Author: idbox

The origin of the SCADA systems

The purpose of the first solid state SCADA systems was to collect data and monitor processes through slow and expensive computers or mainframes. This paved the way for data logging technology. Historians were presented to do just that; store and analyze the large amount of data captured by the SCADA system. Now, however, with 64-bit equipment, massive configuration tools, and next-level graphical user interfaces native to most SCADA products, there are no longer any traditional barriers to entry. The question is, what will be the role of these process control systems when we move to the next phase of manufacturing, also called industry 4.0?

SCADA in the smart factory

The reality is that SCADA as an operator interface, and the features that make it mandatory (such as schematic display, alarms, data logging, real-time monitoring, and data passing to data historians), are not going to be completely denied by IoT technology.

There is no doubt that Edge Computer, a system that processes or stores critical data locally and pushes all received data to a data center or storage repository in the cloud, will begin to encompass certain control functions and will rationalize the amount of data that we decide introduce into the cloud over time, but the Industrial Internet of Things will not negate the need to open and close valves, safely start or stop motors, or reset an actuator.

Ultimately, one cannot compare IIoT solely to Data Acquisition (DA) and forget about Supervisory Control (SC) and the need for reliability, security, fast aggregation, and complex data storage.

4th generation SCADA: the adoption of IoT

One trend that is emerging in Industry 4.0 is the move towards the IIoT cloud. Traditionally, data collected from industrial sensors has moved from proprietary Programmable Logic Controllers (PLCs) to Supervisory Control and Data Acquisition Systems (SCADA) for analysis, with many layers in between. But cloud IIoT is opening up all of this and reducing the number of layers from data capture to actionable intelligence.

IoT and SCADA, complementary technologies for Industry 4.0

Digitalization is driving changes in the way manufacturers operate. The hierarchical nature is slowly changing as a peer-to-peer model opens up through the IoT.

So will IIoT replace SCADA systems? For critical high-value industrial processes, I conclude no. Can the two concepts ever be integrated? Yes, despite traditional SCADA systems operating in the “micro” environment of manufacturing, collecting and visualizing the day-to-day operations of a factory or process, a more powerful SCADA is here to stay. And yes, Industry 4.0 and IIoT belong to the “macro” environment.

The information generated from SCADA systems acts as one of the data sources for IoT. SCADA’s focus is on monitoring and control. The IoT approach is firmly focused on analyzing machine data to improve your productivity and impact your top line. How can we meet consumer needs faster, cheaper and with better quality? SCADA / IoT platforms are the fourth generation visualization that will answer this question.

As the planet continues to warm, the impacts of climate change worsen, but we have a new tool to help us better manage the impacts and protect the planet: artificial intelligence (AI). AI refers to computer systems that can sense their surroundings, think, learn, and act in response to what they perceive and their programmed goals.

In India, Artificial Intelligence has helped farmers achieve 30 percent higher peanut yields per hectare by providing information on land preparation, fertilizer application and choice of planting dates. In Norway, AI helped create a flexible and autonomous power grid, integrating more renewable energy. Artificial Intelligence manages to identify tropical cyclones, meteorological fronts, atmospheric rivers, the latter can cause intense rainfall and it is often difficult for humans to identify themselves. AI also improves weather predictions, and they can help keep people safe.

What are artificial intelligence, machine learning and deep learning or deep learning?

Artificial Intelligence capabilities are rapidly improving thanks to several factors: the large amount of data that is collected by sensors, satellites, and the Internet; the development of more powerful and faster computers; the availability of open source software and data; and the increase in abundant and cheap storage. Artificial Intelligence can now quickly perceive patterns that humans cannot, make predictions more efficiently, and recommend better actions.

What exists today is a “narrow” artificial intelligence, which is task-oriented and capable of doing some things, sometimes better than what humans can do, such as recognizing speech or images and forecasting the weather. Playing chess and classifying images, as in tagging people on Facebook, are examples of limited artificial intelligence.

When Netflix and Amazon recommend shows and products based on our purchase history, they use Machine Learning. Machine learning, which was developed from earlier AI, involves the use of algorithms (sets of rules to follow to solve a problem) that can learn from data. The more data the system analyzes, the more accurate it becomes as the system develops its own rules and the software evolves to achieve its goal.

For example, Deep Learning allowed a computer system to figure out how to identify a cat, without any human input on the cat’s characteristics, after “viewing” 10 million random YouTube images.

Artificial Intelligence is a change in the ecosystem

Microsoft believes that artificial intelligence, which often encompasses machine learning and deep learning, is a “game changer” for climate change and environmental issues.

The winds can cause great catastrophes in many countries, destroying different species of trees. But how is it possible to differentiate one species from another by looking at a green mass from above over such a large area? The human eye could theoretically do this, but it would take forever to process thousands of images. To do this, today artificial intelligence is being used to analyze high resolution photographs and match them with the data, each tree in each plot is mapped and identified.

Understanding how the distribution and composition of forests change in response to hurricanes is important because when forests are damaged, vegetation decomposes and releases more CO2 into the atmosphere. As trees grow back, since they are smaller, they store less carbon. If climate change results in more extreme storms, some forests will not recover, less carbon will be stored, and more carbon will remain in the atmosphere, exacerbating global warming. But all this is in a process of change with Artificial Intelligence.

Another very attractive case of Artificial Intelligence is how it can improve our ecosystem, collecting data on the growth of animals, tracking movements, monitoring weather conditions. All this process will achieve a model that will help to improve the ecosystem, the management of the hatcheries, the harvest, the protection of the habitat … etc.

How to use Artificial Intelligence for energy

AI is increasingly used to manage renewable energy so that more energy can be brought into the grid; It can also handle power fluctuations and improve storage.

Power labs use machine learning and Artificial Intelligence to identify vulnerabilities in the network, strengthen them before possible failures, and restore power more quickly when failures occur.

The systems first study part of the grid, analyzing data from renewable energy sources, battery storage and satellite imagery that can show where trees growing over power lines could cause problems in a storm. The goal is to develop a grid that can automatically manage renewable energy without interruption and recover from system failures with little human involvement.

Wind companies are using Artificial Intelligence so that the propeller of each turbine produces more electricity per rotation incorporating meteorological and operational data in real time. In large wind farms, the front row blades create a wake that reduces the efficiency of those behind them. IA will enable each individual propeller to determine the wind speed and direction coming from other propellers, and adjust accordingly.

Artificial Intelligence can also improve energy efficiency. Google used machine learning to help predict when the energy in its data centers was most in demand. The system analyzed and predicted when users were most likely to view data-heavy YouTube videos, for example, and could optimize the cooling required. As a result, Google reduced its energy use by 40 percent.

Make cities more livable and sustainable

Artificial Intelligence can also improve energy efficiency at the city scale by incorporating data from smart meters and the Internet of Things (the Internet of computing devices that are embedded in everyday objects, allowing them to send and receive data) to forecast energy demand. . Additionally, artificial intelligence systems help with urban planning and disaster preparedness. One vision for a sustainable city is to create an ‘urban dashboard’ consisting of real-time data on energy and water consumption and availability, traffic and climate to make cities more efficient and livable.

AI can forecast air pollution, track sources of pollution, and generate possible strategies to reduce it. You can determine whether, for example, it would be more effective to restrict the number of controllers or shut down certain power plants to reduce pollution in a particular area.

Smart Agriculture

Data from sensors in the field that monitor crop moisture, soil composition, and temperature help AI improve production and know when crops need watering. Incorporating this information with that of drones, which are also used to monitor conditions, can help increasingly automatic AI systems know the best times to plant, spray and harvest crops, and when to avoid disease and other. problems. This will result in higher efficiency, higher yields, and less use of water, fertilizers, and pesticides.

A more sustainable and intelligent transport

As vehicles can communicate with each other and with infrastructure, artificial intelligence will help drivers avoid dangers and traffic jams. In some cities, artificial intelligence systems have been implemented that incorporate sensors and cameras that monitor the flow of traffic by adjusting to traffic lights when necessary. Less downtime, of course, means less greenhouse gas emissions.

AI has many other uses

Artificial Intelligence can monitor drinking water quality, manage residential water use, detect underground leaks in drinking water supply systems, and predict when water plants need maintenance. It can also simulate weather events and natural disasters to find vulnerabilities in disaster planning, determine which disaster response strategies are most effective, and provide real-time disaster response coordination.