CN212459899U - Electric energy acquisition terminal based on artificial intelligence machine learning - Google Patents
Electric energy acquisition terminal based on artificial intelligence machine learning Download PDFInfo
- Publication number
- CN212459899U CN212459899U CN202021067004.9U CN202021067004U CN212459899U CN 212459899 U CN212459899 U CN 212459899U CN 202021067004 U CN202021067004 U CN 202021067004U CN 212459899 U CN212459899 U CN 212459899U
- Authority
- CN
- China
- Prior art keywords
- electric energy
- module
- machine learning
- artificial intelligence
- main control
- 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.)
- Active
Links
- 238000010801 machine learning Methods 0.000 title claims abstract description 24
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 23
- 238000004891 communication Methods 0.000 claims abstract description 34
- 238000012545 processing Methods 0.000 claims abstract description 21
- 238000002955 isolation Methods 0.000 claims abstract description 19
- 230000002457 bidirectional effect Effects 0.000 claims abstract description 6
- 238000005516 engineering process Methods 0.000 claims abstract description 6
- 239000003990 capacitor Substances 0.000 claims description 7
- 239000004065 semiconductor Substances 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 description 12
- 238000005265 energy consumption Methods 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 5
- 239000013078 crystal Substances 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 235000015429 Mirabilis expansa Nutrition 0.000 description 2
- 244000294411 Mirabilis expansa Species 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 235000013536 miso Nutrition 0.000 description 2
- 230000010355 oscillation Effects 0.000 description 2
- 238000009966 trimming Methods 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 102100024735 Resistin Human genes 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000000034 method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 101150091950 retn gene Proteins 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
Images
Landscapes
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
An electric energy acquisition terminal based on artificial intelligence machine learning comprises a main control CPU, wherein the main control CPU is provided with five bidirectional ports and an input port. The bidirectional port is respectively connected with the communication electrical isolation module, the high-speed external SPI communication module, the USB serial communication module, the JTAG connector module and the expansion interface; one input port is connected to the power management module and used for receiving a stable direct current power supply, and the main control CPU realizes the stable operation of the power supply of the whole system through the coordinated management of the power manager. The master control CPU is used for receiving data of the RN7302 electric energy acquisition chip, and processing and analyzing the data processed by the RN7302 electric energy acquisition chip by using an SPI communication technology after passing through the bus transceiver module and the communication electrical isolation module. The RN7302 electric energy acquisition chip acquires various electric parameters through a three-phase current transformer and a voltage processing current and current processing circuit. And finally, the main control CPU obtains corresponding measures and energy utilization schemes.
Description
Technical Field
The utility model relates to a power supply distribution network real-time supervision technical field, an electric energy collection terminal based on artificial intelligence machine learning promptly. In particular to a system for analyzing electricity consumption and electric energy of low-voltage users.
Background
In the prior art, the domestic collection and monitoring of the power grid are developing towards intellectualization, high-precision and man-machine interaction. At present, the application of artificial intelligence technology in various fields is more extensive, and some machine learning algorithms are deep into the field of power grids. However, the method is generally applied to the aspects of intelligent monitoring of power grid energy, intelligent scheduling of power grid, fault prediction of power grid and the like, is still in a starting stage when applied to the aspect of acquisition technology of the power grid, and has no corresponding equipment and device. Meanwhile, the acquisition terminal of the power grid is generally a mutual inductor for monitoring parameters such as voltage and current, and extremely individual equipment can monitor parameters such as power quality and the like, and the acquisition terminal is not combined with an artificial intelligence technology and a machine learning algorithm. The analysis of the energy consumption of the electric energy user is also an indispensable part based on the smart power grid, and the on-line monitoring equipment for the power grid, which can realize the measurement and collection of various electric parameters and electric energy quality parameters and can realize intelligent energy consumption analysis, is urgently needed to be developed.
SUMMERY OF THE UTILITY MODEL
The utility model discloses the main technical problem who solves provides an electric energy collection terminal based on artificial intelligence machine learning, and it can realize the measurement and the collection of various electrical parameters and electric energy quality parameter, also can realize the electric wire netting on-line monitoring equipment of intelligent energy consumption analysis.
The utility model provides an electric energy collection terminal based on artificial intelligence machine learning which characterized in that: the system comprises 13 modules, namely a master control CPU, an RN7302 electric energy acquisition chip, high-speed external SPI communication, USB serial communication, a JTAG connector, an expansion interface, a power management module, an isolation power supply, a communication electrical isolation module, a bus transceiver, a three-phase current transformer, a three-phase voltage processing circuit and a three-phase current processing circuit; the main control CPU is provided with five bidirectional ports and an input port; the bidirectional port is respectively connected with the communication electrical isolation module, the high-speed external SPI communication module, the USB serial communication module, the JTAG connector module and the expansion interface; one input port is connected to the power management module and used for receiving a stable direct-current power supply, and the main control CPU realizes the stable operation of the power supply of the whole system through the coordinated management of the power manager; the master control CPU is used for receiving data of the RN7302 electric energy acquisition chip, and processing and analyzing the data processed by the RN7302 electric energy acquisition chip by using an SPI communication technology after passing through the bus transceiver module and the communication electrical isolation module; the RN7302 electric energy acquisition chip acquires various electric parameters such as voltage, current, frequency, active power, reactive power, phase, full-wave acquisition, electric energy quality parameters and the like through a three-phase current transformer and a voltage processing current and current processing circuit; and finally, the main control CPU utilizes artificial intelligence and a machine learning algorithm to achieve the purposes of intelligently analyzing the power utilization condition, analyzing the type of the electric appliance, analyzing the quality of the electric energy and obtaining corresponding measures and energy utilization schemes.
The master control single chip microcomputer comprises an ARM core-M3 chip, and a minimum system circuit, a clock circuit and a reset circuit of the master control single chip microcomputer are respectively connected with the ARM core-M3 chip.
The main control single chip microcomputer comprises an ARM core-M3 chip and an STM32F103RET6 chip of ST company.
The RN7302 electric energy acquisition chip uses an Arrada photoelectric company RN7302 three-phase electric parameter acquisition chip.
The USB serial communication uses an application circuit formed by a CH340-G USB control chip.
The power management module is an application circuit of an AMS1117-3.3 LDO power management chip manufactured by TI (Texas instruments).
The isolating power supply uses a BS0505-1W isolating power supply module.
The communication electric isolation module uses (AD) ADuM1401 chip of Asia semiconductor company in America.
The three-phase current transformer is a DC/T1005 current transformer of an upward electronic company.
The voltage processing circuit is directly connected with a power grid through a resistor capacitor, and a voltage transformer is not used.
The artificial intelligence and machine learning algorithm is simple harmonic analysis, time domain analysis and improved genetic algorithm.
The utility model discloses can realize the on-line collection and the storage to electric parameter, electric energy quality parameter to can carry out the analysis with various wave forms, the parameter of discerning with the data use artificial intelligence machine learning algorithm that receive, reach low pressure single-phase user's kind and power with electrical apparatus, discern high pressure three-phase user's with the ability law and give corresponding improvement scheme. The acquisition terminal can be applied to the aspects of user classification, electric energy scheduling, reactive power distribution, high-quality service and the like of a power grid company, can also be applied to technical development and research work, and has the advantages of wide use effect and prospect, simple structure and relatively low manufacturing cost.
The utility model has the advantages that: the data acquisition precision is high, can carry out with energy analysis and with electrical apparatus discernment, and is accurate, and the flexibility is high, has the real-time. The defect of slow manual analysis is well overcome, and manpower is greatly saved.
Drawings
Fig. 1 is a schematic block circuit diagram of the present invention.
Detailed Description
Referring to fig. 1, the utility model discloses a master control CPU (the integration of data calculates, the analysis, machine learning algorithm calculates, receive and send data, provide with energy analysis report), RN7302 electric energy collection chip (gather three-phase voltage electric current, various electrical parameters and electric energy quality parameter), high-speed outside SPI communication (with the communications of external parts), USB serial communication (carry out the communication with the computer), the JTAG connector (carry out the program with the computer and burn the emulation), extend the interface, power management module (provide stable power), keep apart the power (carry out the electromagnetism isolation to the power, prevent that the high pressure from gushing into the low pressure side), communication electric isolation module (utilize electromagnetism isolation and opto-coupler to keep apart and carry out the electromagnetism isolation with communication signal), bus transceiver (improve signal area load capacity), three-phase current transformer (gather and exchange high-voltage side electric current), three-phase voltage processing circuit (gather high-voltage alternating voltage) The three-phase current processing circuit (filters and stabilizes the acquired waveform) consists of 13 modules.
The main control CPU receives data from various modules to perform artificial intelligence and machine learning algorithm, identifies the current connected electric equipment of the power grid by using simple harmonic analysis, time domain analysis and improved genetic algorithm, analyzes the corresponding energy consumption details, and finally can give a corresponding energy consumption report or an energy consumption habit improvement scheme.
The core circuit of the main control CPU consists of a reset circuit, an expanded I/O interface circuit and a clock circuit, wherein a BOOTO pin is grounded through a 10K resistor, an NRST pin is provided with a 104 capacitor ground and a 10K resistor which are connected to a 3V network, and the clock circuit is provided with two pins which are connected to an 8MHz crystal oscillator and connected with a 1M oscillation starting resistor and two 22p trimming capacitors in parallel.
The core circuit of the RN7302 electric energy acquisition chip consists of a reset circuit, an expanded I/O interface circuit and a clock circuit, wherein a pin RSTN of the clock circuit is provided with a 30p capacitor grounding and a 10K resistor connected to a 5V network, and pins XO and XI of the clock circuit are connected to an 8.192MHz crystal oscillator and are connected with a 10M oscillation starting resistor and two 15p trimming capacitors grounding in parallel.
The three-phase voltage acquisition circuit consists of a wiring terminal and a divider resistor, and is connected to pins 9 (VAP), 10, 11, 12, 13, 14, 15 and 16 (INN) of the RN7302 core after processing.
The three-phase current acquisition circuit consists of a current transformer and a processing circuit, and is connected to pins 1 (IAP), 2, 4, 5, 7 and 8 (ICN) of the RN7302 core after processing.
The USB serial communication circuit consists of a CH340-G chip, an interface circuit and a clock circuit, wherein the clock circuit is connected to two 22p fine tuning capacitors under a 12MHz crystal oscillator through pins 7 (XI 1) and 8 (XO 1) and grounded, the interface circuit is connected to a USB female port, and the USB serial communication circuit is connected to pins NRST, BOOT0, TXD0 and RXD0 of a main control CPU core after processing.
The communication isolation circuit consists of an ADuM1401 chip and a TLP521-4 optical coupler chip, wherein the low-voltage side is connected to pins MOSI, MISO, CLK, CS, INTN, CF1, CF2 and CF3 of a main control CPU core, and the high-voltage side is connected to pins MOSI, MISO, CLK, CS, INTN, CF1, CF2 and CF3 of RN 7302.
The utility model discloses the three-dimensional model picture utilizes the automatic software design of Altium Designer electronic design to form, and integrated circuit design arranges with the picture top formula and designs.
Claims (10)
1. The utility model provides an electric energy collection terminal based on artificial intelligence machine learning which characterized in that: the system comprises a master control CPU, an RN7302 electric energy acquisition chip, a high-speed external SPI communication, a USB serial communication, a JTAG connector, an expansion interface, a power management module, an isolation power supply, a communication electrical isolation module, a bus transceiver, a three-phase current transformer, a three-phase voltage processing circuit and a three-phase current processing circuit; the main control CPU is provided with five bidirectional ports and an input port; the bidirectional port is respectively connected with the communication electrical isolation module, the high-speed external SPI communication module, the USB serial communication module, the JTAG connector module and the expansion interface; one input port is connected to the power management module and used for receiving a stable direct-current power supply, and the main control CPU realizes the stable operation of the power supply of the whole system through the coordinated management of the power manager; the master control CPU is used for receiving data of the RN7302 electric energy acquisition chip, and processing and analyzing the data processed by the RN7302 electric energy acquisition chip by using an SPI communication technology after passing through the bus transceiver module and the communication electrical isolation module; the RN7302 electric energy acquisition chip acquires various electric parameters through a three-phase current transformer and a voltage processing current and current processing circuit; and finally, the main control CPU utilizes artificial intelligence and a machine learning algorithm to obtain corresponding countermeasures and energy utilization schemes.
2. The electric energy collection terminal based on artificial intelligence machine learning of claim 1, characterized in that: the main control CPU is a main control single chip microcomputer which comprises an ARM core-M3 chip, and a minimum system circuit, a clock circuit and a reset circuit of the main control single chip microcomputer are respectively connected with the ARM core-M3 chip of the main control single chip microcomputer.
3. The electric energy collection terminal based on artificial intelligence machine learning of claim 2, characterized in that: the main control single chip microcomputer comprises an ARM core-M3 chip and an STM32F103RET6 chip of ST company.
4. The electric energy collection terminal based on artificial intelligence machine learning of claim 1, characterized in that: the RN7302 electric energy acquisition chip uses an Arrada photoelectric company RN7302 three-phase electric parameter acquisition chip.
5. The electric energy collection terminal based on artificial intelligence machine learning of claim 1, characterized in that: the USB serial communication uses an application circuit formed by a CH340-G USB control chip.
6. The electric energy collection terminal based on artificial intelligence machine learning of claim 1, characterized in that: the power management module is an application circuit of an AMS1117-3.3 LDO power management chip produced by Texas instruments.
7. The electric energy collection terminal based on artificial intelligence machine learning of claim 1, characterized in that: the isolation power supply uses an application circuit of a BS0505-1W isolation power supply module.
8. The electric energy collection terminal based on artificial intelligence machine learning of claim 1, characterized in that: the communication electric isolation module uses an application circuit of ADuM1401 (American Sunnyya semiconductor corporation).
9. The electric energy collection terminal based on artificial intelligence machine learning of claim 1, characterized in that: the three-phase current transformer is a DC/T1005 current transformer of an upward electronic company.
10. The electric energy collection terminal based on artificial intelligence machine learning of claim 1, characterized in that: the voltage processing circuit is directly connected with a power grid through a resistor capacitor, and a voltage transformer is not used.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202021067004.9U CN212459899U (en) | 2020-06-11 | 2020-06-11 | Electric energy acquisition terminal based on artificial intelligence machine learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202021067004.9U CN212459899U (en) | 2020-06-11 | 2020-06-11 | Electric energy acquisition terminal based on artificial intelligence machine learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN212459899U true CN212459899U (en) | 2021-02-02 |
Family
ID=74492834
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202021067004.9U Active CN212459899U (en) | 2020-06-11 | 2020-06-11 | Electric energy acquisition terminal based on artificial intelligence machine learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN212459899U (en) |
-
2020
- 2020-06-11 CN CN202021067004.9U patent/CN212459899U/en active Active
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3057192A1 (en) | An energy internet and a hierarchical control system and a control method thereof | |
| CN103632307A (en) | Method for checking consistency between SCD and virtual loop table of intelligent substation | |
| CN103683999A (en) | Grid connected inverter and output filtering method thereof | |
| CN205246757U (en) | Electric energy meter signal acquisition metering modules | |
| CN115061025A (en) | Photovoltaic inverter high voltage ride through detection method and system | |
| CN212459899U (en) | Electric energy acquisition terminal based on artificial intelligence machine learning | |
| CN207518272U (en) | A kind of low-voltage intelligent idle compensating control | |
| CN102044877A (en) | Design method of three-tuned filter based on single-tuned filter equivalence | |
| CN110994636B (en) | SVG multi-machine parallel system based on CAN bus and carrier synchronization control method thereof | |
| CN105322887A (en) | Photovoltaic power station monitoring system | |
| CN104953591A (en) | LLCL type filter based on three-winding transformer | |
| CN111562459A (en) | Electric energy acquisition terminal based on artificial intelligence machine learning | |
| CN102946103B (en) | Active electric power filter device with self-testing function and testing method | |
| CN106406416B (en) | A kind of microwave energy fairing and method | |
| CN214479645U (en) | Intelligent photovoltaic combined net cage and system | |
| CN113452016A (en) | Harmonic analysis method for AC/DC hybrid power distribution network based on augmented node admittance matrix | |
| CN105337279B (en) | The analysis method and device of current and voltage harmonic during micro-capacitance sensor access power distribution network | |
| CN204408262U (en) | A kind of photovoltaic generating system | |
| CN102496872A (en) | Intelligent civil engineering power distribution station | |
| CN109116138B (en) | Parameter online measurement method for passive filter and reactive compensation device | |
| Jia et al. | Extended Two-Terminal Network Model of Parallel VSMs for Analysis of Active Power–Frequency Response | |
| CN208477028U (en) | A kind of aging testing system based on current transformer product | |
| CN113131535A (en) | Electric energy quality analysis method for AC/DC hybrid power distribution network | |
| CN112564174A (en) | Intelligent photovoltaic combined net cage and system | |
| CN101106332B (en) | A switch power device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| GR01 | Patent grant | ||
| GR01 | Patent grant |