Browsing by Author "Ozdemir, A."

Now showing 1 - 3 of 3

Citation - Scopus: 3
Energy Loss Minimization with Parallel Implementation of Marine Predators Algorithm
(Institute of Electrical and Electronics Engineers Inc., 2021) Younesi, S.; Ahmadi, B.; Ceylan, O.; Ozdemir, A.
Distribution network (DN) service continuity is one of the significant issues in today's power systems. This paper aims to put a strategy for supplying loads with less discontinuity and affordable energy-consuming. The energy loss in distribution grids causes many problems for the producer and consumer; hence, it needs to be improved to increase supply efficiency accordingly. For this aim a model aiming to minimize power losses by allocating and sizing distributed generators (DGs) is solved using recently developed Marine Predators Algorithm (MPA). Since the proposed method is a time-intensive process due to the vast computations, parallel computation is implemented into MPA to increase computation speed. The proposed formulation and parallel computation are tested for 69-bus radial distribution system. The results are discussed in terms of computational accuracy and solution efficiency. Moreover, the convergence characteristics of MPA are compared with some other heuristic methods. © 2021 Chamber of Turkish Electrical Engineers.
Experimental Data Analysis of Positive Streamer-Leader Dynamics in Long Air Gaps Under Slow Front Impulse Voltages Using Machine Learning
(Institute of Electrical and Electronics Engineers INC., 2025) Dilawaiz, S.; Shah, W.A.; Ozdemir, A.
Knowledge of the electrical discharge characteristics under various voltage conditions is crucial to designing safer and more efficient high-voltage insulation systems. This study presents positive streamer-leader dynamics in the 10-meter rod-plane air gap under slow front positive impulse voltage having a rise time of 1000 microseconds. The realization aims to improve the knowledge of long-gap discharge behavior, which is one of the key aspects in insulation design under high-voltage engineering. The voltage and the current waveforms obtained during experiments were analyzed using a machine-learning-based polynomial regression approach. Besides such analysis, image processing was applied to high-speed camera footage to determine arc lengths for different breakdown stages. Down-sampling was applied to cope with raw data, and the regression models were evaluated in terms of mean squared error (MSE) and R-squared values. The Polynomial regression analysis showed high accuracy in terms of MSE and R-squared values. The image-based analysis demonstrated that a final jump length of nearly 10 m substantiates full leader development to the plane electrode. The results indicate that machine learning and image analysis can accurately model and quantify discharge development in long air gaps. © 2025 IEEE.
A Novel Contingency Analysis Toolbox Developed by the Python API of Powerfactory
(Institute of Electrical and Electronics Engineers INC., 2025) Ebadi, R.; Tosun, G.; Albostan, A.; Ozdemir, A.
An essential aspect of network expansion planning is conducting contingency analysis to evaluate critical system parameters and reliability indices. While various commercial tools exist for network studies, they often lack flexibility in selecting inputs, outputs, and specific operational strategies for power networks. This paper introduces a toolbox developed using the Python API of PowerFactory for N-1 contingency analysis, offering extensive flexibility not provided by the existing commercial tools. The other important feature of the toolbox is the ability to optimize network reconfiguration and power restoration. The toolbox's performance is tested on a real-world bulk distribution network. The results showed the flexibility in generating diverse outputs, the ability to consider different operational strategies while confronting contingencies, and time and resource-saving due to task automation. © 2025 IEEE.