Hello, good morning. I'm Inru Dharaka from the Department of Electrical Engineering, University of Moratua. Our project is, locating faults of medium voltage overhead lines by monitoring the magnetic field vector. Our group members are Lairu Sudharaka, Gihar Rikramatunga and myself. During this presentation, I'm going to discuss the following topics. Strong reliable bits are the better of modern society. But our systems can be subjected to disturbances at any time in a way. So it is very important to identify the fault location to take immediate actions. This has been addressed in different ways in the literature review. In the impedance-based method, the impedance seen by two ends of the fault line is calculated and the fault location is identified based on the impedance profile of the line. The traveling wave method basically relies on the time taken by the fault evave to reach the fault detectors. Due to increased electrification, there is a high necessity of layering faults as soon as possible to minimize the impact on customers. So in this project, we are presenting a new efficient method to identify the faults in three-phase medium voltage overhead lines using the magnetic field vector. So when we move to our objectives, our main objectives are to identify any fault condition in medium voltage overhead lines and localize it accurately so as to minimize the restoration time. This is the overall structure of our proposed system. It shows where our sensors are placed and how data is circulated. The fault detection algorithm is executed in each sensor node and when it detects a fault condition, the captured fault data is transmitted to the main server. There, the fault localization process is taken place and informed to relevant authorities where wave application OSMS. Let me explain finalized algorithms in coming up slides. For the clarity of understanding purposes, I'm going to demonstrate our methodology using this example fault condition. Here, the fault has occurred between the sensor node 0 and sensor node 1. Let's first discuss how these fault is captured by the sensors. In our methodology, first we confirm the positioning of hall effect sensors such that each sensor node is having two hall effect sensors which are placed perpendicular to each other in a horizontal plane. The figure shows a visual image of the sensor placement on a distribution line pole. Here is the finalized fault detection algorithm which is used to capture the fault. This algorithm is totally our own creation and it is developed based on our experiments and observations. As a part of this project, we developed a MATLAB simulating model for hall effect sensor which is not readily available in the MATLAB library. Another key feature in our solution is the development of dynamic threshold function which has self-defined threshold value. This makes our system more reliable and smarter. Let's go through each step of this algorithm. Okay, so theoretically, the sensor outputs a sinusoidal wave which is related to the magnetic flux density. But in practical scenario during the AeroD connoition, it captures the sinusoidal signal together with its sensor noise component. We consider this noise effect by adding wide Gaussian noise with the signal to noise ratio of 12.5 decibels into our simulation. Then we send this data feed through a low pass filter to minimize the noise effect. As it is not practical to achieve 100% noise filtration, we have further improved of algorithm to minimize the noise effect from the remaining noise component and enhance the reliability of the system performance. Then we send this data set through a two cascade RMS functions. They have fundamental frequency of 50 hertz. Here our intention is to minimize the oscillations of the signal to make it convenient for decision making and data extraction. After that, we take its discrete derivation to neglect any steady state condition as we are concerned about the certain variations in the current flow at any fault. Next, we send this data feed through a dynamic threshold block. This function is developed such that it can identify the variations of the incoming signal to determine whether it is a noise over a fault condition. As I mentioned earlier, it uses a dynamic threshold value defined by itself. Once it detects a fault condition, the signal is released to go through the final RMS block which provides a smooth positive sided signal for data extraction. Here it starts processing incoming data feed to find out its peak value. It is carried out for a given period of time which depends on the line protection device settings. This peak value will be sent to the center server with corresponding sense ID. Now the fault locating algorithm is executed using the peak values received from the old sensors. It subtracts peak values from the adjacent sensor nodes and identify which adjacent sensor nodes have the highest variation in their peak values. Now it is possible to identify the GPS coordinates of the fault boundaries using the given database of sensor IDs with their installed location data. This is the single line diagram of our model 11kV distribution line. We used the system to conduct our experiments and develop algorithms discussed early. This is an actual representation of the FLECO distribution network with actual line parameters of 6 km long 11kV distribution line at WPTS substation. We have validated our model against the actual response of FLECO feeder under various types of faults with same fault impedance at the same distance from the substations. Moving to the results, let's consider this line to ground fault with 20 ohms fault impedance. This has been occurred at 750 meters away from the substation. The fault is between sensor node 0 and sensor node 1. So if you check the legend, this plot shows a noticeable variation between the waveforms of sensor node 0 and 1 in white and yellow colors. Therefore, it is clear that the highest peak value difference exceeds between the waveforms of sensor node 0 and 1. So we can clearly identify node 0 and 1 as the fault segment. Likewise, we verified accuracy of our algorithm going through various fault types differing the fault distance from the substation and fault impedance levels. Considering the practical implementation, we have developed data handling procedure in the microcontroller to have a real time data processing. Here we propose ESP32 microcontroller embedded with free real time operating system. Further, it is possible to inform the fault location data to relevant authorities through a web platform over SMS. Currently, we are working on this practical implementation with the help of Amit Baba consultant's private limit. Under this project, we have newly developed algorithm to locate faults in overhead distribution lines by monitoring the resultant magnetic field vector. It is tested and proven in a simulated environment that our algorithm is independent from fault location, fault type and fault impedance bounded to a given value. Further, the performance of the model distribution line was well dated using a real distribution line data in Lecognite work. So we believe this project has a greater potential to impact the enhancement of power system reliability. We are grateful to our supervisors, Lead Professor Ranjeet Pereira, Dr. W. D. Pesad, and Engineer Rianci Fanando for their guidance and support provided throughout this project. Especially we would like to commemorate our loving supervisor, Lead Professor Ranjeet Pereira, who laid the foundation on which we developed the whole structure of this project. Thank you.