arXiv subjects

Anwarul Islam Sifat

Publications and source records attributed to Anwarul Islam Sifat.

4 recordsLinked to original sources

arXiv2026

Real-time Load Current Monitoring of Overhead Lines Using GMR Sensors

Non-contact current monitoring has emerged as a prominent research focus owing to its non-intrusive characteristics and low maintenance requirements. However, while they offer high sensitivity, contactless sensors necessitate sophisticated design methodologies and thorough experimental validation. In this study, a Giant Magneto-Resistance (GMR) sensor is employed to monitor the instantaneous currents of a three-phase 400-volt overhead line, and its performance is evaluated against that of a conventional contact-based Hall effect sensor. A mathematical framework is developed to calculate current from the measured magnetic field signals. Furthermore, a MATLAB-based dashboard is implemented to enable real-time visualization of current measurements from both sensors under linear and non-linear load conditions. The GMR current sensor achieved a relative accuracy of 64.64% to 91.49%, with most phases above 80%. Identified improvements over this are possible, indicating that the sensing method has potential as a basis for calculating phase currents.

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arXiv2023

Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes

Recently, there has been a major emphasis on developing data-driven approaches involving machine learning (ML) for high-speed static state estimation (SE) in power systems. The emphasis stems from the ability of ML to overcome difficulties associated with model-based approaches, such as handling of non-Gaussian measurement noise. However, topology changes pose a stiff challenge for performing ML-based SE because the training and test environments become different when such changes occur. This paper circumvents this challenge by formulating a graph neural network (GNN)-based time-synchronized state estimator that considers the physical connections of the power system during the training itself. The results obtained using the IEEE 118-bus system indicate that the GNN-based state estimator outperforms both the model-based linear state estimator and a data-driven deep neural network-based state estimator in the presence of non-Gaussian measurement noise and topology changes, respectively.

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arXiv2022

Data-Driven Flow and Injection Estimation in PMU-Unobservable Transmission Systems

Fast and accurate knowledge of power flows and power injections is needed for a variety of applications in the electric grid. Phasor measurement units (PMUs) can be used to directly compute them at high speeds; however, a large number of PMUs will be needed for computing all the flows and injections. Similarly, if they are calculated from the outputs of a linear state estimator, then their accuracy will deteriorate due to the quadratic relationship between voltage and power. This paper employs machine learning to perform fast and accurate flow and injection estimation in power systems that are sparsely observed by PMUs. We train a deep neural network (DNN) to learn the mapping function between PMU measurements and power flows/injections. The relation between power flows and injections is incorporated into the DNN by adding a linear constraint to its loss function. The results obtained using the IEEE 118-bus system indicate that the proposed approach performs more accurate flow/injection estimation in severely unobservable power systems compared to other data-driven methods.

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arXiv2022

Monitoring and Detection of Low-current High-Impedance Faults in Distribution Networks

Faults in electricity distribution networks have the potential to ignite fires, cause electrocution, and damage the system itself. High current Low Impedance Faults (LIF) are typically detected and mitigated via over-current, distance, directional relays, fuses, etc. In contrast, while High Impedance Faults (HIF) are equally hazardous, they are much more challenging to detect due to the fault current being much lower than load currents and their time-varying and nonlinear behaviour. Moreover, New Zealand distribution networks are extensive and largely unmonitored beyond the substation, and suitable HIF detection schemes are still an ongoing research challenge. To date, we have built a physical test facility for power system fault analysis and developing and evaluating our sensing and fault detection system. We have simulated LIF and HIF with different fault surface materials and load-switching events. From the data collected, we have characterized the unique fault behaviour for both LIF and HIF in 400V networks and trained a Deep Learning classifier to recognize the type of fault present from its unique signature. We have developed an outdoor pole mountable sensing system and have installed this in Wellington Electricity's network for ongoing data collection and evaluation. This paper will describe the test facility and our experience developing and implementing the sensing system. The widest range of HIF phenomena observed was in the fault experiments involving the tree branch. For brevity, therefore, this paper reports on the results of just these tree-branch experiments. HIF faults on other surface materials will be reported elsewhere. Finally, we will detail the pole-mountable sensing system installed in Wellington Electricity's network and the outcomes thus far.

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