IRANIAN JOURNAL OF ELECTRICAL AND ELECTRONIC ENGINEERING

Enhancing Network Efficiency through Dynamic Reconfiguration with Consideration of Renewable Energy Distributed Generation in Real-Time Operation

Ola Badran

Dynamic Network Reconfiguration (DNR) is a vital and effective technique for reducing energy loss. Due to its complexity, nonlinearity, and large-scale optimization challenge, DNR is still a very difficult problem. This paper presents a new strategy for improving the DNR's stability and reliability under Real-Time Operation Mode (RTOM). It addresses a simultaneous optimization technique within various limitations and constraints about network power flow, voltage limits, output generation of Renewable Energy Resources (RER), Distributed generation mode, and network load profile. In real-time operating mode, it optimizes Distributed Generations Sizing and Location (DG_SL) for Renewable Energy and Dynamic Network Reconfiguration (DNR). Reducing the overall daily active and reactive energy losses of the network, raising the Voltage Stability Index (VSI), distributing the load more evenly, and enhancing distribution efficiency in real-time operation mode are the primary goals. A Multi-Objective Decision-Making Approach (MODMA) based on the Analytic-Hierarchy Process (AHP) and Crow Search Algorithm (CSA). To evaluate the practicality of the proposed method, MATLAB simulations were conducted on the IEEE 33- and 69-bus networks. In the IEEE 33-bus case, the proposed AHP–CSA framework achieves up to 91.75% reduction in daily active losses and more than 90.70% reduction in daily reactive losses, with the Voltage Stability Index consistently improved toward unity. In the IEEE 69-bus case, the method delivers up to 81.78% reduction in daily active losses and 59.78% reduction in daily reactive losses, also enhancing the overall voltage stability profile. These outcomes confirm the effectiveness and robustness of the proposed approach for real-time distribution network operation with renewable DG integration.

Design and Simulation of Synchronous Reluctance Motor in IE4 Efficiency Class

mansour rafiee

The present study aims to design, analyze, and simulate the synchronous reluctance motor (SynRM) based on the IEC90L frame and IE4 efficiency class. Initially, the permissible losses are calculated for the SynRM considering the given efficiency class. The SynRM is then designed using the calculated losses to generate the highest possible output power. In order to achieve optimal performance in terms of output power and power factor (PF), a parametric per-unit system is utilized for SynRM analysis, and the dimensions of various parts of the motor are determined based on design inputs (copper losses and magnetic loading). Subsequently, given this parametric model and the changing range of per-unit parameters, the characteristics of the available motors are thoroughly monitored with respect to output parameters, and the motor model is selected. To validate the analytical model, the finite element analysis (FEA) is conducted for the selected model, and the simulation results are compared with those of the analysis method and design inputs. Ultimately, to enhance overall motor performance, an optimization process was conducted, followed by a comprehensive evaluation of the optimized model to assess efficiency and torque improvements.

Comparative Impact Analysis of Tap Winding Order and Configuration on Transformer Short-Circuit Inductance

Mojtaba Mirsalim

In contemporary power systems, it is crucial to ensure stable voltage levels to mitigate the fluctuations resulting from diverse load conditions. On-load tap changers (OLTCs) play a pivotal role in addressing these fluctuations by dynamically adjusting the number of turns in the transformer winding. This study investigates the integration of OLTCs within transformer designs, focusing on various methodologies related to tap winding order and configurations, which are vital for both electrical and magnetic performance. A comprehensive review of the operational principles governing different types of OLTCs is provided, highlighting their significance in voltage regulation. Furthermore, this paper analyzes the impact of linear OLTC winding order on the short-circuit impedance of a 30 MVA transformer. The findings underscore the importance of OLTC selection and design in optimizing transformer performance.

Deep learning for Robust EEG Signal Forecasting using Long Short Term Memory Neural Network

Zaineb M. Alhakeem

Signal forecasting in the medical field has many applications, such as signal correction and anomaly detection. According to this application, robust forecasting is required to obtain a signal identical to the original signal. This study proposes a forecasting technique that obtains a robust signal that can be used in different applications. A long short-term memory neural network (LSTM-NN) was used to predict future samples from present and past samples. An Electroencephalography (EEG) dataset was used to test this technique. Four channels were used as input examples, one of which was the predicted output. All four channel samples were fed into the four networks to predict the future samples. To decrease complexity, only one hidden layer is used for this purpose. The statistical results are promising for applications that require an almost perfectly predicted signal. The number of hidden cells is first very low (five cells only), which gives a Root Mean Square Error of less than 20, whereas when the number of hidden cells is increased to 100, the Root Mean Square Error (RMSE) is approximately 7.5 for all four channels.

Power allocation in NOMA with decoding order error of successive interference cancellation

Mina Baghani

In this paper, the decoding order error of successive interference cancellation (SIC) of multicarrier nonorthogonal multiple access (NOMA) due to the random walk of the users and position estimation deviation is considered in resource allocation. This factor extremely degrades the performance of NOMA in terms of sum rate and outage probability. Therefore, two optimal power allocation strategies for users are derived that maximize the sum rate and minimize the outage probability. The simulation results show that by considering the decoding order error in resource allocation, better performance can be achieved compared to the previous power allocation algorithms without considering this fact, which are a well-known water filling algorithm and a power allocation that maximizes the rate with minimum rate constraint.

A Combination of Adaptive Neuro-Fuzzy Inference System and Neural Network for Mobile Robot Dynamic Obstacle Avoidance

Zead Mohammed Yosif

A mobile robot must be autonomous to avoid obstacles while traveling towards the target. Dynamic obstacle avoidance remains a significant challenge in mobile robotics. Although reactive navigation strategies have been applied to address this problem, relying on the single-stage module often results in limited efficiency and restricted overall performance. This paper proposes combining an adaptive neuro-fuzzy inference system (ANFIS) and a neural network (NN). The data for obstacle severity classification were used to train the Neural Network. The relative velocity and distance between the mobile robot and obstacles determine the zone. Zone 1 is dangerous, and Zone 5 is safe. This paper uses the ANFIS to avoid obstacles during the mobile robot's motion and to avoid collisions. Based on our empirical study, three essential features have been considered in this paper: the relative speed, distance, and angle between the robot and the obstacle as inputs to the obstacle avoidance system ANFIS. The output was a suggested steering angle and speed for the mobile robot. The simulation results for the tested cases show the capability of the proposed controller to avoid static and dynamic obstacles in a fully known environment. Our results show that the ANFIS System enhances the proposed controller's performance, reducing path length, processing time, and the number of iterations compared to state-of-the-art research papers. The proposed work demonstrated better performance in path length reduction (approximately 6%) and time taken reduction to reach the target, which is reduced by about 60%.

Design and Analysis of a Minkowski Fractal Slot-Integrated UWB Antipodal Vivaldi Antenna for Brain Stroke Detection

Sumi M

This article presents the design and optimization of a Minkowski fractal slot-integrated antipodal Vivaldi antenna (MFS-AVA) for brain stroke detection. The antenna is proposed on a 65 × 65 × 1.6 mm³ FR-4 substrate and integrates a tapered slot radiator with a microstrip feed. Key parameters are optimized through parametric analysis. The exponential curve of the radiator arms and edge conductor is fine-tuned for improved bandwidth and impedance matching, while Minkowski fractal slots enhance the reflection coefficient, gain, and directivity. Simulated using CST Studio Suite 2016, the antenna attains an extensive bandwidth spanning from 1.23 GHz to 12 GHz, a maximum gain of 9 dBi, and a radiation efficiency of 87%. The radiation pattern exhibits a directional beam with minimal side lobes, making it suitable for focused microwave imaging. Compared to a conventional design, the MFS-AVA shows improved S11, VSWR, and surface current performance. Its effectiveness is validated using a four-layered tissue-mimicking cylindrical human head model, confirming adequate field penetration and compliance with safety standards. These results demonstrate the proposed antenna’s suitability for UWB microwave imaging in brain stroke detection.

Integrating GPS, GLONASS, BeiDou, and Galileo Receivers to Overcome Signal Blocking Challenges

M. R. Mosavi

Blocking interference poses significant challenges to the accuracy and reliability of navigation systems by obstructing the communication path. Single-frequency receivers are generally more susceptible to blocking interference due to their limited ability to compensate for obstructed signals or access alternative signal sources. The integration of Global Navigation Satellite Systems (GNSS) is among the most effective strategies for mitigating blocking interference. By combining signals from multiple sources, the likelihood of accessing stable and reliable signals significantly improves. The four Global GNSS include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou, and Galileo. This paper examines the challenges of system integration in addressing navigation equations and proposes suitable solutions. Two datasets were collected under conditions of blocking disturbances, and receiver performance was simulated across 14 different modes using a software platform. The results were analyzed considering factors such as the number of satellites in view, satellite positions, extracted positions, as well as Root Mean Square (RMS), Geometric Dilution of Precision (GDOP), and Position Dilution of Precision (PDOP) parameters. In these scenarios, the GPS system in single-frequency mode, the combination of GPS and GLONASS in dual mode, and the combination of GPS, GLONASS, and Galileo in triple mode demonstrated the best performance. However, the best performance, irrespective of computational load and hardware complexity, was achieved in the quadruple integration mode.

An Experimental Analysis of the Latency of Linux Kernels Applicable for Real-Time Control Strategies

Ayoub Khodaparast

Real-time control applications, crucial in robotics, industrial automation, and medical devices, demand precise and predictable timing for reliable operation. This paper presents an experimental investigation into the latency performance of various Linux kernels, including standard Linux, a low-latency kernel, Xenomai, and a real-time kernel patched with PREEMPT_RT. Our test setup utilizes a data acquisition card to measure the latency between sending and receiving a pulse signal through analog input-output channels, generated by a C++ code. This latency metric serves as an indicator of the responsiveness of the kernel and other control objects on a specific computer system. Our experiments were conducted under a wide range of conditions to comprehensively assess latency performance. This includes different versions of standard and real-time Linux kernels, varying numbers of CPU cores, program priority levels, data saving rates, a range of data acquisition cards, communication protocols, thread assignments to processor cores, and test durations. The results highlight the importance of long-term testing to accurately determine the maximum latency. Furthermore, the findings demonstrate significantly lower latency for the PREEMPT_RT patched kernel across various tests, indicating its suitability for demanding real-time control applications that require tight timing constraints.

Physics-Informed Neural Network-Assisted Compact Modeling of UTB-SOI and Nanowire MOSFETs for Ultra-Low Power Edge-AI Applications

Balamanikandan A

Physics-informed neural networks (PINNs) offer a promising route to bridge device-level simulations and compact circuit models. In this work, we present a hybrid modeling framework that integrates TCAD datasets with a baseline compact model and applies a PINN correction to capture stress-condition effects with high fidelity. The proposed approach achieves ≤ 2% route mean square error (RMSE) across more than 2,000 bias points, maintaining stable predictions under temperature (273–373 K) and radiation (0–100 krad) variations. Extracted Berkeley Short-channel IGFET Model (BSIM) parameters enable direct SPICE simulation, ensuring compatibility with standard circuit design workflows. For deployment, the trained PINN is exported as a quantized ONNX model, achieving sub-millisecond inference and ultra-low energy consumption (0.25 pJ/op) on a Cortex-M55 platform. This dual pathway supports both high-accuracy circuit simulation and real-time edge inference, making it suitable for embedded applications under constrained conditions. Comparative analysis with recent ANN-based models confirms that our physics-informed approach offers superior interpretability, SPICE readiness, and deployment efficiency. All datasets, code, and models are released to support reproducibility, benchmarking, and further research in compact modeling and edge-AI integration.

Contemplation of QCA based Cryptographic Nano Communication Circuit using Multilayer Approach

Saptarshi Gupta

CMOS technology, after contributing a lot to electronics world, is now facing difficulties in designing of more efficient circuits in terms of compactness, power efficiency and speed. It is happening due to various side effects being generated on account of further down scaling of feature size. The Quantum Dot Cellular Automata (QCA) technology seems to be alternate and promising technology for designing of more efficient circuits. The cryptographic encoder and decoder are the key component for secure and safe communication. This paper presents an efficient design of 1:2 demultiplexer, 1:4 demultiplexer and 4:1 multiplexer which are further used to design a cryptographic nano communication circuit. The proposed circuits are efficient in terms of energy, area and speed. The architectures are designed through multilayer approach in QCA technology that makes it compact. The efficiency of the proposed circuits has been verified through the tool QCA Designer 2.0.3.

Active distribution system state estimation based on metaheuristic algorithms in the presence of distributed generations

Mojtaba Ajoudani

The increasing penetration of distributed generation (DG) significantly complicates Distribution System State Estimation (DSSE) by introducing stochasticity and uncertainty. This paper proposes a novel DSSE framework that unlike conventional methods simultaneously estimates the system state, load demands, and DGs output power through a unified constrained optimization model. The model is efficiently solved using the Whale Optimization Algorithm (WOA), whose unique balance of exploration and exploitation enables robust solution search in complex, active distribution networks. Simulation studies on standard IEEE 37-bus and 69-bus test systems reveal that the proposed WOA-based approach achieves outstanding accuracy. For the 37-bus system, WOA attains a Maximum Individual Relative Error (MIRE) of 1.15% and a Maximum Individual Absolute Error (MIAE) of 2.303 on load estimation. On the larger 69-bus system, the method further reduces these errors yielding a MIRE of 0.886% and a MIAE of 1.12 for load, and 0.73% and 1.058 for DG power estimation, respectively. Across all experiments, WOA consistently outperforms leading metaheuristics including ABC, PSO, and GA highlighting its superior accuracy, scalability, and robustness for real-world DSSE challenges.