Smart Assistance and AI Visualization Gateway Solution for Power Substations Based on RK3576

Posted by Andy L.
4
5 days ago
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Amidst the ongoing reforms in the power industry, the intelligent management of key electrical infrastructure such as substations and distribution rooms has become a crucial driver for ensuring the safe, economical, and efficient operation of power grids. With the growing demand for electricity and the accelerating process of urbanization, the number of power substations has surged, with widespread distribution and complex environmental conditions. This presents unprecedented challenges to the stability, reliability, and safety of power systems.

Smart Assistance and AI Visualization Gateway Solution for Power Substations Based on RK3576

Traditional monitoring systems for power rooms face significant limitations: independent operation of subsystems, severe information silos, and consequently low overall monitoring efficiency. This fragmented management approach not only increases operation and maintenance costs but also hampers rapid response to emergencies, with insufficient capability for early warning of potential risks. These issues severely constrain the intelligent upgrading and efficient operation of power grids.

To address these problems, the Smart Assistance and AI Visualization Gateway for power substations has emerged.

Designed specifically for power scenarios such as substations and distribution rooms, this innovative solution integrates LCD screens with advanced AI technology, enabling comprehensive visualization, monitoring, and management of on-site parameters. This intelligent gateway can monitor real-time information from multiple dimensions, including transformer status, switch gear operation, environmental temperature and humidity, air conditioning and fan status, lighting systems, smoke detection, access control, and video surveillance, creating a comprehensive and multidimensional monitoring network for power substations.

Smart Assistance and AI Visualization Gateway Solution for Power Substations Based on RK3576

By intelligently analyzing real-time data collected from various sensors and accurately comparing it with preset thresholds, the intelligent gateway can quickly identify and respond to anomalies, providing features such as automatic alarms, remote control, and fault prediction. This significantly enhances the safety protection level and emergency response capability of power substations. Furthermore, its powerful data integration and analysis capabilities offer robust data support for operational decision-making, facilitating the refined and intelligent transformation of maintenance management.

1. Client Requirements

Through communication with the client, Forlinx Embedded has identified the following requirements for the main control unit of the Smart Assistance and AI Visualization Gateway project for power substations:

(1) The gateway's core CPU should use an ARM architecture with at least 4 cores and a clock speed of no less than 1GHz;

(2) The gateway should include a video processing module for video stream processing and AI analysis, with computing power of no less than 3 TOPS;

(3) The gateway should have at least 2GB of memory and 8GB of storage capacity;

(4) The gateway should use a Linux kernel operating system;

(5) The gateway should include at least 4 RS485 ports, with selectable serial port speeds of 9600bps, 19200bps, and 115200bps;

(6) The gateway should support Ethernet interfaces with adaptive rates of 100/1000Mbps.

2. Main Control Selection: RK3576 Processor

Based on the actual project requirements, the client has chosen the Rockchip RK3576 processor as the main control unit for the Smart Assistance and AI Visualization Gateway for power substations. Based on the actual project requirements, the client has chosen the Rockchip RK3576 processor as the main control unit for the Smart Assistance and AI Visualization Gateway for power substations.

Smart Assistance and AI Visualization Gateway Solution for Power Substations Based on RK3576

In terms of performance, the RK3576 processor features 4 x Cortex-A72 and 4 x Cortex-A53 high-performance cores, with a maximum clock speed of 2.3GHz and an integrated 6 TOPS NPU. This provides robust performance support for the Smart Assistance and AI Visualization Gateway for power substations and ample computational power for scenarios such as helmet detection, small animal intrusion, and personnel fall detection.

Regarding functional interfaces, FET3576-C system on module exposes all functions of the RK3576 processor, with 12 x UART supporting 5, 6, 7, and 8-bit serial data transmission; 2 x Ethernet supporting 10/100/1000Mbps data transmission rates; a FlexBus interface supporting parallel data transmission with up to 100MHz clock speed; and various additional interfaces including DSMC, PCIe2.1, SATA3.0, USB3.2, SAI, and I2C.

For display capabilities, FET3576-C SoM supports H.264 and H.265 HD video encoding and decoding, with five display interfaces: HDMI/eDP, MIPI DSI, Parallel, EBC, and DP. It supports three-screen display, 4K@120Hz ultra-clear display, and super-resolution functions, enhancing customer experience with intelligent image correction and a range of display options.

In terms of configuration, FET3576-C SoM runs on Linux 6.1.57, with 32GB of eMMC ROM and options for 2GB or 4GB LPDDR4 RAM, allowing for flexible project requirements.

An industrial-grade version, FET3576J-C SoM, will be introduced with additional configurations to meet various application needs.

3. Summary

The widespread application of the Smart Assistance and AI Visualization Gateway for power substations represents a profound transformation of traditional monitoring systems and a key step towards the intelligent, networked, and service-oriented evolution of the power industry. Its unique advantages inject new vitality into the safe and economical operation of power grids, supporting robust development in complex and changing environments.

This solution is expected to assist in project selection and provide valuable support.

Originally published at www.forlinx.net.

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