CN102279326B - Electrical appliance recognition method and household user carbon emission metering system applying same - Google Patents

Abstract

The invention discloses an electrical appliance identification method, the core of which is to identify the electrical appliance according to the matching result of the current characteristic parameter value. The present invention also provides a carbon emission metering system for household users applying the above method, comprising: the bus voltage sampling module is respectively connected to the input terminals of the phase detection module, the signal conditioning module and the amplitude detection module; the bus current detection module is connected to the input of the signal conditioning module The output terminals of the phase detection module, the signal conditioning module and the amplitude detection module are connected to the input terminals of the electrical identification module, and the output terminals of the electrical identification module are connected to the carbon metering module. The invention measures carbon emissions on the basis of accurately identifying electrical appliances, helps people establish awareness of energy saving, emission reduction and low-carbon consumption, and corrects incorrect electricity consumption habits.

Description

Electrical Appliance Identification Method and Household User Carbon Emission Metering System Applying the Method

technical field

The invention relates to a household electric load identification and carbon emission monitoring and measurement system, in particular to an electrical appliance identification method and a household user carbon emission measurement system applying the method.

Background technique

With the development of electronic technology, multi-functional energy meters with single-chip microcomputers as the core have gradually become popular. This type of energy meters has high measurement accuracy and calculation speed. Record the consumption, save the maximum demand of electric energy, set parameters, etc. But this is only limited to measuring the power consumption of all electrical appliances of the user, and cannot be accurate to each electrical appliance. And this type of meter is only based on the voltage and current amplitude of the power grid to measure and count the electric energy in the measurement method, so the function is relatively simple. The communication capability of the above meter network is very poor or does not have network communication capability at all, and it is difficult to realize communication and data synchronization with the network host computer. The existing electrical appliance identification technology can only control the relay power-off processing when the user has a high-power electrical appliance loaded (patent number 200620127335.0 electronic energy meter with electrical appliance identification function); another patent (patent number 201010250565.7- A method of using smart grid to regulate users' carbon emissions) proposes a method to identify whether the user's power source is from polluting power or clean power, and analyze the energy consumption and carbon emissions. The existing technology does not realize the The function of electrical identification.

If it is necessary to detect the power consumption of each electrical appliance, the current general method is to install voltage and current sensors at the power inlet of each household appliance, and then measure the power based on the collected data. Although the corresponding purpose can be achieved like this, the price is on the high side, the installation is complicated, and the feasibility is poor.

In the current situation, household users lack the awareness of energy conservation, emission reduction and low-carbon consumption, and little or no consideration is given to the additional carbon emissions generated during the use of some electrical appliances, which may lead to excessive carbon emissions. Due to the low awareness of low-carbon consumption in the whole society and the misleading of certain aspects, it will lead them to unconsciously adopt some unreasonable lifestyles and destroy the construction of the entire environment unconsciously.

Contents of the invention

The purpose of the present invention is to provide a method for identifying electrical appliances, which can accurately identify the electrical appliances in use.

Another object of the present invention is to provide a carbon emission metering system for household users using the above method. Firstly, the electrical appliances in use are identified, and the energy consumption and carbon emissions of the electrical appliances are measured. The whole system is easy to install and simple in structure.

The technical solution adopted by the present invention to solve its technical problems is:

A method for identifying electrical appliances and a carbon emission metering system for household users using the method: including a bus voltage sampling module, a bus current detection module, a phase detection module, a signal conditioning module, an amplitude detection module, a power supply module, a human-computer interaction module, and an electrical appliance Identification module, carbon accounting module.

The bus voltage sampling module is through a voltage sensor (the voltage sensor can be a voltage transformer, a voltage Hall sensor, etc.), and the bus current detection module is through a current sensor (the current sensor can be a current transformer, a current Hall sensor, a shunt, etc.) Convert the high voltage and large current on the grid side into a voltage signal, transmit it to the phase detection module, signal conditioning module and amplitude detection module, and realize electrical isolation.

The phase detection module includes a buffer and a hysteresis comparator to detect the zero-crossing point of the signal and provide a time reference for sampling and feature parameter extraction; the signal conditioning module includes a buffer circuit, a signal gain/attenuation part, a signal superposition part and an anti-aliasing filter , adjust the sampling signal to an appropriate range; the amplitude detection module includes a buffer and a peak hold part, and detects the maximum value of the signal.

The power module uses an AC/DC switching converter to provide power for the entire system, which is more efficient than a linear converter.

The electrical appliance identification module is connected with the phase detection module, signal conditioning module, amplitude detection module and the human-computer interaction module, and is connected with the carbon metering module through the serial port communication. Through the analysis and calculation of the current and voltage data, according to the characteristic parameters of the electrical appliances Fuzzy identification realizes the identification of electrical appliances being used by home users, and can record the power consumption of each electrical appliance, and realize super power processing and power limit of electrical appliances. The serial port communication adopts the network addressing mode to realize the communication between the single serial port and the computer and the carbon metering module.

The characteristic parameters of electrical appliances detected by the above electrical appliance identification module include current peak value, current average value, current effective value, current fundamental wave, current harmonic amplitude, current harmonic phase, apparent power of electrical appliances, active power, and electrical consumption Electric curve, current harmonic length, etc.

The electrical appliance identification method adopted by the above electrical appliance identification module is a score-based fuzzy identification algorithm. The corresponding score is obtained according to the matching degree between the characteristic parameters of the electrical appliances and the corresponding parameters in the database, and the recognition result is determined by comparing the score with the passing score.

The above-mentioned feature database contains feature information of all types of household electrical appliances, and the feature database can dynamically change in real time according to changes in household appliances.

The above-mentioned dynamic change of the characteristic data refers to real-time updating of the appliance characteristic parameter scores in the database according to the situation of each household appliance and loading of new appliance data when a new appliance is detected.

The liquid crystal controller contained in the human-computer interaction module 1 can realize the interface display content of the touch liquid crystal screen, touch sensing processing and communication with the main controller for electrical identification. The content displayed on the LCD interface includes the statistics of the total energy consumption of household appliances, the household appliances used in real time and the detailed statistics of individual household appliances.

The carbon metering module is connected with the electrical appliance identification module and the human-computer interaction module 2 by means of serial port communication, and is equipped with an Internet communication interface. According to a certain conversion standard, the power consumption of electrical appliances is converted into the corresponding carbon value, so as to quantify the abstract carbon emission process and monitor and calculate household energy consumption. Through the touch liquid crystal display included in the human-computer interaction module 2, the results of the current carbon emission statistics are presented graphically, and useful information is given in the form of information. It has three functions of accounting monitoring, personal inquiry and intelligent suggestion, which can analyze the direction of household carbon consumption and put forward reasonable suggestions.

The technical effect of the present invention is reflected in: adopting the non-invasive monitoring method, there is no need to install a detection device at the power supply entrance of each electrical appliance, but only to install voltage and current sensors at the total entry end of the user's electric wires, to collect voltage and current data in real time, and use software Analysis is carried out to determine the power consumption of each household appliance, the investment is low, and the monitoring device can continuously capture the information of the change of power load every moment. Fuzzy identification is carried out according to the characteristic parameters of electrical appliances, which realizes the identification of electrical appliances being used by household users, and records the power consumption of each electrical appliance, and can realize super power processing and power limitation of electrical appliances. At the same time, the data is transmitted to the carbon metering module. According to a certain conversion standard, the consumption of electrical appliances is converted into the corresponding carbon value for recording and processing, which will help people establish awareness of energy conservation, emission reduction and low-carbon consumption, and correct incorrect ones. Make your own contribution to the environment by using electricity habits.

Description of drawings

Fig. 1 is a schematic diagram of the overall structure of the present invention.

Fig. 2 is a circuit diagram of a bus current detection module and a signal conditioning module of an example of the present invention.

Fig. 3 is a circuit diagram of a bus voltage sampling module, a phase detection module, a signal conditioning module, and an amplitude detection module according to an example of the present invention.

Fig. 4 is a circuit diagram of a power module of an example of the present invention.

Fig. 5 is a circuit diagram of a processor (MCU) of an electrical identification module of an example of the present invention.

Fig. 6 is a software flowchart of electrical appliance identification in an example of the present invention.

Fig. 7 is a schematic diagram of the fuzzy scoring principle of an example of the present invention: scoring method based on error language variable relationship.

Fig. 8 is a flow chart of an electrical appliance identification method in an example of the present invention.

Detailed ways

Provide an embodiment below in conjunction with accompanying drawing:

As shown in Figure 1, an electrical appliance identification method and a household user carbon emission metering system applying the method: including a bus voltage sampling module, a bus current detection module, a phase detection module, a signal conditioning module, an amplitude detection module, a power supply module, Human-computer interaction modules 1 and 2, electrical appliance identification module, and carbon metering module.

The bus voltage sampling module is through a voltage sensor (the voltage sensor can be a voltage transformer, a voltage Hall sensor, etc.), and the bus current detection module is through a current sensor (the current sensor can be a current transformer, a current Hall sensor, a shunt, etc.) Convert the high voltage and large current on the grid side into low voltage and small current, and transmit it to the phase detection module, signal conditioning module and amplitude detection module, and realize electrical isolation.

As shown in Figure 2, the current sensor in this example adopts the current closed-loop Hall sensor CSM010SY, the primary side current measurement range is -20A to 20A, the secondary side rated output voltage is 4V, the linearity is less than 0.1%, and the accuracy is less than 0.7%. The latter stage is a gain/attenuation two-way conditioning circuit, which uses an op amp op07 with ultra-low offset, low input bias current, low offset voltage drift, and ultra-stable to ensure the best accuracy of the output signal and convert the large current on the bus The voltage signal is sent to the electrical identification module.

As shown in Figure 3, the voltage sensor in this example uses a voltage transformer TVA1421-01, the rated current is 6mA/6mA, and the nonlinearity is less than 0.2%. After the buffer, the hysteresis zero-crossing comparator, the amplitude detector and the corresponding conditioning circuit, the large voltage on the user's incoming line is converted into a voltage signal and sent to the electrical identification module.

The phase detection module is used to include a buffer and a hysteresis comparator to detect the zero-crossing point of the signal and provide a time reference for sampling and feature parameter extraction; the signal conditioning module includes a buffer circuit, a signal gain/attenuation part, a signal superposition part and anti-aliasing The filter adjusts the sampling signal to an appropriate range; the amplitude detection module includes a buffer and a peak hold part, which is used to detect the maximum value of the signal.

As shown in Figure 4, the power module uses LH10-10A15, AC/DC switching converter; LM2576, DC/DC switching converter; AS1117, linear DC converter to supply power for the entire circuit, which not only ensures the conversion efficiency is guaranteed Stability of power supply.

As shown in Figure 5, the main control chip of the electrical identification module adopts ADuC7026, which integrates a 1MSPS, 12-bit, multi-channel high-performance ADC data acquisition system in a single chip, and the core is ARM7TDMI, which is a 16-bit/32-bit RISC machine. The highest performance peak is as high as 41MIPS. 8KB of SRAM and 62KB of non-volatile Flash/EE memory are integrated on-chip. The system developed based on this chip can easily complete the identification of electrical appliances, power measurement, data transmission, event management, etc., with high precision and high reliability. The human-computer interaction module 1 adopts a 5.7-inch LCD touch screen, which makes the data display more intuitive and the operation more convenient.

Figure 6 is a flowchart of the electrical identification work. First, use the 12-bit ADC on the ADuC7026 chip to sample the voltage and current signals. In the data sampling stage, in order to keep the data from being disturbed when the system is running, the system can only collect data at an appropriate time. During data processing, data sampling needs to be prohibited. However, in order to ensure a certain consistency in each data processing process, there must be a benchmark at the beginning of sampling. Since this system has done zero-crossing detection on the grid voltage, the moment when the voltage just crosses zero provides the possibility for data sampling. Based on the above analysis, the specific method of implementing data sampling is: set up the flag bit Data hold to indicate whether the data needs to be held, set it to 1, the system thinks it needs to be held, it only occurs when the data is collected 256 points, set it to 0, it is considered Sampling can be performed at a certain moment, which only occurs when the data processing is completed; the flag bit AD enable is set to indicate whether the AD sampling time has arrived, and setting 1 only occurs when the Data hold is 0 and the voltage zero-crossing time arrives. Setting to 0 occurs when Data hold is 1. In order to meet the requirement of sampling 256 points in one cycle under the grid frequency of 50Hz, a timer of 20000/256us is set. After the sampling is completed, the sampling data of this time is compared with the sampling data of the previous time to obtain the sampling data of the newly added electrical appliances this time, and then the corresponding characteristic parameters are extracted from the sampling data, and the FFT transformation is performed on the sampled data at the same time , and extract the corresponding feature parameters again. The characteristic parameters of electrical appliances used in this example include current peak value, current average value, current RMS value, current fundamental wave, current harmonic amplitude, current harmonic phase, active power of electrical appliances, and current harmonic length. The current peak value and the current average value can be obtained by processing the current data in real time during sampling. The characteristic parameters such as current effective value and fundamental wave can be obtained after Fourier FFT transformation. After the FFT transformation, the obtained data has not been normalized. In order to ensure the consistency of the feature parameters in the access process, the data needs to be normalized. The normalization criterion is that the amplitude component of the fundamental wave is 100, and the other harmonic components are adjusted proportionally. In order to restore the normalized data, it is also necessary to store the power information of each consumer. Then, compare these parameters with the corresponding parameters in the database. In order to achieve the purpose of electrical appliance identification, this example adopts the scoring method based on the error language variable relationship shown in Figure 7. First, a fuzzy algorithm is used to compare a certain characteristic parameter with the corresponding value in the existing database. If the characteristic value matches the existing value very well, a high score will be given, and the score will be scored as the matching degree decreases. also decreased accordingly. Then, for the fuzzy recognition results of each characteristic parameter, there is a corresponding matching coefficient, and the matching coefficient is multiplied by the score of the parameter to obtain the score of the parameter. Finally, add up the scores of all characteristic parameters to get the matching score of the electrical appliance. For each electrical appliance in the database, a corresponding score will be obtained, and the comparison between the maximum score and the passing score determines whether the electrical appliance identification is successful. If the maximum score is higher than the defined passing line, it is considered that the system appliance identification is successful, and the result is the electrical appliance corresponding to the maximum score; if the maximum score does not exceed the passing line, it is considered that there is no such user in the database. Electrical appliances need to be processed in the next step. In this example, the passing score is positioned as the minimum matching success requirement, and is not limited thereto, and may be adjusted according to the recognition accuracy rate of multiple test results.

FIG. 8 is a flow chart of electrical appliance identification. Firstly, it is judged whether the useful appliance is turned on or off according to the power change of the entrance terminal. If the power remains unchanged, the recognition result at the previous detection moment is the current recognition result; further, the recognition result can be verified, that is, the current recognition result The reference current characteristic parameter value of each electrical appliance in the system is summed, and compared with the extracted current characteristic parameter value, if the comparison result meets the minimum recognition success requirement, the recognition is successful, otherwise the recognition fails. The minimum recognition success requirement is determined based on the accuracy of multiple test results. Since the opening recognition and closing recognition of electrical appliances are two similar processes, the recognition process is described below by taking the opening recognition of useful electrical appliances as an example:

It starts to detect that the power of the wire inlet terminal increases, indicating that the useful electrical appliance is turned on. At this time, the characteristic parameters of the opened electrical appliances are extracted according to the method of extracting characteristic parameters described above, and first compared with the corresponding parameters of a single electrical appliance in the database. Use the scoring method based on the error language variable relationship mentioned above to score the electrical appliances. In order to illustrate more clearly, the following is an example of scoring:

Table 1 Information about the reference current harmonics of the display

As shown in Table 1, it is the reference current harmonic related information displayed in the database of an example of the present invention. Harm_length is the total harmonic length; Harm_num is the corresponding harmonic order; FFT_R is the imaginary part of the harmonic after FFT transformation; FFT_I is the real part of the harmonic after FFT transformation.

Table 2 Detected parameters related to current harmonics of newly used electrical appliances

When a newly connected electrical appliance is identified, it is compared with the corresponding parameters of each electrical appliance stored in the database, and a score is given. As shown in Table 2, it is a newly identified parameter related to the electrical current harmonic of an example of the present invention. Here, this parameter is used as an example to illustrate the scoring process. The total weight of the current harmonic is 0.8, which is the percentage system 80 points, and the weight of the harmonic length is 0.3, that is, 80*0.3=24 points; the distribution weight of each harmonic is assigned with reference to the harmonic parameters in the database, such as this time and the For comparison on the display, the weight assigned to each harmonic is 0.7/6=0.117, and then compare the corresponding parameters of the newly identified electrical appliances with it to obtain a score, and finally add all the scores of this parameter , and then add up the scores of all parameters to get the following table:

Table 3 Scoring table for newly used electrical appliances

It can be seen from Table 3 that the electric fan has the highest score of 90 points, and the rest of the scores are all unqualified, so it can be seen that the newly connected electrical appliance is recognized as an electric fan. At the same time, start to record the corresponding parameters such as the power of the electrical appliance. The above-mentioned single electrical appliance identification process is the first stage of identification. If all the scores are unqualified, the recognition fails and the second stage of recognition is transferred.

The second stage of identification: first analyze the collected data information of electrical appliances, read out the record of each electrical appliance parameter in the database, and carry out the permutation and combination of different electrical appliance parameters through the power information and harmonic information of each appliance , taking the harmonic length of electrical appliances as the priority combination principle, and according to the number of electrical appliances from less to more, the combination parameters of different electrical appliances are obtained. In fact, the combination process of multiple electrical appliances is a process of solving multidimensional linear equations. Afterwards, use a method similar to the identification of a single electrical appliance to identify multiple electrical appliances that are turned on at the same time. If the recognition is successful, start to record parameters such as the power of the electrical appliance and display them. If all the scores are unacceptable, the recognition fails and the next stage of recognition is transferred.

The third stage of identification: none of the above identifications is successful, indicating that the characteristic parameters of the newly connected electrical appliance are not stored in the database. At this time, the characteristic parameters of the new electrical appliance will be dynamically loaded into the database and prompted to enter the electrical appliance The name, so far the whole identification process is completed.

During the recognition process, due to external interference, there will be a certain false recognition rate. In order to prevent misidentification of the system, even if no new electrical appliances are reconnected to the system, the system is still in the real-time verification state at this time. In this state, the work that the system needs to do is to verify whether the identification result of the electrical appliance is correct. The verification method is to fuzzily compare the characteristic data information after the identification result is superimposed with the real-time information to obtain the identification result. If the identification result is incorrect, Then clear the identification results, re-identify all electrical appliances, and transfer to the first stage above. If it is correct, continue processing; then, update the weights of the database feature parameters from real-time data to make the system more localized and update the database. The process of the feature parameter weight in the middle is actually the process of maximizing the recognition result score of the correct electrical appliance; finally, the electrical appliance data is sent to the computer and the home client.

Table 4 Schematic query of electrical energy consumption records

As shown in Table 4, after the electrical appliance is successfully identified, the corresponding energy consumption will be recorded. The query record format of the energy consumption of the appliance is: the name of the appliance, the time period of the appliance, the cumulative single use time of the appliance, and the energy consumption of the appliance. The power of electrical appliances, the energy consumption of electrical appliances, etc., and can generate corresponding daily statistics, monthly statistics, annual statistics, etc. And it can limit the power of the corresponding electrical appliances. If the limit is exceeded, the power will be cut off.

The identification process of turning off the electrical appliance is similar to that of turning on the electrical appliance, so we won't go into details here.

The main control microprocessor of the carbon metering module is selected as the ARM CortexM3 core chip, and the human-computer interaction module 2 uses a 3.2-inch TFT touch LCD screen. The home client provides monitoring and accounting of the carbon emission value of home energy consumption, query services and intelligent environmental protection suggestions. The first is to monitor and calculate household energy consumption and to calculate carbon emissions. The second is to provide real-time query services, which can display the current carbon emission value and carbon exchange rate in real time. The third is to conduct "smart" analysis based on the current carbon emission quota and related consumption information, so as to provide users with reasonable suggestions and avoid paying environmental protection fees due to excessive carbon emission values at the end of the quarter. The home client updates the current carbon emission information by connecting to the Internet. It can be connected with home-related devices through the Internet to form a network, and can access the database to query past carbon emission records, update current carbon emission information in real time, and remind customers to reduce non-low-carbon living behaviors in time. Because it can only provide users with the function of querying data, and the underlying layer is invisible to customers, it can effectively avoid malicious damage to carbon measurement.

Although the content of the present invention has been described in detail through the above preferred examples, it should be understood that the above description should not limit the present invention. Various modifications and alterations to the present invention will become apparent to those skilled in the art upon reading the foregoing disclosure. Therefore, the protection scope of the present invention should be defined by the appended claims.

Claims (5)

1. electrical appliance recognition, is specially:

(1) from the current current characteristic parameter value of current draw of home dwelling electric wire house lead in end, described current characteristic parameter value comprises electrical appliance active power;

(2) judge whether current power and the last power constantly that detects change, if power is constant, front one-time detection recognition result is constantly current recognition result, finishes; If the increase of output power, shows to have new electrical equipment to be used, enter step (3); If power reduction, showing has electrical appliance to stop using, and enters step (6);

(3) current current characteristic parameter value and last detection current characteristic parameter value constantly is poor, obtain newly increasing electric current characteristic ginseng value;

(4) by newly increasing electric current characteristic ginseng value, mate with the reference current characteristic ginseng value of each electrical equipment that may use respectively, if Optimum Matching result meets minimum the match is successful requirement, electrical equipment corresponding to Optimum Matching result is the electrical equipment newly increasing, the last electrical equipment that detects identification constantly adds the electrical equipment of new use as current recognition result, finishes; Otherwise the electrical equipment that may use combines each, the reference current characteristic ginseng value of the electrical equipment in each combination is done and, enter step (5);

(5) by newly increasing electric current characteristic ginseng value, mate with the reference current characteristic ginseng value sum of each combination respectively, if Optimum Matching result meets minimum the match is successful requirement, electrical equipment combination corresponding to Optimum Matching result is the electrical equipment of new use, the last electrical equipment that detects identification constantly adds the electrical equipment of new use as current recognition result, finishes; Otherwise recognition failures, finishes;

(6) current current characteristic parameter value and last detection current characteristic parameter value constantly is poor, obtain stopping using electric current characteristic ginseng value;

(7) will stop using electric current characteristic ginseng value to mate with the reference current characteristic ginseng value of each electrical equipment that may use respectively, if Optimum Matching result meets minimum the match is successful requirement, electrical equipment corresponding to Optimum Matching result is withdraw from service electrical equipment, the last electrical equipment that detects identification is constantly removed withdraw from service electrical equipment as current recognition result, finishes; Otherwise the electrical equipment that may use combines each, the reference current characteristic ginseng value of the electrical equipment in each combination is done and, enter step (8);

(8) will stop using electric current characteristic ginseng value to mate with the reference current characteristic ginseng value sum of each combination respectively, if Optimum Matching result meets minimum the match is successful requirement, electrical equipment combination corresponding to Optimum Matching result is withdraw from service electrical equipment, the last electrical equipment that detects identification is constantly removed withdraw from service electrical equipment as current recognition result, finishes.

2. electrical appliance recognition according to claim 1, it is characterized in that, also current recognition result is verified in the following manner: the reference current characteristic ginseng value of each electrical equipment in current recognition result is done and, itself and the current current characteristic parameter value extracting are compared, if comparative result meets successfully requirement of minimum identification, identify successfully, otherwise recognition failures.

3. electrical appliance recognition according to claim 1, it is characterized in that, described current characteristic parameter value also comprises or combination in current peak, electric current average, current effective value, current first harmonics, current harmonics amplitude, current harmonics phase place, current harmonics length.

4. electrical appliance recognition according to claim 3, it is characterized in that, employing is related to that based on error linguistic variable scoring method characterizes the matching degree of the reference current characteristic ginseng value of each current characteristic parameter value and electrical equipment, again the score value of each current characteristic parameter value is weighted to summation, obtains the matching result of current current characteristic parameter value and this electrical equipment.

5. electrical appliance recognition according to claim 1, is characterized in that, the electrical equipment that may use combines from less to more according to quantity each.

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