How AI is Revolutionizing Predictive Maintenance in Smart Grids? A Complete Guide

In today’s world, the electricity system is becoming more intelligent and efficient with smart grids. Smart grids utilize advanced technologies, such as artificial intelligence, to monitor, manage, and deliver electricity efficiently. To prevent equipment failure and maintain the overall health of the system, predictive maintenance is crucial for smart grids. When it comes to predictive maintenance, the most powerful tool is AI, or artificial intelligence. This article describes the implementation of AI-based predictive maintenance in smart grids. But “How AI is Revolutionizing Predictive Maintenance in Smart Grids?”. Let’s break it down.

What Is a Smart Grid?

A smart grid or smart electricity grid is an advanced electricity distribution network that incorporates information and communication technologies, sensors, and software. It uses two-way communication, which enables the flow of electricity and real-time data between the power system and all network participants, including producers, distributors, and consumers.

In a smart grid, the use of real-time data enhances the overall efficiency of the electricity supply system. These local energy systems can operate on their own or connect to the main power grid. The major difference between the traditional electrical grid and the smart grid is that the traditional one uses a one-way electricity flow, whereas the smart grid is an IoT-enabled application that uses two-way communication, advanced IT, the internet, and smart automation devices to improve the stability and reliability of the grid.

Importance of Predictive Maintenance in Smart Grids

Assessment of Device Conditions:

By using the data from the smart grid devices, the operators can identify the devices that are not in good condition. For example, if they identify that any transformer is operating with less capacity, then they redistribute the load to other transformers.

Reduces Maintenance Cost:

The data from the smart grids is analyzed to identify potential faults present in the system. The operators take necessary actions before they result in system failures or the devices get permanently damaged.

Improves System’s Reliability:

Taking necessary actions on system faults reduces downtime and energy losses, and improves the system’s reliability.

Improves Safety:

Identification of potential safety hazards prevents on-site accidents and improves the safety of workers.

Role of AI in Minimizing Downtime and Energy Losses

How AI Helps in Minimizing Downtime?

Predictive Maintenance: The AI algorithms analyze the data collected from sensors and equipment and alert the operators to fix the faults and failures before they cause downtime.
Fault Detection: The AI-powered system can easily detect and diagnose the faults in the system, which prevents downtime.
Control Energy Flow: The AI algorithms can forecast the load demand at different times of the day. According to the information, it shifts the load, manages battery usage, and controls energy flow to avoid downtime.

How AI Helps in Cutting Energy Losses?

Energy Demand Forecasting: The AI algorithms can forecast the energy demand, the generated energy, and energy consumption, which helps in keeping the system balanced.
Energy Storage Management: The AI-powered tools can optimize when to store and release energy, which reduces energy waste and extends battery life.

How AI is Revolutionizing Predictive Maintenance in Smart Grids?

How do AI/ML models analyze large-scale sensor data?

AI and machine learning methods can handle large-scale data generated from various sensors placed throughout the grid, such as transformers, breakers, substations, power lines, etc. The AI/ML can analyze the large-scale data in 5 steps, which are described below.

Data Collection:

A smart grid has thousands of sensors connected to its different parts, and they send real-time information about the equipment to the controller of the system. This information is used to track the equipment’s condition and grid performance. This information or data includes changes in voltage levels, current, temperature spikes in transformers, vibrations in rotating machinery, etc.

Data Preprocessing and Organization

The data collected from the sensors includes noise, missing values, or duplicate entries. So, after collecting the data, the AI algorithms go through a process known as “Data Preprocessing,” which includes cleaning, organizing, and formatting of data. This process helps to analyze the data properly.

Learning from Patterns

The machine learning models are properly trained using the historical data, real-time data, normal operating conditions, fault conditions, and past equipment failures. This helps the models learn what normal behavior looks like and what conditions lead to faults and equipment failures.

Real-time Prediction

Once the machine learning models are trained, the AI system can analyze the sensor data in real-time. If the AI tool finds any types of unusual behavior, such as a sudden change in voltage or temperature, then it alerts the maintenance team before a breakdown happens. This whole process is known as predictive maintenance.

Continuous Improvement

The AI/Machine Learning models learn continuously day by day. With more data flowing in, the models adjust and improve the prediction. With time, the AI models get smarter and can make better decisions.

Common Algorithms Used:

Decision trees:

The Decision Trees algorithm works like a flow chart. It is easy to interpret, and the predictions are based on live sensor data. This decision tree has a root, which is the main question, and some branches, also called nodes, which are a series of “Yes” or “No” questions. This process eventually leads to a final answer. The final answer is like “Maintenance required soon” or “System is operating normally”.

Neural networks:

The Neural Networks (NN) algorithm consists of layers of “neurons” that process information. The Artificial Neural Networks connect artificial neurons to make decisions, solve problems, make predictions, and recognize patterns.

In smart grids, the neural networks are trained to understand how the system behaves under normal or faulty conditions. The algorithms are trained using historical data, past equipment failures, normal operating conditions, etc., so that they can detect complex and non-obvious patterns.

Anomaly detection:

The process of identifying data that does not match normal behavior is known as anomaly detection. The anomalies in smart grids look like a sudden drop in voltage, an abnormal rise in temperature, a sudden increase in vibration pattern, abnormal current flows, etc.

Core Technologies Used in AI-Based Predictive Maintenance

IoT Sensors: The IoT sensors, or Internet of Things sensors, are small connected devices that are installed in critical components of smart grids like transformers, batteries, power lines, circuit breakers, etc. These sensors constantly measure parameters like voltage, current, temperature change, vibration, etc., and send the data to the controller unit.

The IoT sensors send data to the AI system, which can recognize the pattern and detect something unusual. Then the AI system sends an alert to the operator so that they can take necessary action before the malfunction occurs.

Cloud Computing: It is an advanced technology that uses internet-based servers to store, manage, and process data. It provides scalable storage for the enormous amount of data generated by smart grids. The data stored in the cloud can be easily accessed by utility companies from anywhere.

Edge AI: Edge AI is the combination of Artificial Intelligence and edge computing. With Edge AI, the AI algorithms can be directly run on local devices like sensors, gateways, etc., instead of sending a large amount of raw data to the clouds for processing.

Case Studies

DUKE Energy

In the United States, the electric power holding company DUKE Energy has implemented AI-driven tools to monitor grid assets. The AI-powered tools collect and analyze data from various sensors installed in transformers and breakers. This reduces unexpected transformer failures and improves maintenance efficiency.

Reference: DUKE Energy

2. Enel Group

In Italy, the Enel Group has deployed an AI-powered platform to monitor the grid components. The AI tools continuously monitor the voltage, current, and temperature to detect any potential faults present in the system.

Reference: Enel Group

3. TEPCO Power Grid

In Japan, the TEPCO power grid installed AI-powered tools to reduce service interruptions. The AI algorithms use both historical data and real-time data to predict potential faults in transmission lines and substations.

Reference: TEPCO Energy

Advantages of AI-Based Predictive Maintenance in Smart Grids

The AI algorithms analyze the data collected from the equipment sensors and detect any potential faults present in the system. This allows the operators to take necessary actions before it leads to any costly breakdown.
The use of AI-powered tools improves the reliability of the smart grids.
The AI tools can detect potential faults and failures early, which avoids expensive traditional maintenance. This saves time, money, and resources.
The AI-based preventive maintenance helps in improving the life span of the grid infrastructure.
The early detection of faults and failures improves the overall efficiency of the microgrids, which also leads to less energy loss and carbon footprint.
The real-time data provided by AI tools helps the grid operators make smarter decisions, which leads to better long-term planning and resource use.

Conclusion

The combination of smart technologies like artificial intelligence and predictive maintenance in smart grids is not just the future but one of the most advanced innovations in the energy sector. The involvement of AI improves the efficiency of smart grids, reduces downtime, and minimizes energy losses. The AI-based predictive maintenance is a game-changer for utility companies worldwide.

FAQS

What Is a Smart Grid?

2. What are IoT sensors?

The IoT sensors, or Internet of Things sensors, are small connected devices that are installed in critical components of smart grids like transformers, batteries, power lines, circuit breakers, etc.

How AI is Revolutionizing Predictive Maintenance in Smart Grids? A Complete Guide

What Is a Smart Grid?

Importance of Predictive Maintenance in Smart Grids