CN111562459A - Electric energy acquisition terminal based on artificial intelligence machine learning - Google Patents
Electric energy acquisition terminal based on artificial intelligence machine learning Download PDFInfo
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- CN111562459A CN111562459A CN202010528137.XA CN202010528137A CN111562459A CN 111562459 A CN111562459 A CN 111562459A CN 202010528137 A CN202010528137 A CN 202010528137A CN 111562459 A CN111562459 A CN 111562459A
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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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 invention relates to the technical field of real-time monitoring of power supply and distribution networks, in particular to an electric energy acquisition terminal based on artificial intelligence machine learning. 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.
Disclosure of Invention
The invention mainly solves the technical problem of providing an electric energy acquisition terminal based on artificial intelligence machine learning, which can realize the measurement and acquisition of various electric parameters and electric energy quality parameters and can also realize the on-line monitoring equipment of a power grid for 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 invention can realize the on-line collection and storage of the electric parameters and the electric energy quality parameters, and can analyze the received data by using an artificial intelligent machine learning algorithm to recognize various identified waveforms and parameters, obtain the type and the power of the electric appliance of a low-voltage single-phase user, recognize the energy consumption rule of a high-voltage three-phase user and provide a 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 invention 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 diagram of the circuit of the present invention.
Detailed Description
Referring to fig. 1, the invention includes a main control CPU (integrated computation, analysis, computation of machine learning algorithm, receiving and sending data, providing an energy consumption analysis report), an RN7302 electric energy collection chip (collecting three-phase voltage current, various electric parameters and electric energy quality parameters), a high-speed external SPI communication (communicating with external components), a USB serial communication (communicating with a computer), a JTAG connector (performing program burning simulation with a computer), an expansion interface, a power management module (providing a stable power supply), an isolation power supply (performing electromagnetic isolation on the power supply to prevent high voltage from flowing into a low voltage side), a communication electrical isolation module (performing electromagnetic isolation on communication signals by using electromagnetic isolation and optical coupling isolation), a bus transceiver (improving the load capacity of a signal band), a three-phase current transformer (collecting alternating current on the high voltage side), a three-phase voltage processing circuit (collecting high voltage alternating current 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 three-dimensional model diagram is designed by utilizing automatic software for the electronic design of the Altium Designer, and the integrated circuit design is arranged and designed in an upper diagram mode.
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 |
|---|---|---|---|
| CN202010528137.XA CN111562459A (en) | 2020-06-11 | 2020-06-11 | Electric energy acquisition terminal based on artificial intelligence machine learning |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202010528137.XA CN111562459A (en) | 2020-06-11 | 2020-06-11 | Electric energy acquisition terminal based on artificial intelligence machine learning |
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| CN111562459A true CN111562459A (en) | 2020-08-21 |
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| CN202010528137.XA Pending CN111562459A (en) | 2020-06-11 | 2020-06-11 | Electric energy acquisition terminal based on artificial intelligence machine learning |
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