CN110929115B - Power utilization safety monitoring method and system based on power utilization characteristics - Google Patents
Power utilization safety monitoring method and system based on power utilization characteristics Download PDFInfo
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Abstract
The invention discloses a power utilization safety monitoring method and system based on power utilization characteristics, wherein the method comprises the following steps: receiving power utilization data and preprocessing the power utilization data to generate a power utilization parameter matrix; identifying the current electric equipment in a working state based on a pre-trained electric equipment identification model according to the electric parameter matrix; and for the electric equipment in the working state, carrying out state monitoring based on a pre-trained electric equipment state diagnosis model. The invention can realize the real-time monitoring of the safety of the electric equipment.
Description
Technical Field
The invention belongs to the technical field of power utilization safety monitoring, and particularly relates to a power utilization safety monitoring method and system based on power utilization characteristics.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The existing traditional monitoring method is that a data acquisition and transmission device is installed at each electric equipment terminal, so that online real-time monitoring of each electric equipment of a user is realized, for example, a leakage current monitoring instrument is additionally installed, the economical efficiency is poor, and the maintenance and the management are not convenient.
The monitoring device is arranged at the power inlet, so that the power utilization information of each electric appliance is identified from the monitored power utilization information of the user to acquire the energy consumption and other information of the household electric appliance, and the problem of poor economy can be effectively solved. For such a monitoring mode, the electric equipment is generally identified by a mathematical optimization solution, for example, rico and the like use a heuristic algorithm to solve a decomposition model based on the steady-state current, but the considered electric equipment has fewer types and larger load decomposition deviation. The model adopted by Lin Y H and the like considers that the power of the electric devices are close to each other, but the recognition accuracy is not high. The mathematical optimization algorithm generally has the problem of low solving efficiency.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an electricity utilization safety monitoring method and system based on electricity utilization characteristics, which are suitable for small-area scenes such as families or factories, can identify the on-off of the current electric equipment based on the electricity utilization data acquired in real time, and can monitor the safety of the electric equipment in a working state.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
an electricity safety monitoring method based on electricity utilization characteristics comprises the following steps:
receiving power utilization data and preprocessing the power utilization data to generate a power utilization parameter matrix;
identifying the current electric equipment in a working state based on a pre-trained electric equipment identification model according to the electric parameter matrix;
and for the electric equipment in the working state, carrying out state monitoring based on a pre-trained electric equipment state diagnosis model.
Further, the electricity consumption data comprises voltage data, current data and leakage current data.
Further, generating the electricity usage parameter matrix includes:
correcting waveform data of the electricity consumption data;
calculating power consumption parameters according to the power consumption data based on the corrected waveform data;
and sequencing the power utilization parameters according to time, and taking each type of power utilization parameter as a line to obtain a power utilization parameter matrix.
Further, the electric equipment identification model construction method comprises the following steps:
for various electric equipment, collecting electric data in the starting or stopping process;
and calculating the power utilization parameter matrix of various power utilization equipment in the starting or stopping process as training data, and training the power utilization equipment identification model based on the artificial neural network.
Further, the electric device state diagnosis model is constructed as follows:
for various electric equipment, respectively acquiring electric data of the electric equipment in a normal working state and an abnormal working state;
and calculating power utilization parameter matrixes of various power utilization equipment in normal working states and abnormal working states as training data, and training a power utilization equipment identification model based on an artificial neural network.
Furthermore, after the power utilization equipment is in the working state, data of corresponding time periods of start-stop events are eliminated, and state monitoring is carried out based on a pre-trained power utilization equipment state diagnosis model.
One or more embodiments provide an electricity safety monitoring system based on electricity utilization characteristics, which includes an electricity utilization data acquisition terminal and a cloud server; wherein the content of the first and second substances,
the power consumption data acquisition terminal is used for receiving power consumption data and preprocessing the power consumption data to generate a power consumption parameter matrix; identifying the current electric equipment in a working state based on a pre-trained electric equipment identification model according to the electric parameter matrix;
and the cloud server is used for carrying out state monitoring on the electric equipment in the working state based on a pre-trained electric equipment state diagnosis model.
Further, the system also comprises a client; and when monitoring the abnormal working state of the electric equipment, the cloud server generates alarm information and sends the alarm information to the client.
The above one or more technical solutions have the following beneficial effects:
according to the power utilization safety monitoring method, the power utilization parameter matrix is constructed based on the multiple types of power utilization data, so that the real power utilization of the power utilization equipment can be restored to a great extent, and the training and identification precision of a subsequent model are facilitated; the type of the running equipment is judged by identifying the starting and stopping states from the power utilization data, so that the identification accuracy is higher;
the power utilization safety monitoring system provided by the invention introduces edge calculation, applies the identification of the type of the power utilization equipment to the front-end power utilization data acquisition terminal, has small data volume and small data exchange volume transmitted to the cloud platform, so that the transmission rate is high, the overall working efficiency of the system is high, the real-time monitoring of the power utilization equipment is realized, the power utilization characteristics of the power utilization equipment are monitored in real time, the timely discovery of potential safety hazards can be ensured, and the safety early warning is realized.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of an electricity safety monitoring method based on electricity consumption characteristics according to one or more embodiments of the present invention.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment discloses an electricity utilization safety monitoring method based on electricity utilization characteristics, which is suitable for small-area scenes such as families and factories, and can be used for identifying the opening and closing of current electric equipment based on real-time collected electricity utilization data and carrying out safety monitoring on the electric equipment in a working state. The method specifically comprises the following steps:
step 1: and receiving various electricity utilization data at regular time, and preprocessing the electricity utilization data to obtain waveform data of each electricity utilization data.
The electric devices are roughly classified into: resistors including electric heaters, water dispensers, incandescent lamps, etc.; motors, including electric appliances such as refrigerators and washing machines; electronic, such as computers, televisions, etc.; and power electronic control, including air conditioner, mobile phone charger, etc.
The plurality of electricity usage data includes voltage, current, and leakage current. The step 1 specifically comprises:
step 1.1: and the voltage signal, the current signal and the leakage current signal are respectively processed by an overvoltage conversion module, a current conversion module and a leakage current conversion module to obtain a voltage millivolt signal, a current millivolt signal and a leakage current millivolt signal.
Step 1.2: the voltage millivolt signal, the current millivolt signal and the leakage current millivolt signal are respectively filtered by an anti-aliasing filter circuit to obtain the filtered voltage millivolt signal, the filtered current millivolt signal and the filtered leakage current millivolt signal.
Step 1.3: the analog/digital sampling circuit samples the filtered voltage millivolt signal, current millivolt signal and leakage current millivolt signal every time t (t is 1/F/N, wherein F is the power grid frequency, and N is the number of sampling points per cycle) to obtain a digital voltage signal, a digital current signal and a digital leakage current signal.
Step 1.4: and respectively arranging N digital voltage signals, N digital current signals and N digital leakage current signals according to a sampling sequence every time T (T is 1/F) passes to obtain a voltage signal waveform, a current signal waveform and a leakage current signal waveform.
Step 2: and calculating to obtain the electricity utilization parameters of a period of time at the current moment according to the waveform data of each electricity utilization data.
The step 2 specifically comprises:
step 2.1: the waveform data of the electricity consumption data is corrected. Specifically, the collected voltage waveform data, current waveform data and leakage current waveform data are corrected, wherein the correction includes denoising, filtering, gain compensation and the like, and corrected waveform data are obtained.
Step 2.2: and calculating the electricity utilization parameters according to the electricity utilization data based on the corrected waveform data. The preprocessing comprises Fourier transformation, effective value calculation, active power calculation, reactive power calculation, harmonic power calculation, power factor calculation and the like.
Step 2.3: and sequencing the electricity utilization parameters according to time to obtain an electricity utilization parameter matrix.
In the embodiment, the change trend of each electricity utilization parameter in a period of time is reflected by a matrix, and the period of time comprises a plurality of voltage cycles, and the period of time is a fixed value (20s) in the embodiment. Each column of the matrix represents the value of each electricity utilization parameter at a certain moment, and each row represents the variation trend of the value of one electricity utilization parameter along with time.
And when the power utilization parameter of one voltage period is obtained by receiving and processing each time, the power utilization parameter matrix is updated once, the obtained power utilization parameter in the new voltage period is filled to the tail of the matrix, and the power utilization parameter data of the voltage period which is filled into the matrix at first is deleted.
And step 3: and identifying the current electric equipment in the working state based on a pre-trained electric equipment identification model according to the electric parameters in the period of time.
The embodiment identifies the electric equipment in the working state by identifying the start and stop of the electric equipment.
The electric equipment identification model is constructed as follows:
(1) for various electric equipment, collecting electric data in the starting or stopping process;
as an implementation mode, because the power consumption parameters in different working states have different variation trends, long-term power consumption data of the power consumption equipment can be obtained, and clusters representing different working states are obtained based on a clustering algorithm, so that data related to start-stop events are divided from the power consumption data, and the subsequent model training is facilitated.
(2) Generating a power-on and power-off parameter matrix by adopting the same method as the step 1-2;
(3) and training an electric equipment identification model based on the neural network model according to the start-stop electric parameter matrixes of different electric equipment.
Based on the electric device identification model, the type of the electric device to be started or stopped and the corresponding time can be identified.
Taking an electric kettle as an example, the electricity utilization model of the electric kettle is similar to a pure resistance circuit, and harmonic current is not generated, so that the voltage phase is the same as the current phase, and the harmonic voltage phase is the same as the harmonic current phase. The resistance of the heating tube changes along with the temperature change (the resistivity of most metals increases along with the temperature rise), so the ratio of the current to the voltage in the startup process of the heating tube tends to decline and tends to be stable after the temperature is stabilized. The electricity parameter matrix can record the voltage variation trend, the current variation trend and the voltage-current ratio trend in the starting process of the electric kettle in detail. The starting process of the electric kettle can be identified by the artificial neural network after the electric parameter matrix is trained.
For the condition that more than two kinds of electric equipment are started simultaneously, the electric parameter matrixes of the electric equipment which are started simultaneously can be sent to a neural network for training, and the condition that the two kinds of equipment are started simultaneously can be identified.
And (3) inputting the electricity utilization parameter matrix obtained in the step (2) into an electricity utilization equipment identification model, so that the starting time and the stopping time of the electricity utilization equipment and the type of the electricity utilization equipment can be identified.
And 3, after the electric equipment in the working state is obtained based on the step 3, excluding data of the corresponding time period of the start-stop event, and carrying out next state monitoring.
And 4, step 4: and for the electric equipment in the working state, performing state monitoring based on a pre-trained state diagnosis model.
In this embodiment, a neural network for performing state monitoring on various types of electrical devices is trained in advance, and after the electrical devices in a working state are identified, state diagnosis is performed based on the corresponding neural network. And (4) eliminating power utilization data related to the starting event, and sending the power utilization parameter matrix to a pre-trained power utilization equipment identification model for calculation and analysis. And the neural network identifies the running working state of the specific electric equipment according to the matrix characteristics.
The electric equipment state diagnosis model is constructed as follows:
(1) respectively acquiring power utilization data of representative known power equipment in a normal working state and an abnormal working state; wherein, the abnormal working state can be obtained through simulation test.
(2) And (3) acquiring the electricity utilization parameter matrix of each piece of electric equipment in different working states as training data by adopting the same method in the step 1-2.
(3) And for each electric device, training a neural network based on the electric parameter matrix under different working states. The electrical parameter characteristics of the known electric equipment in the specified working state can be obtained through the neural network, and the used artificial neural network is not limited to a convolutional neural network or a cyclic neural network. And updating the weight parameters of the artificial neural network by using the obtained features. The trained neural network has the capability of recognizing the known working state of the known electric equipment. The recognition capability of the neural network can be increased by changing the working state of the electric equipment or using different electric equipment and repeating the steps.
In addition to the above state diagnosis based on the neural network, in order to prevent the omission of the fault identification of the electric equipment, for the high-power equipment (for example, an electric kettle, a blower, etc.), the present embodiment further records the starting time of the electric equipment, sets the maximum working time (for example, the maximum single working time of the electric kettle is 15 minutes), and determines that the electric equipment is in an abnormal condition if the electric equipment still works beyond the set maximum working time; if the leakage current is obviously increased during the starting period of the electric equipment, the electric equipment can be judged to generate the leakage current.
Example two
The purpose of this embodiment is to provide an electrical safety monitoring system.
An electrical safety monitoring system comprising: the system comprises a power consumption data acquisition terminal, a cloud server and a client.
Power consumption data acquisition terminal includes:
the power consumption data acquisition module is used for acquiring various power consumption data;
the power utilization data preprocessing module is used for preprocessing various power utilization data to obtain a power utilization parameter matrix;
and the electric equipment identification module is used for identifying the electric equipment in the current working state based on the electric parameter matrix.
Those skilled in the art can understand that the electricity consumption data acquisition terminal in this embodiment may be an existing device such as a smart meter and a distribution transformer terminal, and is not limited herein.
A cloud server comprising:
the state monitoring module is used for carrying out state monitoring according to the type of the electric equipment and corresponding electric data based on a pre-trained neural network;
and the alarm module is used for sending alarm information to the client when the state monitoring module monitors that the state is abnormal.
Those skilled in the art will appreciate that the alert information includes, but is not limited to, voice, text, and the like.
A client, comprising:
the data query module is used for acquiring the type and the working state of the equipment in the working state from the cloud server;
alarm module receives the alarm information that the high in the clouds server sent, according to the unusual operating condition early warning of discernment, can produce audible and visual alarm on the spot and also can push APP or SMS etc..
The transmission means for the electricity consumption data acquisition terminal to send data to the cloud server includes, but is not limited to, 3G, 4G, 5G, Wifi and ethernet.
The terminal transmits the matrix data to the cloud end through 5G communication, and the neural network of the cloud end accurately identifies normal operation and abnormal states of the electric equipment according to the mass feature library stored in the cloud end.
The system introduces edge calculation, applies the identification of the type of the electric equipment to the front-end electric data acquisition terminal, has small data volume and small data exchange volume transmitted to the cloud platform, thereby having high transmission rate and high overall working efficiency of the system and being beneficial to realizing the real-time monitoring of the electric equipment.
The steps related to the second embodiment correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (8)
1. An electricity safety monitoring method based on electricity utilization characteristics is characterized by comprising the following steps:
receiving power utilization data and preprocessing the power utilization data to generate a power utilization parameter matrix;
identifying the current electric equipment in the working state by identifying the starting and stopping of the electric equipment based on a pre-trained electric equipment identification model according to the electric parameter matrix;
for the electric equipment in the working state, carrying out state monitoring based on a pre-trained electric equipment state diagnosis model;
the electricity utilization parameter matrix reflects the variation trend of each electricity utilization parameter in a period of time, and the period of time comprises a plurality of voltage cycles; each column of the electricity utilization parameter matrix represents the value of each electricity utilization parameter at a certain moment, and each row represents the change trend of the value of one electricity utilization parameter along with the time; and when the power utilization parameter of one voltage period is obtained by receiving and processing each time, the power utilization parameter matrix is updated once, the obtained power utilization parameter in the new voltage period is filled to the tail of the matrix, and the power utilization parameter data of the voltage period which is filled into the matrix at first is deleted.
2. The electrical safety monitoring method based on electrical characteristics of claim 1, wherein the electrical data comprises voltage data, current data and leakage current data.
3. The electrical safety monitoring method based on electrical characteristics of claim 1, wherein generating the electrical parameter matrix comprises:
correcting waveform data of the electricity consumption data;
calculating power consumption parameters according to the power consumption data based on the corrected waveform data;
and sequencing the power utilization parameters according to time, and taking each type of power utilization parameter as a line to obtain a power utilization parameter matrix.
4. The electrical safety monitoring method based on electrical characteristics as claimed in claim 1, wherein the electrical equipment identification model construction method comprises:
for various electric equipment, collecting electric data in the starting or stopping process;
and calculating the power utilization parameter matrix of various power utilization equipment in the starting or stopping process as training data, and training the power utilization equipment identification model based on the artificial neural network.
5. The electrical safety monitoring method based on electrical characteristics as claimed in claim 1, wherein the electrical equipment state diagnosis model is constructed by the following steps:
for various electric equipment, respectively acquiring electric data of the electric equipment in a normal working state and an abnormal working state;
and calculating power utilization parameter matrixes of various power utilization equipment in normal working states and abnormal working states as training data, and training a power utilization equipment identification model based on an artificial neural network.
6. The electrical safety monitoring method based on electrical characteristics as claimed in claim 1, wherein after the electrical equipment is in the working state, the data of the corresponding time period of the start-stop event is also excluded, and then the state monitoring is performed based on a pre-trained electrical equipment state diagnosis model.
7. An electricity utilization safety monitoring system based on electricity utilization characteristics is characterized by comprising an electricity utilization data acquisition terminal and a cloud server; wherein the content of the first and second substances,
the power consumption data acquisition terminal is used for receiving power consumption data and preprocessing the power consumption data to generate a power consumption parameter matrix; identifying the current electric equipment in the working state by identifying the starting and stopping of the electric equipment based on a pre-trained electric equipment identification model according to the electric parameter matrix;
the cloud server is used for carrying out state monitoring on the electric equipment in the working state based on a pre-trained electric equipment state diagnosis model;
the electricity utilization parameter matrix reflects the variation trend of each electricity utilization parameter in a period of time, and the period of time comprises a plurality of voltage cycles; each column of the electricity utilization parameter matrix represents the value of each electricity utilization parameter at a certain moment, and each row represents the change trend of the value of one electricity utilization parameter along with the time; and when the power utilization parameter of one voltage period is obtained by receiving and processing each time, the power utilization parameter matrix is updated once, the obtained power utilization parameter in the new voltage period is filled to the tail of the matrix, and the power utilization parameter data of the voltage period which is filled into the matrix at first is deleted.
8. The electrical safety monitoring system based on electrical characteristics of claim 7, wherein the system further comprises a client; and when monitoring the abnormal working state of the electric equipment, the cloud server generates alarm information and sends the alarm information to the client.
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