CN113985266A - Centralized load identification method for multiple intelligent electric meters - Google Patents
Centralized load identification method for multiple intelligent electric meters Download PDFInfo
- Publication number
- CN113985266A CN113985266A CN202111269172.5A CN202111269172A CN113985266A CN 113985266 A CN113985266 A CN 113985266A CN 202111269172 A CN202111269172 A CN 202111269172A CN 113985266 A CN113985266 A CN 113985266A
- Authority
- CN
- China
- Prior art keywords
- event
- intelligent electric
- active
- events
- power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000005070 sampling Methods 0.000 claims abstract description 21
- 230000008859 change Effects 0.000 claims abstract description 11
- 230000008569 process Effects 0.000 claims abstract description 8
- 230000003247 decreasing effect Effects 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 5
- 230000009467 reduction Effects 0.000 claims description 5
- 230000001052 transient effect Effects 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/327—Testing of circuit interrupters, switches or circuit-breakers
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention discloses a centralized load identification method for multiple intelligent electric meters, wherein n intelligent electric meters are connected between a live wire and a zero line in a hanging mode, the intelligent electric meters collect grid voltage and total load current at the sampling frequency of 1.6kHz and detect power change events, once a non-invasive load identification computer uploads a data instruction, the stored events are sent through a CAN bus and deleted, and a top-level high-performance computer processes the power change events in a centralized mode to realize load identification of each intelligent electric meter. The invention specifically provides a specific step of detecting a power change event after the intelligent electric meter collects a complete cycle power grid voltage and current. In addition, the invention provides a specific method for processing the uploading event of the intelligent electric meter by the top-level high-performance computer. The invention reduces the load identification algorithm computation amount, so that one high-performance computer can finish the load identification of a plurality of intelligent electric meters.
Description
Technical Field
The invention relates to the technical field of electric power Internet of things edge calculation, in particular to a centralized load identification method for multiple intelligent electric meters.
Background
The load identification technology is a key content of future intelligent power grid demand side management, and has wide application prospects in the aspects of optimizing power grid supply-demand relation, promoting energy conservation and emission reduction and the like. The non-intrusive load identification is widely concerned due to the advantages of simple operation and maintenance, low investment cost, strong information security and the like.
The load identification algorithm can be divided into a cloud processing algorithm separated from the intelligent electric meter and a local processing algorithm of the intelligent electric meter according to the position of the completion link. The load identification cloud processing algorithm runs on an upper-layer high-performance computer, and required data are collected by the intelligent electric meter and uploaded through a network. The data is generally collected at a relatively low frequency due to the limitations of the network transmission rate. Under the low-frequency sampling, because the active power, the current transient characteristics and the like of the load are lost, the identification accuracy of the method is difficult to guarantee under the conditions that the household load appliances are various in types and the working states are complex and diversified. The local processing algorithm of the intelligent electric meter is based on high sampling frequency, and can accurately acquire the characteristics of the load electric appliance in the transient process as the supplement of the active and reactive characteristics of the load steady state. The intelligent calculation-based local identification method for the intelligent electric meter needs a lot of time for training and learning sample data, and once the load type changes, the training and learning needs to be carried out again. In addition, the advanced algorithm with complicated iterative computation needs to be completed by a single high-performance computer, but a high-price computer is arranged in each smart meter, so that the advanced algorithm is obviously not preferable from the cost consideration.
Disclosure of Invention
The invention aims to solve the technical problem that the method overcomes the huge calculation amount caused by the high complexity of a load identification algorithm, so that one high-performance computer can finish the load identification of a plurality of intelligent electric meters, and the system cost is greatly reduced.
In order to solve the technical problem, the solution of the invention is as follows: providing a centralized load identification method for multiple intelligent electric meters, wherein a top-level high-performance computer processes uploading events of the intelligent electric meters to finish load identification; and each intelligent electric meter collects the voltage of the power grid and the total current of the load to finish the detection of the power change event.
The invention further provides that the detection of the power change event of the intelligent ammeter comprises the following steps: 1) selecting and storing a group of voltage and current data sets in a complete power frequency period by taking the zero-crossing moment of the power grid voltage as the starting point from the power grid voltage and current sampling values of every three continuous cycles; 2) performing FFT on the voltage and the current in each data set to obtain active power; 3) comparing the active power of the current data set with the active power of the previous data set, and judging whether a new event occurs; 4) if the active power increase event occurs, storing a power grid voltage and incremental current data set in the active power increase event, wherein the power grid voltage and incremental current data set comprises an initial time, a steady-state active power increment, a plurality of power frequency period active power increment time sequences and a power frequency period in a steady-state stage; 5) and if the active power reduction event occurs, storing a power grid voltage and incremental current data set in one power frequency period including the initial time, the steady-state active increment and the steady-state stage in the active power reduction event.
The invention also provides a processing process of the top-level high-performance computer aiming at the uploading event of each intelligent electric meter, all the events are firstly arranged according to the time sequence, and only the storage operation is carried out when the newly generated event is an active power increasing event. And after the event is stored when the newly generated event is an active decreasing event, respectively extracting 7, 6, 5, 4, 3, 2 and 1 events from the events before the new event and the new event combination, and judging whether the event group belongs to a certain load electrical appliance according to the established feature library. And once the event group is judged to meet the condition of belonging to a certain load electrical appliance, storing the identification result and deleting the event group. The conditions that an event group belongs to a certain load electrical appliance are specifically as follows: 1) the active increment of the initial event is positive, and the active increment of the termination event is negative; 2) the sum of active increments of all events is less than a set threshold; 3) no more than 3 continuous power increasing events or more than 3 continuous power decreasing events exist in the event group; 4) the active increment time sequence in all active increment events in the event group and the Euclidean distance of a certain characteristic template corresponding to the load electrical appliance in the characteristic library are within a set threshold value; 5) aiming at voltage and current sampling data of a steady-state power frequency period in an initial event, load electrical parameters calculated by utilizing FFT and generalized S transformation are compared with corresponding characteristic values in an electrical appliance characteristic library, and errors are required to be within a set threshold value; 6) and (3) sequentially superposing the steady-state power frequency period incremental current in the events except the last event from the start of the first event in the group, forming steady-state power frequency period sampling data by superposing each group of steady-state power frequency period incremental currents, calculating relevant load parameters aiming at the steady-state data set, and matching the load electrical parameters calculated after each superposition with corresponding characteristic values in an electrical appliance characteristic library.
The centralized load identification method for the multiple intelligent electric meters has the advantages that the method is suitable for load electric appliance identification under complex working conditions of mixed operation of large and small power electric appliances, multi-state electric appliance operation, multi-electric appliance superposition operation and the like; and the load identification algorithm has small operand, and one high-performance computer can finish the load identification of a plurality of intelligent electric meters.
Description of the drawings:
fig. 1 is a structure diagram of a centralized load identification system of multiple intelligent electric meters.
Detailed Description
The method is realized on the basis that load identification is completed by processing an uploading event of each intelligent electric meter based on a top-level high-performance computer; and N intelligent electric meters are connected between the live line L and the zero line N in a hanging mode, once the intelligent electric meters receive a data uploading instruction from a top-layer high-performance computer, the stored events are sent through a CAN bus and deleted, and the intelligent electric meters collect the grid voltage and the load total current at the sampling frequency of 1.6kHz and finish the detection of the power change event.
The method for detecting the power change event of the intelligent electric meter specifically comprises the following steps: 1) selecting voltage and current sampling data in a current power frequency period and voltage and current sampling data in the last two power frequency periods, wherein the voltage and the current are respectively marked as U [ m ] (1 < m > 96) and I [ m ] (1 < m > 96), and when the zero crossing time of the power grid voltage is used as a reference, and U [ l ] > (0) and U [ l-1] < ═ 0 are searched, obtaining a kth power frequency period data [ k ] { (U [ l ], I [ l ]); (U +1, I + 1) … (U +31, I + 31); 2) performing FFT on the voltage and current in the data set data [ k ] to obtain active power Pk; 3) comparing P [ k ] with P [ k-2], if | P [ k ] -P [ k-2] | is less than the threshold value W, no event occurs, otherwise, an event occurs; 4) if (P [ k ] -P [ k-2]) > (W), an active increment event occurs, wherein the event comprises occurrence time, steady-state active increment, transient G power frequency period active increment time sequence, and 32-point power grid voltage and 32-point increment current sampling data in one power frequency period in a steady-state stage; 5) and if (P [ k ] -P [ k-2]) < -W, an active power reduction event occurs, wherein the event comprises the occurrence time, the steady-state active increment and 32-point incremental current sampling data of the grid voltage in a power frequency period at the steady-state stage.
The processing process of the top-level high-performance computer for uploading events of each intelligent electric meter specifically includes that all events are arranged according to time sequence, event [1] … event [ i ], only storage operation is performed when event [ i ] is an active increment event, 7, 6, 5, 4, 3, 2, 1 event and event [ i ] combination are respectively extracted from i-1 events before event [ i ] after the event [ i ] is stored when the event [ i ] is an active decrement event, whether an event group belongs to a certain load electric appliance or not is judged according to an established feature library, once the event group is judged to meet the condition of belonging to the certain load electric appliance, an identification result is stored and the event group is deleted, and the condition that the event group belongs to the certain load electric appliance specifically includes: 1) the active increment of the initial event is positive, and the active increment of the termination event is negative; 2) the sum of active increments of all events is less than a threshold value W; 3) no more than 3 continuous power increasing events or more than 3 continuous power decreasing events exist in the event group; 4) the active increment time sequence in all active increment events in the event group and the Euclidean distance of a certain characteristic template corresponding to the load electrical appliance in the characteristic library are within a set threshold value; 5) aiming at voltage and current sampling data of a steady-state power frequency period in an initial event, calculating active, reactive, 100Hz harmonic components and 150Hz harmonic components by using FFT, calculating inter-harmonic components of 25Hz harmonic components, 75Hz harmonic components and 125Hz harmonic components by using generalized S transformation, comparing the calculated load electrical parameters with corresponding characteristic values in an electrical appliance characteristic library, and setting errors within a set threshold value; 6) and (3) sequentially superposing the steady-state power frequency period incremental current in the events except the last event from the start of the first event in the group, forming a steady-state power frequency period sampling data by superposing each group of steady-state power frequency period incremental currents, calculating relevant load electrical parameters according to the steady-state data set, and matching the load electrical parameters calculated after each superposition with corresponding characteristic values in an electrical appliance characteristic library.
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a structural diagram of a centralized load identification system for multiple intelligent electric meters, wherein N intelligent electric meters are connected between a live line L and a zero line N in a hanging mode, the intelligent electric meters collect grid voltage and total load current at a sampling frequency of 1.6kHz and complete power change event detection, once a non-invasive load identification computer uploading data instruction is received, stored events are sent through a CAN bus and deleted, and a top-level high-performance computer processes power change events in a centralized mode to realize load identification of each intelligent electric meter.
Claims (2)
1. The centralized load identification method for the multiple intelligent electric meters is characterized in that a top-layer high-performance computer finishes load identification aiming at the processing of an uploading event of each intelligent electric meter, N intelligent electric meters are connected between a live line L and a zero line N in a hanging mode, once the intelligent electric meters receive a data uploading instruction to the top-layer high-performance computer, the stored events are sent through a CAN bus and deleted, the intelligent electric meters acquire grid voltage and load total current at a sampling frequency of 1.6kHz and finish power change event detection, and the process of detecting the primary power change event of the intelligent electric meters specifically comprises the following steps: 1) selecting voltage and current sampling data in a current power frequency period and voltage and current sampling data in the last two power frequency periods, wherein the voltage and the current are respectively marked as U [ m ] (1 < m > 96) and I [ m ] (1 < m > 96), and when the zero crossing time of the power grid voltage is used as a reference, and U [ l ] > (0) and U [ l-1] < ═ 0 are searched, obtaining a kth power frequency period data [ k ] { (U [ l ], I [ l ]); (U +1, I + 1) … (U +31, I + 31); 2) performing FFT on the voltage and current in the data set data [ k ] to obtain active power Pk; 3) comparing P [ k ] with P [ k-2], if | P [ k ] -P [ k-2] | is less than the threshold value W, no event occurs, otherwise, an event occurs; 4) if (P [ k ] -P [ k-2]) > (W), an active increment event occurs, wherein the event comprises occurrence time, steady-state active increment, transient G power frequency period active increment time sequence, and 32-point power grid voltage and 32-point increment current sampling data in one power frequency period in a steady-state stage; 5) and if (P [ k ] -P [ k-2]) < -W, an active power reduction event occurs, wherein the event comprises the occurrence time, the steady-state active increment and 32-point incremental current sampling data of the grid voltage in a power frequency period at the steady-state stage.
2. The centralized load identification method for multiple intelligent electric meters based on claim 1, the processing process of the top-level high-performance computer for uploading the events of each intelligent electric meter is specifically that all events are arranged according to the time sequence, the event [1] … event [ i ] is arranged, only the storage operation is carried out when the event [ i ] is an active increasing event, 7, 6, 5, 4, 3, 2, 1 event and the event [ i ] are respectively extracted from i-1 events before the event [ i ] after the event [ i ] is stored when the event [ i ] is an active decreasing event, judging whether the event group belongs to a certain load electrical appliance according to the established feature library, storing the identification result and deleting the event group once the event group is judged to meet the condition of belonging to the certain load electrical appliance, wherein the condition that the event group belongs to the certain load electrical appliance is specifically as follows: 1) the active increment of the initial event is positive, and the active increment of the termination event is negative; 2) the sum of active increments of all events is less than a threshold value W; 3) no more than 3 continuous power increasing events or more than 3 continuous power decreasing events exist in the event group; 4) the active increment time sequence in all active increment events in the event group and the Euclidean distance of a certain characteristic template corresponding to the load electrical appliance in the characteristic library are within a set threshold value; 5) aiming at voltage and current sampling data of a steady-state power frequency period in an initial event, calculating active, reactive, 100Hz harmonic components and 150Hz harmonic components by using FFT, calculating inter-harmonic components of 25Hz harmonic components, 75Hz harmonic components and 125Hz harmonic components by using generalized S transformation, comparing the calculated load electrical parameters with corresponding characteristic values in an electrical appliance characteristic library, and setting errors within a set threshold value; 6) and (3) sequentially superposing the steady-state power frequency period incremental current in the events except the last event from the start of the first event in the group, forming a steady-state power frequency period sampling data by superposing each group of steady-state power frequency period incremental currents, calculating relevant load electrical parameters according to the steady-state data set, and matching the load electrical parameters calculated after each superposition with corresponding characteristic values in an electrical appliance characteristic library.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111269172.5A CN113985266B (en) | 2021-10-28 | 2021-10-28 | Centralized load identification method for multiple intelligent electric meters |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111269172.5A CN113985266B (en) | 2021-10-28 | 2021-10-28 | Centralized load identification method for multiple intelligent electric meters |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113985266A true CN113985266A (en) | 2022-01-28 |
| CN113985266B CN113985266B (en) | 2024-02-09 |
Family
ID=79744110
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111269172.5A Active CN113985266B (en) | 2021-10-28 | 2021-10-28 | Centralized load identification method for multiple intelligent electric meters |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113985266B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030006780A1 (en) * | 2000-01-26 | 2003-01-09 | Socrat | Method and device for testing a telecommunication cable |
| CN101710153A (en) * | 2009-12-12 | 2010-05-19 | 安徽鑫龙电器股份有限公司 | Embedded electric energy meter with harmonic measurement function and measurement method thereof |
| CN101957439A (en) * | 2010-02-10 | 2011-01-26 | 江西省电力科学研究院 | Sampling and calculating separated type electric energy meter calibrating method and electric energy meter site parameter recorder |
| US20120123709A1 (en) * | 2011-11-11 | 2012-05-17 | San Diego Gas & Electric Company, c/o Sempra Energy | Method for detection of plug-in electric vehicle charging via interrogation of smart meter data |
| WO2014075581A1 (en) * | 2012-11-16 | 2014-05-22 | 天津大学 | Current-mode matching method for non-invasive power load monitoring and decomposition |
| CN104459307A (en) * | 2013-09-12 | 2015-03-25 | 郑州朗鑫智能电子科技有限公司 | Single-phase smart electric meter with wireless meter reading function |
| CN106483370A (en) * | 2016-10-21 | 2017-03-08 | 威胜集团有限公司 | Non-intrusion type household loads real-time identification method based on multi-feature fusion and device |
| CN113361831A (en) * | 2021-08-10 | 2021-09-07 | 国网江苏省电力有限公司营销服务中心 | Non-invasive load identification electric quantity decomposition method and system based on priority distribution |
-
2021
- 2021-10-28 CN CN202111269172.5A patent/CN113985266B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030006780A1 (en) * | 2000-01-26 | 2003-01-09 | Socrat | Method and device for testing a telecommunication cable |
| CN101710153A (en) * | 2009-12-12 | 2010-05-19 | 安徽鑫龙电器股份有限公司 | Embedded electric energy meter with harmonic measurement function and measurement method thereof |
| CN101957439A (en) * | 2010-02-10 | 2011-01-26 | 江西省电力科学研究院 | Sampling and calculating separated type electric energy meter calibrating method and electric energy meter site parameter recorder |
| US20120123709A1 (en) * | 2011-11-11 | 2012-05-17 | San Diego Gas & Electric Company, c/o Sempra Energy | Method for detection of plug-in electric vehicle charging via interrogation of smart meter data |
| WO2014075581A1 (en) * | 2012-11-16 | 2014-05-22 | 天津大学 | Current-mode matching method for non-invasive power load monitoring and decomposition |
| CN104459307A (en) * | 2013-09-12 | 2015-03-25 | 郑州朗鑫智能电子科技有限公司 | Single-phase smart electric meter with wireless meter reading function |
| CN106483370A (en) * | 2016-10-21 | 2017-03-08 | 威胜集团有限公司 | Non-intrusion type household loads real-time identification method based on multi-feature fusion and device |
| CN113361831A (en) * | 2021-08-10 | 2021-09-07 | 国网江苏省电力有限公司营销服务中心 | Non-invasive load identification electric quantity decomposition method and system based on priority distribution |
Non-Patent Citations (3)
| Title |
|---|
| ZHAO FUPING: "An electrical energy meter for electric vehicle charging machines based on FFT and wavelet analysis", 2014 INTERNATIONAL SYMPOSIUM ON FUNDAMENTALS OF ELECTRICAL ENGINEERING * |
| 张红梅;孙洁;许仪勋;李旺;张敬周;: "基于暂态特征聚类的家用负荷识别", 电子技术与软件工程, no. 06 * |
| 殷波;张帅;: "家用非侵入式电力负荷监测与识别算法研究", 中国海洋大学学报(自然科学版), no. 06 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113985266B (en) | 2024-02-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110350528B (en) | Low-voltage distribution area topology automatic identification method | |
| CN106093565B (en) | A kind of electricity subentry measurement method and device based on steady state characteristic Waveform Matching | |
| CN111242391B (en) | Machine learning model training method and system for power load identification | |
| CN113011481B (en) | Electric energy meter function abnormality assessment method and system based on decision tree algorithm | |
| CN111209976B (en) | Non-invasive load monitoring method and system | |
| CN109840691B (en) | Non-invasive polynomial electric quantity estimation method based on deep neural network | |
| CN113030564A (en) | Load identification method based on double-core intelligent electric meter system | |
| CN112564110B (en) | Transformer area low-voltage treatment method and system | |
| CN111654392A (en) | Low-voltage distribution network topology identification method and system based on mutual information | |
| CN103018611B (en) | Non-invasive load monitoring method and system based on current decomposition | |
| CN115758188A (en) | Non-invasive load identification method, device, equipment and medium | |
| CN110968703B (en) | Method and system for constructing abnormal metering point knowledge base based on LSTM end-to-end extraction algorithm | |
| CN112615428A (en) | Line loss analysis and treatment system and method | |
| CN109495355A (en) | Intelligent household energy router | |
| CN115207887A (en) | Short-circuit fault direction judging method and system for wind power plant outgoing line | |
| CN112182499B (en) | Low-voltage distribution network topological structure identification method based on time sequence electric quantity data | |
| CN101707371A (en) | Method for identifying equivalent parameters of power system load model under small disturbance condition | |
| CN109541346A (en) | A kind of non-intrusion type electrical load under steady state condition identifies method for improving | |
| CN113985266A (en) | Centralized load identification method for multiple intelligent electric meters | |
| WO2022111741A1 (en) | System and method for load identification in collaboration with cloud end | |
| CN112730984B (en) | Low-voltage distribution network phase identification method based on intelligent electric meter | |
| CN111239480B (en) | Dyn11 low-voltage distribution network theoretical line loss calculation method and system | |
| CN113552527A (en) | Multi-meter set line loss meter-setting device and line loss meter-setting method | |
| Shi et al. | Load identification method of household smart meter based on decision tree algorithm | |
| CN112345824B (en) | Variable frequency air conditioner non-invasive identification method based on piecewise linear approximation algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |