JAREE (Journal on Advanced Research in Electrical Engineering)

Techno-economic Analysis of Large-scale Integration of Variable Renewable Energy Sources into Myanmar's National Grid with Interconnection and Energy Storage System

2025-02-24T18:33:54+00:00

Myanmar’s abundant hydropower, solar, and wind resources present significant opportunities for a sustainable energy transition. Hydropower, with a potential capacity of 19,567 MW, currently forms the backbone of the energy strategy, though its seasonal variability necessitates improved transmission networks and planning. Solar energy, with an annual potential of 51,973.8 TWh, is increasingly utilized for both utility-scale and off-grid applications, while wind energy, with a theoretical potential of 80 TWh annually, requires further exploration. This study employs the EnergyPLAN modeling tool to analyze the techno-economic feasibility of integrating variable renewable energy sources (VRES) into Myanmar’s grid by 2030, targeting 11% of electricity generation from solar and wind. Five scenarios are examined, highlighting the role of grid flexibility solutions such as cross-border interconnections and energy storage system (ESS). Without flexibility measures, Critical Excess Electricity Production (CEEP) could reach 7.69 TWh annually. Introducing interconnections reduces CEEP to 0.57 TWh, while the combined deployment of interconnections and ESS further minimizes CEEP to 0.41 TWh and increases Exportable Excess Electricity Production (EEEP) to 7.29 TWh. These results underscore the importance of strategic investments in ESS and regional interconnections to address VRES variability, enhance grid stability, and reduce curtailment. Such measures are crucial for optimizing Myanmar’s renewable energy potential, advancing its energy transition, and reinforcing its position in the ASEAN regional energy market.

Improved Surge Speed Adaptive LOS Guidance Law in Current Disturbance Environment for USV

2025-07-03T02:10:01+00:00

This work proposes a method for calculate the desired surge velocity integrated with an Adaptive Line of Sight, called Improved Surge Speed Adaptive LOS (ISS-ALOS), to enhance the path-following performance of USVs. This method utilizes ALOS guiding law to enable the USV to maintain the desired path regardless of disturbances. The proposed ISS-ALOS uses the path's curvature, cross-track error, and cross-track error rate to calculate the desired surge velocity. The method is applied to USV following a curved path with several bends and a rectangle path with sharp turns, to demonstrate with high reliability that the proposed method can direct the USV along the specified path with minimum cross-track error. Comparison with the latest guidance law available in publication is also presented to show the superiority of the proposed method.

An Implementation of the Convolutional Neural Network Algorithm for Detecting Crack in 150 kV Transmission Line Insulator

2025-07-16T15:51:37+00:00

High Voltage Overhead Transmission Lines require routine inspections to detect potential fault and ensure reliable operation. These inspections are conducted through manual tower climbing, known as Climb Up Inspection (CUI). With the advancement of technology, drones—Unmanned Aerial Vehicles (UAVs)—have increasingly been adopted as a more efficient alternative for conducting such inspections. While drone-based inspections significantly improve the speed of data acquisition, they often create a bottleneck at the stage of data analysis and image processing due to limited human resources and concurrent operational tasks. To address this issue, this study presents the development of an Artificial Intelligence (AI) based software application utilizing Deep Learning, specifically the Convolutional Neural Network (CNN) algorithm, to automate the classification of insulator conditions captured by drone imagery. The proposed application is designed to categorize insulators into two conditions: broken and normal. By automating the analysis process, this system is expected to enhance inspection efficiency, reduce maintenance response time, and support the creation of a more reliable and resilient power transmission network.

A 3x3 Phased Array Antenna for Ground Station Application

2025-07-22T10:19:34+00:00

The utilization of satellite technology has undergone a rapid expansion, particularly in the domain of Low Earth Orbit (LEO) satellites. The necessity of antennas at ground stations for the purposes of mission control and data communication is of paramount importance. However, the implementation of antennas with large reflectors is hindered by several factors, including their high manufacturing cost, operational maintenance requirements, and inherent complexity. Consequently, a solution is required to employ microstrip antennas that are more compact, cost-effective, and capable of automation in tracking LEO satellites. In order to facilitate communication with LEO satellites, it is imperative that the designed antenna exhibit high gain value, narrow bandwidth, and effective scanning capability. The proposed research delineates a uniform rectangular array design with half-wavelength spacing as the fundamental configuration. The array employs a cavity-supported patch antenna and an X-slot-shaped deformed ground structure, a configuration designed to enhance bandwidth and generate circular polarization. The 3x3 array employed in this study exhibits circular polarization with a gain level of 10.08 dB and a beamwidth of 66.18° at a frequency of 2.22 GHz. The antenna has been engineered for implementation at ground stations within the domain of satellite technology.

Life Cycle Cost Analysis of PV-Wind-Battery Hybrid Power System at PLN Nusantara Power Brantas Generation Unit Tulungagung

2025-06-18T05:44:55+00:00

Battery Equalization Strategy Based On Cuk Converter Using Fuzzy-PI Control

2025-06-23T01:06:26+00:00

Inconsistency in series-connected batteries is one of the factors that affect the performance while reduce lifetime battery. To ensure that the cells in a series battery pack have the same energy charge rate and maximize the overall full capacity of the battery, a battery balancing component is required. However, conventional balancing techniques that still use voltage as the control variable have a weakness, when the difference or voltage gap between batteries is small, the balancing current provided is also small, as a result this condition affects the balancing speed. To overcome deficiencies, This research proposes the design of a Cuk converter-based active balancing circuit with a bidirectional energy transfer mechanism using State of Charge (SOC) as a control variable with a Fuzzy Logic Controller (FLC) - Proportional Integral (PI) strategy. The test simulation results show that compared to the conventional technique, the proposed balancing topology is able to shorten balancing time significantly, guarantee safe balancing operation, and a dynamic balancing current regulation mechanism that is adaptive to battery conditions.

IMPACT OF DYNAMIC PRICING WITH SMART METERS ON ENERGY CONSUMPTION AND GRID LOAD STABILITY IN ELECTRIC VEHICLE USAGE

2025-06-26T09:18:42+00:00

The rapid adoption of electric vehicles (EVs) in Indonesia presents both opportunities and challenges for the national electricity grid. This study analyzes the impact of dynamic electricity pricing, enabled by smart meter technology, on energy consumption patterns and grid load stability among EV users. Using a quantitative descriptive approach, data were collected from 100 EV users charging their vehicles at smart meter-equipped stations. The study examined variations in electricity consumption and costs across peak, off-peak, and super off-peak periods and analyzed changes in grid load before and after dynamic pricing implementation. Results show that while electricity consumption volumes remain statistically similar across different charging times, the total electricity cost varies significantly due to dynamic tariff differences. EV users who shifted charging to lower-tariff off-peak hours benefited from reduced costs, demonstrating economic incentives to adapt consumption behavior. Furthermore, dynamic pricing contributed to a 10% reduction in peak electricity load and a more even distribution of grid demand during nighttime hours, enhancing grid stability and operational efficiency. In conclusion, dynamic pricing combined with smart metering is an effective approach to managing the growing electricity demands from EV adoption, improving cost efficiency for consumers and promoting a more resilient and sustainable.

An An Active Balancing for Liquid Metal Battery Using Bidirectional Flyback Converter with Fuzzy Logic Control

2025-07-18T03:47:10+00:00

The use of renewable energy to replace fossil fuels in electricity generation systems has been increasing every year. Unlike fossil fuels, which can generate energy according to load demand, renewable energy sources produce power unpredictably. Therefore, batteries are needed for power shifting. Li-ion is the most commonly used battery, although it only has a life cycle of about 1500 - 4500 cycles. We need another type of battery as an alternative because if we only rely on Li-ion, it will cause battery waste, which is a problem for the environment. Liquid Metal Battery (LMB), according to the latest research, have a life cycle of up to 10,000 times, so they could eventually become a promising alternative. LMB is composed of modules with a combined configuration of parallel and series circuits and requires a management system, especially for the balancing process. Due to the large capacity of this battery type, bidirectional active balancing using a bidirectional flyback converter is an ideal alternative, as the process is fast and highly efficient. Fuzzy logic control can be used in this system because it does not require mathematical equations and is suitable for systems with multiple inputs and outputs.

OPTIMAL DESIGN OF BLDC MOTOR USING ABO (AFRICAN BUFFALO OPTIMIZATION) ALGORITHM TO MINIMIZE COGGING TORQUE

2025-07-23T02:23:32+00:00

The use of BLDC motors in recent years has shown rapid development. This is motivated by efforts to reduce pollution with renewable energy. On the other hand, the advantages of BLDC motors have their own appeal so that they are widely applied not only in the automotive, health but also in the fields of aerospace and household appliances. The BLDC motor design that uses permanent magnets makes it produce good characteristics in power distribution, torque, and efficiency. However, it is not uncommon for the BLDC motor design to also create ripples in torque caused by cogging torque, causing unsmooth torque distribution, vibration and noise. The stator design in BLDC motors is the main factor causing the high cogging torque that occurs. This is due to the difference in flux density in the air gap and the interaction of magnets with the stator surface. This research focuses on reducing cogging torque by optimizing these parameters using the African Buffalo Optimization (ABO) method. The research was conducted by modeling the phenomenon of cogging torque through mathematical calculations and optimization through MATLAB. The optimized parameters are then simulated with MOTOR-CAD software to be analyzed and evaluated.

The Governor Predictive Controlled Based on LSTM for Optimizing Cofiring Power Generator Operation

2025-06-30T04:20:31+00:00

The renewable energy with cofiring technology has a significant impact on the use of Biomass. The use of biomass with different qualities greatly affects the performance of a plant. Deep Learning Time Series Forecasting is designed for predicting two control parameters cofiring powerplant operation consist of governor control and output generator. Long Short-Term Memory (LSTM) combined with Multilayer Perceptron, Convolutional, and Adaptive Moment Estimation (ADAM) optimizer algorithms are utilized to optimize the process governor control and predict generating power output. Correlation analysis is used to determine the input variables and resulting input parameters of governor control prediction consist of Temperature Steam, Pressure Steam, Output Generator, Coal Flow, Flow Steam. Moreover, the input variable for prediction generation power output are steam flow, steam temperature, coal flow, and steam pressure. The combination of Deep Learning Forecasting is successfully to predict both operation parameter percentage errors of 5.33%.

Design and Simulation of a Micro-Inverter for Microgrid Applications Using a Recurrent Neural Network-Based Control Strategy

2025-06-30T03:32:37+00:00

This study presents the design and simulation of a micro-inverter system for photovoltaic (PV) energy conversion in microgrid applications using a Recurrent Neural Network (RNN)-based Maximum Power Point Tracking (MPPT) control strategy. The objective is to enhance power conversion efficiency and system responsiveness under dynamic solar conditions. The proposed system integrates a boost converter, micro-inverter, LCL filter, and Phase Locked Loop (PLL) for grid synchronization. The RNN model is trained using variations of irradiance and PV electrical characteristics to determine the optimal duty cycle for controlling the converter. System modeling and simulations were conducted in MATLAB to assess performance. Simulation results demonstrate that the RNN-MPPT algorithm significantly improves the system’s ability to track the maximum power point accurately and rapidly, with improved output power stability under fluctuating irradiation. The boost converter and inverter responses confirm effective voltage regulation and reduced current ripple, while the overall system achieved high efficiency across different irradiance levels. These findings highlight the potential of AI-based control techniques in advancing renewable energy technologies and contribute to the development of intelligent energy management systems suitable for smart grid integration.

A Computer Vision Approach for Non-contact Psychophysiological Assessment: Speaking-Aware Video-Based Stress Detection Using Extended TSST Protocol

2025-07-16T15:22:56+00:00

The assessment of psychological stress through non-contact computer vision methods faces significant challenges when facial expressions are affected by motions caused by speech. In this paper, we introduce a novel video-based stress detection system with speaking awareness that dynamically processes facial features according to online detection of speech activities. The system employs an extended 41-minute Trier Social Stress Test protocol with a controlled baseline, moderate stress induction, peak stress increase, and recovery stages to comprehensively record the dynamics of stress development. Facial landmark detection is handled by MediaPipe Face Mesh and provides 468 three-dimensional landmarks with sub-pixel accuracy, while the speaking-aware processing strategy dynamically selects the most appropriate feature sets: seven robust ones during speech-containing intervals like face movement, eye aspect ratio, and forehead wrinkles, and nine full-featured ones during silent intervals with additional mouth and jaw metrics. The adaptive strategy addresses the intrinsic limitation of traditional facial analysis, which treats speaking and non-speaking states homogeneously. Deployment on the Raspberry Pi CM4 edge computing device enables real-time operation with privacy preservation via localized processing. Empirical testing with 71 participants illustrates strong performance with an F1-score of 83.16%, outperforming conventional methods by 4.24%. Cross-population validation affirms good generalization potential across various populations for the entire 41-minute protocol, thereby demonstrating the system's applicability to real-world applications in stress tracking across healthcare, educational, and workplace well-being contexts.