CN111025011A - Non-invasive electrical appliance identification method based on harmonic similarity algorithm - Google Patents
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Abstract
The invention discloses a non-invasive electrical appliance identification method based on a harmonic similarity algorithm. The method comprises the following steps of 1: firstly, carrying out a learning process on an electric appliance to be identified, and measuring the active power and the reactive power of fundamental wave and the active power and the reactive power of each harmonic of each sample electric appliance under standard conditions in the learning process; step 2: initializing the operation times N of the electrical appliance identification algorithm to enable N to be 0, and starting the operation for the Nth to be N +1 times; and step 3: acquiring time domain data of voltage and current of unknown electrical appliances on a bus; and 4, step 4: converting the acquired time domain data of the voltage and the current into frequency domain data through Fast Fourier Transform (FFT); and 5: and calculating to obtain the total fundamental wave, the active power and the reactive power of each harmonic wave in the current power utilization state according to the current and voltage harmonic waves of different times in the frequency domain data. Compared with the prior art, the harmonic similarity-based non-invasive electrical appliance identification method provided by the invention has wider application occasions.
Description
Technical Field
The invention relates to the technical field of electrical appliance identification research under a non-invasive detection system, in particular to a non-invasive electrical appliance identification method based on a harmonic similarity algorithm.
Background
The monitoring and statistics of the power utilization behaviors can not only provide effective safe power utilization reminding for users, but also provide reliable analysis data for governments, electrical appliance manufacturers and the like. Through research on electricity utilization behaviors, an effective electricity utilization behavior data acquisition and load accurate identification scheme and algorithm are determined, and the method has practical and long-term significance for improving electricity utilization safety and promoting industrial change.
At present, in a traditional electrical appliance identification method, an intrusive load monitoring method is mostly adopted, the traditional intrusive load monitoring needs to install a detection device in an electrical appliance inside a target to be detected to obtain on-off state information and the like of the electrical appliance, however, monitoring equipment has certain manufacturing cost, related overhaul and maintenance are needed in the using process, the cost is increased, and the monitoring equipment needs to enter a user home in the installing and maintaining process, so that normal life is influenced, and large-scale application is difficult.
Disclosure of Invention
The invention provides a non-invasive electrical appliance identification method based on a harmonic similarity algorithm, which can be identified by adopting a similarity index under the condition that a plurality of electrical appliances can work simultaneously without entering the home of a user.
In order to achieve the purpose, the invention provides the following scheme:
a single-phase electric appliance identification method based on harmonic power similarity comprises the following steps:
step 1: selecting sample electric appliances, firstly carrying out a learning process on the electric appliances to be identified, and measuring the active power, the reactive power of the fundamental wave and the active power and the reactive power of each harmonic of each sample electric appliance under standard conditions in the learning process;
step 2: initializing the operation times N of the electrical appliance identification algorithm to enable N to be 0, and starting the operation for the Nth to be N +1 times;
and step 3: acquiring time domain data of voltage and current of unknown electrical appliances on a bus;
and 4, step 4: converting the acquired time domain data of the voltage and the current into frequency domain data through Fast Fourier Transform (FFT);
and 5: according to the current and voltage harmonics of different times in the frequency domain data, calculating to obtain the total fundamental wave, the active power and the reactive power of each harmonic in the current power utilization state;
step 6: giving identification similarity data to the identification result through the constructed similarity function;
and 7: judging whether the result similarity is greater than or equal to 0.9, if so, outputting the identification result and the similarity; if not, judging whether the arithmetic operation times N are more than 10 times, and if so, outputting the current optimal recognition result and similarity; if not, the operation returns to the step 2 to carry out the operation again.
Optionally, the step 1: selecting sample electric appliances, firstly carrying out a learning process on the electric appliances to be identified, and measuring the active power, the reactive power of the fundamental wave and the active power and the reactive power of each harmonic of each sample electric appliance under a standard condition in the learning process, wherein the maximum times of each harmonic are seven times.
Optionally, the step 5: giving identification similarity data to the identification result through the constructed similarity function, and specifically comprising the following steps:
constructing a similarity function;
substituting the power values of the fundamental wave and each harmonic wave learned in the learning process and the power values of the fundamental wave and each harmonic wave of the unknown electrical appliance in the measuring process into the similarity function;
and solving the similarity function to obtain the power utilization condition of the current monitoring target.
Optionally, the constructing the similarity function specifically includes:
establishing a state matrix A;
calculating to obtain 2n combined active power deviation vectors delta P according to the state matrix A, the harmonic power of each sample electric appliance learned in the learning process and the power of the 2k-1(k is 1,2, 3.) subharmonic on the bus in the actual situation obtained in the measuring process2k-1Active power deviation vector delta Q2k-1；
According to the active power deviation vector delta P2k-1The reactive power deviation vector delta Q2k-1Each kind of the prescription is calculated respectivelyRelative deviation vector delta P of active power of case and actual situation2k-1 relRelative deviation vector delta Q of reactive power2k-1 rel；
Relative deviation vector delta P according to active power2k-1 relRelative deviation vector delta Q of reactive power2k-1 relA similarity vector S is calculated.
Optionally, the solving of the similarity function may obtain a power consumption condition of the current monitoring target, and specifically includes:
and selecting the electric appliance working state row vector in the state matrix A corresponding to the element closest to 1 in the similarity vector S as an identification result.
Compared with the prior art, the technology has the following beneficial effects:
the invention provides a non-invasive electrical appliance identification method based on a harmonic similarity algorithm, which comprises the steps of firstly carrying out a learning process on an electrical appliance to be identified, and measuring the active power and the reactive power of fundamental wave, the reactive power and the active power and the reactive power of some higher harmonics of each sample electrical appliance under standard conditions in the learning process; acquiring time domain data of voltage and current of unknown electrical appliances on a bus; converting the acquired time domain data of the voltage and the current into frequency domain data through Fast Fourier Transform (FFT); calculating active and reactive data of each harmonic in the current power utilization state according to current and voltage harmonics of different times in a frequency domain; and giving identification similarity data to the identification result through the designed similarity function. The specific identification process is as follows: (i) establishing a similarity function; (ii) substituting the power values of the fundamental wave and each harmonic wave learned in the learning process and the power values of the fundamental wave and each harmonic wave of the unknown electrical appliance in the measuring process into the similarity function; (iii) and solving the similarity function to obtain the power utilization condition of the current monitoring target. And matching the obtained active power data and the reactive power data of each subharmonic of the electric appliance with the data obtained in the previous learning process, and identifying the type of the sample electric appliance.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a non-invasive electrical appliance identification method based on a harmonic similarity algorithm according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating the distribution of the harmonic power of the first electrical appliance according to the present invention;
FIG. 3 is a diagram illustrating the distribution of the harmonic power of the second electrical appliance according to the embodiment of the present invention;
FIG. 4 is a diagram illustrating the distribution of the power of each harmonic in the third electrical appliance according to the embodiment of the present invention;
fig. 5 is a distribution diagram of the power condition of each harmonic of the fourth electric appliance according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a single-phase electric appliance identification method based on a harmonic power similarity algorithm, which can be identified by adopting a similarity index under the condition that a plurality of electric appliances can work simultaneously without entering the home of a user.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of a non-intrusive electrical appliance identification method based on a harmonic similarity algorithm according to an embodiment of the present invention, and as shown in fig. 1, the method is intended to identify a current power utilization condition of a monitoring object by collecting related data of an outdoor bus of the monitoring object, and includes the following steps:
step 1: firstly, performing a learning process on the electric appliances to be identified, and measuring the active power and the reactive power of fundamental wave, the reactive power and the active power and the reactive power of some higher harmonics of each sample electric appliance under standard conditions in the learning process; the number of harmonics to be measured of the learning object can reach a seventh harmonic.
Step 2: acquiring time domain data of voltage and current of unknown electrical appliances on a bus;
and step 3: converting the acquired time domain data of the voltage and the current into frequency domain data through Fast Fourier Transform (FFT);
and 4, step 4: calculating active and reactive data of each harmonic in the current power utilization state according to current and voltage harmonics of different times in a frequency domain;
and 5: and giving out a recognition similarity value to the recognition result through the constructed similarity function.
In the step (5), the matching process is as follows: (i) establishing a similarity function; (ii) substituting the power values of the fundamental wave and each harmonic wave learned in the learning process and the power values of the fundamental wave and each harmonic wave of the unknown electrical appliance in the measuring process into the similarity function; (iii) and solving the similarity function to obtain the power utilization condition of the current monitoring target.
Step 6: and matching the obtained active power data and the reactive power data of each subharmonic of the electric appliance with the data obtained in the previous learning process, and identifying the type of the sample electric appliance.
N electrical appliances are set to participate in identification, and the specific identification process is as follows:
the first step is as follows: the initialization state makes the arithmetic operation number N equal to 0. Firstly, performing a learning process on the electric appliances to be identified, and measuring the active power and the reactive power of fundamental wave, the reactive power and the active power and the reactive power of some higher harmonics of each sample electric appliance under standard conditions in the learning process; the number of harmonics to be measured of the learning object can reach a seventh harmonic. Pn2k-1Represents the 2k-1 th harmonic active power of the nth sample appliance learned in the learning process,Qn2k-1the 2k-1 th harmonic reactive power of the nth sample appliance learned in the learning process is shown, where k is 1,2,3, 4.
The second step is that: let the arithmetic operation number N be N + 1. Acquiring voltage and current time domain data of unknown electrical appliances on a bus, converting the acquired voltage and current time domain data into frequency domain data through Fast Fourier Transform (FFT), and calculating active and reactive data values P of 2k-1 harmonic waves in the current power utilization state according to current and voltage harmonic waves of different times in the frequency domain2k-1 mea，Q2k-1 meaWherein k is 1,2,3, 4.
The third step: establishing a similarity function, namely firstly establishing a state matrix A in the following form:
each element in the A matrix is 0 or 1, and each row represents the working state combination of an electric appliance; since n electrical appliances participate in the identification, each row has n elements, wherein 1 in each row represents that the corresponding electrical appliance works, and 0 represents that the corresponding electrical appliance does not work, for example, in the 2n-1 th row, the element is (111.. 10), the first n-1 elements are 1, and the last element is 0, which indicates that the first n-1 electrical appliances are working and the nth electrical appliance does not work; the rows in the array A are arranged from top to bottom according to the binary coding rule, and the total number is 2nA combination form, which shows that all possible working conditions of the n electric appliances are 2nSeed, seed and seed number of A matrix is 2n×n。
Calculating 2 by using the state matrix A, the harmonic power of each sample electric appliance learned in the learning process and the power of the 2k-1(k is 1,2, 3.) subharmonic on the bus in the actual situation obtained in the measuring processnSeed-combined power deviation vector Δ P2k-1、ΔQ2k-1Wherein:
ΔP2k-1representing active work at the 2k-1 th harmonicRate deviation vector, Δ P2k-1(i)(i＝1,2…2n) Representing a 2k-1 harmonic active power deviation value corresponding to the ith combination; pn2k-1Representing the 2k-1 th harmonic active power of the nth sample electrical appliance learned in the learning process; p2k-1 meaThe active power value of the 2k-1 th harmonic on the bus obtained in the measurement process for the single electric appliance. Delta Q2k-1The calculation process is similar to it, as follows:
ΔQ2k-1representing the reactive power deviation vector, Δ Q, of the 2k-1 th harmonic2k-1(i)(i＝1,2…2n) Representing a 2k-1 harmonic reactive power deviation value corresponding to the ith combination; qn2k-1Representing the 2k-1 harmonic reactive power of the nth sample appliance learned in the learning process; q2k-1 meaThe reactive power value of the 2k-1 harmonic on the bus obtained in the measurement process.
Calculating relative deviation vector delta P of each scheme from actual situation2k-1 rel、ΔQ2k-1 relThe specific calculation method is as follows:
Establishing a similarity function and calculating a similarity vector S, wherein the ith element SiThe percent similarity of the ith protocol to the true case is characterized.
Similarity of ith scheme SiThe calculation method comprises the following steps:
Si＝1-(p1×ΔP1i rel+p3×ΔP3i rel+…p2k-1×ΔP2k-1i rel+q1×ΔQ1i rel+q3×ΔQ3i rel+…q2k-1×ΔQ2k-1i rel)
wherein p is1,p3…p2k-1And q is1,q3…q2k-1Is a weight coefficient, and p1+p3+…+p2k-1＝0.5，q1+q3+…+q2k-10.5. When the power characteristics of the low-order harmonic waves of the electric appliance to be identified are similar, the high-order harmonic power characteristics with smaller values can be prevented from being submerged by adjusting the weight coefficient, and the power characteristics can be equal by default under normal conditions. 2 can be calculated by the above formulanThe similarity value of each scheme to the real situation, the closer the result is to 1, the closer the scheme is to the real situation, and if the condition that a certain element appears to be less than 0, the scheme is processed as being equal to 0.
The fourth step: selecting the row vector A of the working state of the electric appliance in the array A corresponding to the element closest to 1 in the vector Si(the ith combination in the A matrix is the current optimal recognition result), namely the current recognition result.
The fifth step: judging whether the result similarity is greater than or equal to 0.9, if so, outputting the identification result and the similarity; if not, judging whether the arithmetic operation times N are more than 10 times, and if so, outputting the current optimal recognition result and similarity; if not, returning to the second step to carry out the operation again.
The above algorithm was verified by simulation using MATLAB software:
in the simulation process, 4 electric appliances are provided, and the seventh harmonic is collected for each electric appliance, so n is 4, and k is 4. The collected power conditions of the electric appliances in normal operation are shown in table 1, and the distribution diagram of the harmonic power condition of each electric appliance is shown in fig. 2 to 5.
TABLE 1 learning process harmonic power of each electrical appliance
After the learning process is finished, the measurement and identification process can be started, a plurality of electric appliances can be operated at will, in this example, the electric appliances No. 1 and No. 2 are operated, and the power conditions of each harmonic on the bus under the current state are obtained through measurement as shown in Table 2:
TABLE 2 measurement of Process Power Condition
The data obtained from the above two processes are substituted into an algorithm, and similarity data corresponding to each scheme obtained by MATLAB simulation is shown in table 3.
Table 3 table of similarity data corresponding to all schemes
According to the result, the algorithm calculates the row vector (1100) of the working state of the electrical appliance with the maximum similarity, the similarity reaches 0.9581, which shows that the electrical appliance No. 1 and 2 works, the non-working condition of the electrical appliance No. 3 and 4 is closest to the actual condition, the actual condition is that the electrical appliance No. 1 and 2 works, the verification result is completely consistent with the actual condition, and the validity of the algorithm is directly verified.
The invention provides a non-invasive electrical appliance identification method based on a harmonic similarity algorithm, which comprises the steps of firstly carrying out a learning process on an electrical appliance to be identified, and measuring the active power and the reactive power of fundamental wave, the reactive power and the active power and the reactive power of some higher harmonics of each sample electrical appliance under standard conditions in the learning process; acquiring time domain data of voltage and current of unknown electrical appliances on a bus; converting the acquired time domain data of the voltage and the current into frequency domain data through Fast Fourier Transform (FFT); calculating active and reactive data of each harmonic in the current power utilization state according to current and voltage harmonics of different times in a frequency domain; and giving identification similarity data to the identification result through the designed similarity function. The specific identification process is as follows: (i) establishing a similarity function; (ii) substituting the power values of the fundamental wave and each harmonic wave learned in the learning process and the power values of the fundamental wave and each harmonic wave of the unknown electrical appliance in the measuring process into the similarity function; (iii) and solving the similarity function to obtain the power utilization condition of the current monitoring target. And matching the obtained active power data and the reactive power data of each subharmonic of the electric appliance with the data obtained in the previous learning process, and identifying the type of the sample electric appliance. The invention provides a non-invasive electrical appliance identification method based on a harmonic similarity algorithm, which can be identified by adopting a similarity index under the condition that a plurality of electrical appliances can work simultaneously without entering the home of a user.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (5)
1. A non-invasive electrical appliance identification method based on a harmonic similarity algorithm is characterized by comprising the following steps:
step 1: selecting sample electric appliances, firstly carrying out a learning process on the electric appliances to be identified, and measuring the active power, the reactive power of the fundamental wave and the active power and the reactive power of each harmonic of each sample electric appliance under standard conditions in the learning process;
step 2: initializing the operation times N of the electrical appliance identification algorithm to enable N to be 0, and starting the operation for the Nth to be N +1 times;
and step 3: acquiring time domain data of voltage and current of unknown electrical appliances on a bus;
and 4, step 4: converting the acquired time domain data of the voltage and the current into frequency domain data through Fast Fourier Transform (FFT);
and 5: according to the current and voltage harmonics of different times in the frequency domain data, calculating to obtain the total fundamental wave, the active power and the reactive power of each harmonic in the current power utilization state;
step 6: giving identification similarity data to the identification result through the constructed similarity function;
and 7: judging whether the result similarity is greater than or equal to 0.9, if so, outputting the identification result and the similarity; if not, judging whether the arithmetic operation times N are more than 10 times, and if so, outputting the current optimal recognition result and similarity; if not, the operation returns to the step 2 to carry out the operation again.
2. The harmonic similarity algorithm based non-invasive appliance identification method according to claim 1, wherein the step 1: selecting sample electric appliances, firstly carrying out a learning process on the electric appliances to be identified, and measuring the fundamental wave active power, the fundamental wave reactive power and the active power and the reactive power of each harmonic wave of each sample electric appliance under a standard condition in the learning process, wherein the highest times of each harmonic wave is seven times.
3. The non-invasive electrical apparatus identification method based on the harmonic resonance wave similarity algorithm according to claim 1, wherein the step 6: giving identification similarity data to the identification result through the constructed similarity function, and specifically comprising the following steps:
constructing a similarity function;
substituting the power values of the fundamental wave and each harmonic wave learned in the learning process and the power values of the fundamental wave and each harmonic wave of the unknown electrical appliance in the measuring process into the similarity function;
and solving the similarity function to obtain the power utilization condition of the current monitoring target.
4. The harmonic similarity algorithm-based non-invasive electrical appliance identification method according to claim 3, wherein the constructing of the similarity function specifically comprises:
establishing a state matrix A;
calculating to obtain 2 according to the state matrix A, the harmonic power of each sample electric appliance learned in the learning process and the power of the 2k-1(k is 1,2, 3.) subharmonic on the bus in the actual situation obtained in the measuring processnCombined active power deviation vector delta P2k-1Active power deviation vector delta Q2k-1；
According to the active power deviation vector delta P2k-1Active power deviation vector delta Q2k-1Respectively calculating the relative deviation vector delta P of the active power of each scheme and the actual situation2k-1 relRelative deviation vector delta Q of reactive power2k-1 rel；
Relative deviation vector delta P according to active power2k-1 relRelative deviation vector delta Q of reactive power2k-1 relA similarity vector S is calculated.
5. The harmonic similarity algorithm-based non-invasive electrical appliance identification method according to claim 3, wherein solving the similarity function can obtain the electricity utilization condition of the current monitoring target, and specifically comprises:
and selecting the electric appliance working state row vector in the state matrix A corresponding to the element closest to 1 in the similarity vector S as an identification result.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112540549A (en) * | 2020-12-02 | 2021-03-23 | 吉林建筑大学 | Student dormitory electricity utilization monitoring method and device |
| CN112711613A (en) * | 2020-11-27 | 2021-04-27 | 浙江海普发科技有限公司 | Electric appliance preference analysis method based on intelligent judgment of power utilization behavior |
| CN116861316A (en) * | 2023-09-04 | 2023-10-10 | 国网浙江省电力有限公司余姚市供电公司 | Electrical appliance monitoring method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112711613A (en) * | 2020-11-27 | 2021-04-27 | 浙江海普发科技有限公司 | Electric appliance preference analysis method based on intelligent judgment of power utilization behavior |
| CN112711613B (en) * | 2020-11-27 | 2022-07-29 | 浙江海普发科技有限公司 | Electric appliance preference analysis method based on intelligent judgment of power utilization behavior |
| CN112540549A (en) * | 2020-12-02 | 2021-03-23 | 吉林建筑大学 | Student dormitory electricity utilization monitoring method and device |
| CN112540549B (en) * | 2020-12-02 | 2021-11-26 | 吉林建筑大学 | Student dormitory electricity utilization monitoring method and device |
| CN116861316A (en) * | 2023-09-04 | 2023-10-10 | 国网浙江省电力有限公司余姚市供电公司 | Electrical appliance monitoring method and device |
| CN116861316B (en) * | 2023-09-04 | 2023-12-15 | 国网浙江省电力有限公司余姚市供电公司 | Electrical appliance monitoring method and device |
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