Electric substations are the backbone of our power systems. They face many dangers like heat, noise, and even animal waste. These dangers can cause problems like cable failures, circuit breaker issues, and melted conductors. When these issues happen, the substation's reliability drops, and power losses occur during transmission. To fix this, we need to optimize the voltage profile using Distributed Generation (DG). This involves studying faults and transients in substations and using AI to optimize various parameters.

One way to do this is by simulating a 500kV substation using advanced software. This software, called Electrical Transient Analyzer Program (ETAP), performs detailed load flow analysis and short circuit studies. The simulation uses real-time data from the past eighteen months. This data includes both normal and faulty conditions. The first step is to classify these conditions as normal or faulty. The second step is to identify the type of fault, such as line-to-line, line-to-ground, or double line-to-ground.

Several AI techniques are used for this task. Catboost, Support Vector Machine (SVM), and Logistic Regression are the top performers. In the first step, Catboost classifies conditions with 98% accuracy, SVM with 96%, and Logistic Regression with 93%. In the second step, identifying different faulty conditions, Catboost achieves 97% accuracy, SVM 95%, and Logistic Regression 92%.

These AI techniques help in predicting and preventing faults in substations. By using real-time data and advanced software, we can make our power systems more reliable and efficient. This is crucial for ensuring that we have a stable power supply. However, it's important to note that while AI can help, it's not a magic solution. Regular maintenance and human oversight are still necessary to keep our power systems running smoothly.

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