency, the sampling point number of one period is determined, and if the sampling period is not an integer point, a plurality of periods are needed to be taken as a new period. For example, the 6400 sampling rate, the signal frequency is 50Hz, then the number of sampling points in one period is 128 points, and the data of the corresponding position of each period is aligned. If the sampling rate is 4687.5, the signal frequency is still 50Hz, the number of sampling points per period is 93.75, obviously not an integer, and four periods are needed as one large period for alignment. I.e. 375 samples for the new period.
After time node alignment processing is carried out on the electric signals of the sampling periods, the difference value between the electric signal amplitudes corresponding to the target sampling points of the same time nodes of two adjacent new periods is obtained, and when the difference value is detected to be larger than a threshold value, abnormal jump of the target sampling points is determined. The threshold value is not limited. In the embodiment of the invention, in order to enable the set threshold to be more flexible and accurate, when the threshold is set, the threshold is determined by the initial threshold and/or the sum of the average values of the historical difference values between the electric signal amplitudes corresponding to the sampling points of the same time node in the historical new period, and the historical new period is a new period before the new period where the target sampling point is located.
In order to make the determined initial threshold value appropriate, in practice, determining the initial threshold value includes:
Acquiring an amplitude value difference value when the power grid data normally fluctuates; the method comprises the steps that a preset factor is considered to determine the condition that power grid data belong to normal fluctuation, wherein the preset factor at least comprises one of seasonal factors and time-period factors;
The amplitude difference is taken as an initial threshold.
In practice, the difference value may be greater than the threshold value due to transient fluctuation, and if the difference value is detected to be greater than the threshold value, it is directly determined that the target sampling point has abnormal jump, so that erroneous judgment may occur. Thus, in some embodiments, prior to determining that the target sample point has an abnormal transition, it comprises:
Judging whether the difference value is continuously detected to be larger than a threshold value within a preset time length;
if yes, a step of determining that the target sampling point has abnormal jump is carried out;
if not, determining that the target sampling point is normal transient fluctuation.
The preset duration is not limited, and is determined according to actual conditions. Whether the difference value is larger than the threshold value is continuously detected within the preset time length or not, erroneous recognition of abnormal jump is avoided as far as possible, and recognition accuracy is improved.
After determining that the target sampling point has abnormal jump, in order to process the abnormal jump in time, the method further comprises the following steps:
And sending prompt information for representing suspending using the current metering, and/or sending prompt information for representing detecting abnormal jump of the signal to a user, and/or recording the detected abnormal jump condition.
The presentation information to be issued is not limited as long as the presentation is possible.
The method for identifying abnormal jump of the sampling signal of the electric energy meter provided by the embodiment of the invention comprises the following steps: acquiring continuously acquired electrical signals of at least two sampling periods; then, carrying out time node alignment processing on the electric signals in the sampling periods, so that each time node in two adjacent new periods corresponds to one sampling point; after alignment processing, obtaining a difference value between the amplitude values of the electric signals corresponding to the target sampling points of the same time nodes of two adjacent new periods; and under the condition that the difference value is detected to be larger than the threshold value, determining that the target sampling point has abnormal jump. Firstly, the method utilizes the inherent characteristics of the periodic signals (namely, if the influence of other factors is not considered, the amplitude of the sampled data at the corresponding position of each period is completely equal, and the value of the sampled data repeatedly appears in a certain period in time) to realize more accurate detection of abnormal jump of the sampled signals of the electric energy meter; and secondly, determining the threshold value by the initial threshold value and/or the sum of the average values of the historical difference values between the electric signal amplitudes corresponding to the sampling points of the same time node in the historical new period, wherein the historical new period is a new period before the new period where the target sampling point is located. Because the average value of the historical difference values between the amplitudes of the new periods before the new period of the target sampling point can change along with the difference of the new periods, the threshold value in the embodiment of the invention is dynamically changed, the set threshold value is more flexible and more accords with the characteristics of the electric signal corresponding to the sampling point of the new period, namely, the determined threshold value is more accurate, the abnormal condition can be captured accurately, the normal fluctuation change can be filtered, and the accuracy of identifying the abnormal jump of the sampling signal is improved; thirdly, compared with a method adopting a statistical analysis method and a method adopting a signal filtering and mutation detection technology, the method provided by the embodiment of the invention can timely identify abnormal jump without carrying out statistical analysis and trend prediction; in addition, compared with the method adopting the signal filtering and mutation detection technology and the machine learning technology, the method provided by the embodiment of the invention does not need to carry out a large number of sample training, and reduces the consumption of calculation resources.
In order to make the person skilled in the art better understand the present invention, the following further details of the overall technical solution of the present invention are given with reference to fig. 5 and the detailed description. Fig. 5 is a schematic diagram of a periodic signal characteristic according to an embodiment of the present invention. In fig. 5, the abscissa represents the sampling point and the ordinate represents the amplitude. Blue arrows represent the corresponding sample point magnitudes at the same time node in adjacent two periods. The sampled data amplitude at the corresponding position of each period is completely equal, and the values thereof repeatedly appear in a certain period in time.
The embodiment of the invention provides a method for identifying abnormal jump of a sampling signal of an electric energy meter based on periodic signal characteristics, which comprises the following steps of:
Periodic signal sampling and storage: firstly, the electric energy meter continuously samples periodic signals such as voltage, current and the like, and stores signal data of continuous periods so as to perform comparison analysis of the periodic signals before and after.
Signal period division and alignment: the period length of the signals is determined according to the power grid frequency (such as 50Hz or 60 Hz), and the period boundary positioning and alignment processing is carried out on the sampled data, so that the adjacent periodic signals are ensured to be compared on the same time node.
Signal feature extraction and comparison: and extracting the signal amplitude of the corresponding position of the previous period and the next period aiming at all sampling points in each period. Then, the signal amplitude difference at the corresponding position of the two periods is calculated.
Dynamic threshold setting: according to the statistical characteristics of historical data of signals, an initial threshold value is firstly set, and the threshold value is dynamically adjusted by utilizing the average value of amplitude difference values in the running process, so that whether the amplitude difference values of the periodic signals before and after exceed the normal fluctuation range is judged. The threshold value should be set by fully considering the normal fluctuation condition of the power grid and possibly seasonal and time-period changes and other factors.
Abnormal jump judgment: when the signal amplitude difference value at the corresponding position of the front period and the rear period exceeds a set threshold value, the system judges that abnormal jump is possible at the point, and further analyzes the jump amplitude and duration to determine whether the jump is a real abnormal event or normal transient fluctuation.
Exception handling and recording: once the abnormal jump of the sampling signal is identified, the system takes corresponding measures, such as recording an abnormal event, suspending the current metering, triggering a warning or notifying an electric company, and the like, reasonably corrects or marks metering data in an abnormal period, and ensures fairness and accuracy of electric energy metering.
Compared with the existing related technology for identifying the abnormal jump of the sampling signal of the electric energy meter, the method for identifying the abnormal jump of the sampling signal of the electric energy meter based on the periodic signal characteristics has the following advantages:
1) The identification accuracy is improved: by comparing the changes of the corresponding positions of the front periodic signal and the rear periodic signal and combining dynamic threshold setting, normal fluctuation and real signal abnormal jump can be distinguished more accurately.
2) Real-time enhancement: the signal periodic characteristics are utilized to monitor the signal change in real time, abnormal jump can be detected rapidly, and the response is made in time, so that the stability of the power system and the fairness of electric energy measurement are improved.
3) The adaptability is strong: the dynamic threshold setting strategy can be adjusted according to different operating environments and load characteristics, so that the self-adaptability of the identification scheme is enhanced, and the dynamic threshold setting strategy can be more suitable for complex and variable operating conditions of a power grid.
4) The resource efficiency is high: by analyzing the key points focused in the signal period, invalid calculation is reduced, and the utilization rate of calculation resources is improved.
5) Simple and easy to implement: compared with complex data modeling and machine learning methods, the periodic signal characteristic-based identification method is easier to realize and deploy, and is beneficial to software upgrading and optimizing on the basis of the existing electric energy meter hardware.
6) The risk of misoperation is reduced: by more accurately identifying the abnormal jump, the electric energy metering error caused by misjudgment and the misoperation of related equipment can be reduced, and the safety and the user satisfaction degree of the electric power system are improved.
The embodiment of the invention realizes the efficient and accurate identification of the abnormal jump of the sampling signal of the electric energy meter by utilizing the inherent characteristic of the periodic signal, solves the problems of misjudgment and missed judgment in the prior art, and improves the stability and reliability of an electric energy metering system.
In the above embodiment, the method for identifying the abnormal jump of the sampling signal of the electric energy meter is described in detail, and the invention also provides the device for identifying the abnormal jump of the sampling signal of the electric energy meter and the corresponding embodiment of the electric energy meter. It should be noted that the present invention describes an embodiment of the device portion from two angles, one based on the angle of the functional module and the other based on the angle of the hardware.
Fig. 6 is a block diagram of an apparatus for identifying abnormal transitions of a sampling signal of an electric energy meter according to an embodiment of the present invention. The embodiment is based on the angle of the functional module, and comprises:
a first acquisition module 10, configured to acquire electrical signals of at least two sampling periods acquired continuously;
an alignment processing module 11, configured to perform time node alignment processing on the electrical signal in the sampling period; after alignment treatment, each time node in two adjacent new periods corresponds to one sampling point;
a second obtaining module 12, configured to obtain, after the alignment process, a difference value between the electric signal amplitudes corresponding to the target sampling points of the same time nodes of two adjacent new periods;
A determining module 13, configured to determine that an abnormal jump exists in the target sampling point when the difference is detected to be greater than the threshold; the threshold value is determined by the initial threshold value and/or the sum of the average values of the historical difference values between the amplitude values of the electric signals corresponding to the sampling points of the same time node in the historical new period, and the historical new period is a new period before the new period where the target sampling point is located.
Since the embodiments of the apparatus portion and the embodiments of the method portion correspond to each other, the embodiments of the apparatus portion are referred to the description of the embodiments of the method portion, and are not repeated herein. The effect is the same as above.
Fig. 7 is a block diagram of an electric energy meter according to another embodiment of the present invention. The embodiment is based on a hardware angle, as shown in fig. 7, and the electric energy meter includes:
a memory 20 for storing a computer program;
A processor 21 for implementing the steps of the method for identifying abnormal transitions of a sampled signal of a power meter as mentioned in the above embodiments when executing a computer program.
Processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The Processor 21 may be implemented in at least one hardware form of a digital signal Processor (DIGITAL SIGNAL Processor, DSP), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 21 may also include a main processor and a coprocessor, the main processor being a processor for processing data in an awake state, also referred to as a central processor (Central Processing Unit, CPU); a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 21 may be integrated with a graphics processor (Graphics Processing Unit, GPU) for rendering and drawing of content required to be displayed by the display screen. In some embodiments, the processor 21 may also include an artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) processor for processing computing operations related to machine learning.
Memory 20 may include one or more computer-readable storage media, which may be non-transitory. Memory 20 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In this embodiment, the memory 20 is at least used for storing a computer program 201, where the computer program can implement the relevant steps of the method for identifying abnormal transitions of sampling signals of an electric energy meter disclosed in any of the foregoing embodiments after being loaded and executed by the processor 21. In addition, the resources stored in the memory 20 may further include an operating system 202, data 203, and the like, where the storage manner may be transient storage or permanent storage. Operating system 202 may include Windows, unix, linux, among other things. The data 203 may include, but is not limited to, the data related to the method for identifying abnormal transitions of the sampled signal of the electric energy meter and the like.
In some embodiments, the electric energy meter may further include a display 22, an input/output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.
Those skilled in the art will appreciate that the configuration shown in fig. 7 is not limiting of the power meter and may include more or fewer components than shown.
The electric energy meter provided by the embodiment of the invention comprises a memory and a processor, wherein the processor can realize the following method when executing a program stored in the memory: the method for identifying the abnormal jump of the sampling signal of the electric energy meter has the same effects.
Finally, the invention also provides a corresponding embodiment of the computer readable storage medium. The computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps as described in the method embodiments above.
It will be appreciated that the methods of the above embodiments, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium for performing all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The computer readable storage medium provided by the invention comprises the method for identifying the abnormal jump of the sampling signal of the electric energy meter, and the effect is the same as that of the method.
The method and the device for identifying the abnormal jump of the sampling signal of the electric energy meter, the electric energy meter and the medium are described in detail. In the description, each embodiment is described in a progressive manner, and each embodiment is mainly described by the differences from other embodiments, so that the same similar parts among the embodiments are mutually referred. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section. It should be noted that it will be apparent to those skilled in the art that the present invention may be modified and practiced without departing from the spirit of the present invention.
It should also be noted that in this specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. The method for identifying the abnormal jump of the sampling signal of the electric energy meter is characterized by comprising the following steps:
acquiring continuously acquired electrical signals of at least two sampling periods;
performing time node alignment processing on the electric signals of the sampling period; after alignment treatment, each time node in two adjacent new periods corresponds to one sampling point;
After alignment processing, obtaining a difference value between the amplitude values of the electric signals corresponding to the target sampling points of the same time nodes of two adjacent new periods;
Under the condition that the difference value is detected to be larger than a threshold value, determining that abnormal jump exists in the target sampling point; the threshold value is determined by the sum of the average values of historical difference values between the initial threshold value and the amplitude values of the electric signals corresponding to sampling points of the same time node in a historical new period, and the historical new period is a new period before the new period where the target sampling point is located.
2. The method for identifying abnormal transitions of a sampled signal of an electric energy meter according to claim 1, wherein the performing a time node alignment process on the electric signal of the sampling period comprises:
Acquiring the number of sampling points in the sampling period;
judging whether the number of the sampling points in the sampling period is an integer or not;
If yes, determining that the time nodes of the electric signals of the two adjacent sampling periods are aligned;
If not, combining the preset number of sampling periods to serve as the new periods, and determining that the electrical signal time nodes of two adjacent new periods are aligned; the preset number is the minimum value of all the target numbers, and the number of the sampling points after the sampling periods of the target numbers are combined is an integer.
3. The method for identifying abnormal transitions of a sampled signal of an electric energy meter according to claim 2, wherein the obtaining the number of sampling points in the sampling period comprises:
Acquiring a sampling rate and a power grid frequency;
Acquiring the ratio of the sampling rate to the grid frequency;
the ratio is taken as the number of sampling points in the sampling period.
4. The method for identifying abnormal jump of the sampling signal of the electric energy meter according to claim 1, wherein determining the initial threshold comprises:
Acquiring an amplitude value difference value when the power grid data normally fluctuates; the method comprises the steps that a preset factor is considered to determine the condition that power grid data belong to normal fluctuation, wherein the preset factor at least comprises one of seasonal factors and time-period factors;
and taking the amplitude difference value as the initial threshold value.
5. The method for identifying abnormal transitions in a sampled signal of an electric energy meter according to any one of claims 1 to 4, wherein before determining that the target sampling point has an abnormal transition, the method comprises:
judging whether the difference value is continuously detected to be larger than the threshold value within a preset time length;
If yes, entering the step of determining that the target sampling point has abnormal jump;
If not, determining that the target sampling point is normal transient fluctuation.
6. The method for identifying abnormal transitions in a sampled signal of a power meter according to claim 5, further comprising, after said determining that the target sampling point has an abnormal transition:
And sending prompt information for representing suspending using the current metering, and/or sending prompt information for representing detecting abnormal jump of the signal to a user, and/or recording the detected abnormal jump condition.
7. The method for identifying abnormal transitions in a sampled signal of an electric energy meter according to claim 1, wherein after the acquiring the continuously acquired electric signals of at least two sampling periods, before the performing the time node alignment processing on the electric signals of the sampling periods, the method further comprises:
and storing the electric signals of the sampling periods according to the time sequence.
8. An electric energy meter sampling signal abnormal jump recognition device, which is characterized by comprising:
the first acquisition module is used for acquiring the electric signals of at least two sampling periods which are continuously acquired;
the alignment processing module is used for performing time node alignment processing on the electric signals in the sampling period; after alignment treatment, each time node in two adjacent new periods corresponds to one sampling point;
the second acquisition module is used for acquiring the difference value between the electric signal amplitudes corresponding to the target sampling points of the same time nodes of two adjacent new periods after the alignment processing;
The determining module is used for determining that the target sampling point has abnormal jump under the condition that the difference value is detected to be larger than a threshold value; the threshold value is determined by the sum of the average values of historical difference values between the initial threshold value and the amplitude values of the electric signals corresponding to sampling points of the same time node in a historical new period, and the historical new period is a new period before the new period where the target sampling point is located.
9. An electric energy meter, comprising:
A memory for storing a computer program;
a processor for implementing the steps of the method for identifying abnormal transitions of a sampled signal of an electric energy meter according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program when executed by a processor implements the steps of the method for identifying abnormal transitions of a sampling signal of an electric energy meter according to any one of claims 1 to 7.
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