Research

Description:
High photovoltaic (PV) penetration in low-voltage (LV) distribution networks can cause overvoltage during high generation, low-demand periods, requiring active power curtail ments. Fairness-aware curtailment methods aim to distribute the curtailment burden equitably among prosumers. However, practical networks may include heterogeneous prosumer classes where a commercial prosumer receives preferential treatment under contractual obligation. This paper investigates how the feeder location of a priority PV prosumer affects the curtailment burden on residential users. A modified CIGRE LV network is used where along with the 16 residential PV users, a commercial PV unit of 63.25 kWp is connected at each of the PV-capable buses. Solving a day-ahead linear programming (LP) formulation with priority weighted curtailment ratio fairness under high PV, low-load scenario under six commercial priority weights show that for most buses stronger commercial priority reduces commercial curtailment while increasing residential curtailment. Results also show that, as the commercial prosumer is placed at a more remote location, the burden of priority-weighted fairness on the residential prosumers increases, exhibiting crucial topology dependence, exceptions being buses 16 and 18 due to their lateral placement and local voltage sensitivity. The results indicate that priority-based curtailment is strongly topology-dependent and must be evaluated using network-aware voltage constraints.  

First Author
Accepted in: 2026 IEEE INTERNATIONAL CONFERENCE ON POWER AND ENERGY (PECON 2026)

Supervisor: Dr. Md. Ziaur Rahman Khan, Professor, Dept. of EEE, BUET

Written as an extension of Postgraduate Thesis

Description:
The rapid growth of artificial intelligence workloads creates large and rapidly varying power fluctuations on distribution feeders, creating voltage violations which cannot be suppressed reliably by battery energy storage system alone, especially when storage headroom is limited. This paper proposes a cascaded primal-dual online feedback optimization controller which jointly dispatches a grid connected battery storage and GPU batch-size schedules for real-time voltage regulation. A cascade priority function, created from logistic state-of-charge indicators, continuously transfers regulatory authority from the battery to the compute side as storage approaches its operational limits. The proposed controller, therefore, replaces abrupt switches with a smooth transition between actuators. On the IEEE 13 bus distribution test feeder under an 1800 second heterogeneous workload, the proposed controller reduces mean voltage deviation by 76.3% against the uncontrolled baseline and by 23.6% against battery-only online feedback optimization with identical hardware. It also cuts the mean inter-token latency by 38.9% relative to the uncontrolled baseline, while retaining 77% of aggregate throughput. Monte Carlo uncertainty trials confirm voltage deviation variance to be 1.4 times tighter than battery-only control.  

First and Only Author
Under Review: IEEE Transactions on Smart Grid

Description:
This study presents the design and implementation of the BUET Low-Cost Accelerometer (BLCA), a compact earthquake monitoring module developed specifically for dense seismic networks in developing countries. Utilizing the high-performance ADXL355 MEMS sensor, the device employs a computationally efficient Peak Ground Acceleration (PGA) thresholding algorithm that enables rapid, on-site earthquake detection and improves warning lead times. Shake table experiments validated the module's accuracy, showing a 95% to 99% correlation between input and measured responses, while simulations of the Chi-Chi earthquake demonstrated a potential early warning lead time of approximately 7.4 seconds. Additionally, the system's low power consumption allows it to operate autonomously for several hours during power outages, providing a robust and affordable solution for continuous seismic monitoring and alert transmission.

Co-author
Submitted in: IEEE Transactions on Instrumentation and Measurement (Currently Under Review)

Supervisor: Dr. Zunaid Baten, Professor, Dept. of EEE, BUET and Dr. Tahmeed Malik Al-Hussaini, Dept. of CE, BUET

Funded by: RISE, BUET

Collaboration of Dept. of Civil Engineering and Dept. of EEE of BUET

Description:
This project introduces a deep learning framework designed for month-ahead electricity price forecasting, a critical but often overlooked area in deregulated power systems like ISO New England (ISO-NE). By combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, the proposed model effectively captures complex trends, residuals, and seasonal patterns in historical market data to predict the final locational marginal price (LMP). The simulation results indicate high accuracy, with forecasted prices closely matching real-world datasets, offering market participants a reliable tool for generation scheduling, bidding strategies, and risk management.

Supervised by: Dr. Sheikh Anowarul Fattah, Professor, Dept. of EEE, BUET

Part of Machine Learning and Pattern Recognition Course of M.Sc. 

Description:
This project details the development of an autonomous firefighting and rescue robot designed to serve as a first line of defense in high-rise buildings and industrial settings in Bangladesh. Utilizing a combination of IR and MQ2 gas sensors, the robot detects fire and smoke, moves toward the source, and mitigates the hazard using an onboard water tank and pump. A distinctive feature of this prototype is its Bangla voice recognition capability, which allows it to identify a victim's call for help and send a notification with location details to emergency services via a SIM 900a GSM module. Built with an Arduino UNO and powered by rechargeable lithium-ion batteries—designed for solar charging to align with Sustainable Development Goals (SDG)—the project offers a scalable, budget-friendly solution for improving fire response times and victim rescue efforts 

Supervised by: Dr. Celia Shehnaz, Professor, Dept. of EEE, BUET

Based on Control Systems Laboratory Project in Undergraduate 

Description:
This project details the development of a Bangla voice-based attendance system designed to replace manual, proxy-prone methods at BUET with a seamless biometric solution. Utilizing MATLAB, the system employs a subspace K-Nearest Neighbors (KNN) classifier and Mahalanobis distance to identify students based on unique vocal characteristics extracted via Mel Frequency Cepstral Coefficients (MFCC) and pitch. The researchers compiled a robust database of 4,740 voice samples from 237 individuals, implementing a 5th-order low-pass filter to mitigate classroom noise. Experimental results demonstrated high effectiveness, achieving a final individual detection accuracy of 95.62% for IDs and 95.26% for names, significantly outperforming previous models. Future implementation plans involve a central database and a dedicated mobile application, with potential upgrades to deep learning frameworks for enhanced precision. 

Supervised by: Dr. Celia Shehnaz, Professor, Dept. of EEE, BUET

Based on Digital Signal Processing Laboratory Project in Undergraduate