This blog discusses Buzz Solutions’ Electric Grid Boosting, NVIDIA Inception, PowerGUARD platform, and NVIDIA DeepStream SDK.
Buzz Solutions Boosts the Electric Grid with Vision AI. The electric grid’s dependability is vital. Utility firms have a lot to monitor, from managing spikes in demand and changing power requirements to averting infrastructure breakdowns that could result in wildfires.
By employing AI to enhance utilities’ infrastructure monitoring and maintenance, Buzz Solutions, a participant in NVIDIA’s Inception program for innovative entrepreneurs, is contributing.
Buzz Solutions assists utility companies in analyzing the vast quantities of inspection data that are gathered by helicopters and drones. Before they result in outages or wildfires, the company’s in-house machine learning algorithms detect possible problems including rusted and broken parts, encroaching vegetation, and unwanted wildlife visits.
Buzz Solutions developed PowerGUARD, a container-based application pipeline that employs AI to instantly analyze video data from substation cameras, to assist in resolving substation problems. After annotating the video and identifying security, safety, fire, smoke, and equipment problems, it notifies the user via email or a dashboard.
Use PowerGUARD to get the most out of substation video monitoring. Electric utilities can use PowerGUARD, a software program, to automatically identify substation security, safety, and equipment condition problems. The program examines live footage from substation cameras, and when it detects something, it notifies users and shows the information in the PowerGUARD program very instantly.
NVIDIA Inception
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What is NVIDIA Inception?
NVIDIA Inception is a program created to assist companies in accelerating commercial growth and technological innovation at every level of their development. Inception is free and offers its worldwide community members significant advantages from partners and NVIDIA.
The PowerGUARD platform
A list of alerts found by skilled AI detection algorithms is shown by the PowerGUARD program. Along with event photos, the listing contains the camera number, alert ID, time, and alert kind.
PowerGUARD Capabilities
PowerGUARD frees up utility resources by automating the analysis of streaming substation video to precisely identify more hazards. The software converts substation video into actionable alerts by streamlining a five-step procedure.
PowerGUARD Workflow
- Analyzing substation videos and monitoring alerts.
- Using PTZ fixed cameras, stream thermal and optical videos in real time.
- Imagine: Details of the alert detection are shown on the PowerGUARD interface.
- Examine: Security, safety, thermal, and visual equipment condition problems are detected by pre-trained AI algorithms.
- Set priorities: Work order management systems can prioritise detections through the interface.
- Be mindful: Software notifications, text messages, and emails are used to notify the appropriate teams.
Benefits of PowerGUARD
- Increased security: Security teams can become fully aware of unauthorized incidents, such as vehicle, human, and animal incursions, with the use of PowerGUARD. With this information, utilities can lower the chance of future attacks, theft, and vandalism.
- Improved safety: Emergency professionals can respond more quickly when PowerGUARD notifies them of injuries and important incidents earlier. In order to lower the risk of injuries, it also highlights the necessity for more PPE training.
- Decreased outages: Substation engineers can respond more quickly to high-energy incidents like fire, smoke, and arc flashing when they use PowerGUARD. Additionally, engineers can prevent outages by detecting equipment anomalies and thermal overloading.
- Savings of resources: Utility workers don’t need to continuously watch substation video feeds since PowerGUARD software alerts them to significant substation incidents.
- Reduced truck rolls: At unmanned substations, combining PowerGUARD with fixed PTZ cameras allows for proactive monitoring and minimises the number of truck rolls needed to assess substation conditions.
To appreciate the Buzz Solutions team’s efforts to innovate! PowerGUARD will improve the ability to closely monitor substation equipment and help maintain a more secure grid and substations.
PowerGUARD Features
- Thermal and optical video from PTZ fixed cameras are supported.
- Accurate AI identification of safety and security incidents as well as abnormalities in equipment condition.
- Software notifications, text messages, and email alerts.
- Software interface for prioritizing and viewing warnings.
PowerGUARD processes and infers video streams for real-time video analytics using the NVIDIA DeepStream software development kit. To increase efficiency, cut expenses, and save time, DeepStream operates on cloud-based virtual machines or on the NVIDIA Jetson edge AI platform under the NVIDIA Metropolis architecture.
NVIDIA DeepStream SDK
You can create vision AI apps and services more quickly and easily with DeepStream’s multi-platform support. With a single click, you can even deploy them in the cloud, on the edge, and on-premises.
The AI is only getting started in the utility sector since it allows employees to respond instead of spending months going over photos by hand. Also are only beginning to see AI make its way into the energy industry and begin to offer significant benefits.
What is NVIDIA DeepStream?
For AI-based multi-sensor processing, video, audio, and picture comprehension, NVIDIA’s DeepStream SDK is a comprehensive streaming analytics toolkit built on top of Streamer. It’s perfect for OEMs, software partners, startups, and vision AI developers creating IVA apps and services.
Neural networks and other sophisticated processing tasks, such as tracking, video encoding/decoding, and video rendering, can now be integrated into stream-processing pipelines. Real-time analytics on sensor, image, and video data are made possible by these pipelines.
NVIDIA Metropolis, the platform for creating end-to-end services and solutions that convert pixel and sensor data into useful insights, relies heavily on DeepStream.
Advantages
- Strong and Adaptable SDK: DeepStream SDK is perfect for a variety of use cases in a large number of industries.
- Numerous Programming Options: Use Python, C/C++, or the straightforward and user-friendly UI of Graph Composer to create robust vision AI applications.
- Understanding rich and multimodal real-time sensor data at the edge is possible with real-time insights.
- Managed AI Services: Use Kubernetes to orchestrate the deployment of AI services in cloud-native containers.
- Decreased TCO: By using DeepStream to deploy models and the TAO tools to train, modify, and optimise them, stream density can be increased.
Unique Capabilities
- Savour the Smooth Transition from Edge to Cloud:
You can create smooth streaming pipelines for AI-based video, audio, and image analytics more quickly and easily with DeepStream. More than 40 hardware-accelerated plugins and extensions are included with it to improve message brokers, multi-object tracking, pre/post processing, inference, and other areas. It also provides some of the best multi-object, real-time trackers in the world.
Build cloud native apps with ease using DeepStream’s off-the-shelf containers. These apps can be installed on NVIDIA Jetson, workstations with NVIDIA GPUs, and public and private clouds. Its “develop once, deploy anywhere” methodology offers excellent scalability and streamlines code management. Additionally, the DeepStream Container Builder tool facilitates the development of cloud-native, high-performance AI applications using NVIDIA NGC containers, which are simple to scale and administer using Helm Charts and Kubernetes.
DeepStream REST-APIs make it easier to create SaaS applications by enabling you to manipulate various parameters at run-time. You can integrate into your current apps or create online portals for control and configuration using the standard REST-API interface.
- Construct Complete AI Solutions:
By using NVIDIA Metropolis to build an end-to-end vision AI system, you may accelerate development efforts overall and get better real-time performance. Create production-quality vision AI models first, then use TAO Toolkit to modify and improve them before deploying them with DeepStream.
Choose your inference path and enjoy amazing freedom, ranging from quick prototype to large production level solutions. Models may be deployed in native frameworks like PyTorch and TensorFlow for inference to native interaction with NVIDIA Triton Inference Server. The optimal performance can also be attained by using NVIDIA TensorRT for high-throughput inference, which offers multi-GPU, multi-stream, and batching support options.
A new developer tool called PipeTuner 1.0 now makes it simple to adjust a large number of parameters to optimize AI pipelines for tracking and inference.
- Quicken the Development of Vision AI:
More than thirty sample apps are included with the DeepStream SDK to help you get started with your programming. The majority of the samples run on both the NVIDIA Jetson and dGPU platforms and are provided in C/C++, Python, and Graph Composer versions. You no longer need to access distant Linux systems in order to create in Windows environments to support for Windows Subsystem for Linux (WSL2).
By abstracting away the complexity of GStreamer, DeepStream Service Maker makes it easier to create C++ object-oriented applications. With just a few lines of code, you can create entire DeepStream pipelines with Service Maker.
DeepStream Libraries, which provide low-level GPU-accelerated operations to optimise the pre and post stages of visual AI pipelines, are driven by CV-CUDA, NvImageCodec, and PyNvVideoCodec.
With the help of Container Builder, DeepStream developers may swiftly launch complex pipelines to Graph Composer’s robust, low-code development option.
- Develop AI Applications of the Future:
For deterministic systems like automated quality control lines and robotic arms to integrate, tight scheduling control, customised schedulers, and effective resource management are essential.
Integration with control signals that function on a different temporal domain than the vision streaming sensors being analysed by a DeepStream pipeline is made simple with the advent of Graph eXecution Format (GXF).
You can accelerate the creation of generative AI apps with the aid of new reference applications. Additionally, BEVFusion, a new sensor fusion feature, expands the use cases for developers by adding lidar and radar inputs that may be merged with camera inputs.
- Obtain a Vision AI Solution That Is Ready for Production:
As a component of NVIDIA AI Enterprise, an end-to-end, secure, cloud-native AI software platform designed to propel businesses to the forefront of AI, DeepStream is accessible.
NVIDIA AI Enterprise offers enterprise-grade support, security, and API stability to reduce the possible risks of open-source software, validation and integration for NVIDIA AI open-source software, access to AI solution workflows to expedite time to production, and certifications to deploy AI globally.
