Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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Optimizing Low-Voltage Ride-Through in DFIG Wind Turbines via QPQC-Based Predictive Control for Grid Compliance
Full text linkThis paper introduces a novel Model Predictive Control (MPC)-based strategy to enhance Low-Voltage Ride-Through (LVRT) capability for wind turbines equipped with Doubly Fed Induction Generators (DFIGs). According to modern grid codes, grid-connected wind turbines must remain operational during voltage dips and support the grid by injecting both active and reactive power. However, voltage dips pose significant challenges for (DFIG)-based wind turbines because voltage dips can induce significant large inrush current in the rotor, potentially damaging the rotor converter. Conventional control methods employ proportional-integral (PI) controllers for rotor current regulation and crowbar circuits to protect the converter by diverting high rotor currents away from the converter when they exceed their safe limit. While effective in protecting the hardware, crowbar activation temporarily disconnects the rotor from control, leading to a loss of power injection capabilities and noncompliance with grid codes. To overcome these limitations, this paper proposes an MPC-based rotor current controller formulated as a Quadratically-Constrained Quadratic Programming (QCQP) optimization problem. This controller explicitly incorporates rotor current and voltage constraints while optimizing control performance during grid faults. MATLAB-based simulations for both low- and medium-voltage dips demonstrate the superiority of the proposed approach over conventional PI controllers. The results confirm that the MPC strategy ensures LVRT compliance without the need for a crowbar circuit, maintaining stability and improving performance during a wide-range of fault conditions
Implementing PID-Kalman Algorithm to Reduce Noise in DC Motor Rotational Speed Control
Full text linkThis research attempts to combine Proportional Integral Derivative (PID) control and Kalman filter as a noise filter for encoder sensor readings and reference tracking accelerator of JGA25-370 DC motor. Through experiments, the applied PID controller demonstrated its ability to maintain the stability of DC motor rotation under different load conditions. The control signal generated by the motor driver had different voltage outputs: 7.8V for PWM 125, 8.4V for PWM 150, 8.8V for PWM 175, 9.1V for PWM 200, 9.4V for PWM 225, and 9.6V for PWM 250, with an encoder constant multiplier of 1.71. In particular, the Kalman filter, whose parameter values of R = 0.1 and Q = 0.01, effectively reduced the noise of the JGA25-370 DC motor encoder sensor readings. When operating independently, the PID controller successfully optimized the motor control using Kp = 1, Ki = 0.5, and Kd = 0.01. However, superior results were achieved by integrating the Kalman filter (R = 0.1, Q = 0.01) with the PID controller (Kp = 1, Ki = 0.4, Kd = 0.1), with successful reference tracking within a rise time value of 1.037 seconds, a completion time of 2.093 seconds, and a surpassing of 1.073%. These findings formed an efficient methodology for reducing encoder sensor reading results and speeding up the DC motor in achieving reference values using a combined PID-Kalman approach
Synergetic Control-Based Sea Lion Optimization Approach for Position Tracking Control of Ball and Beam System
Full text linkOne of the most difficult systems to control is the ball and beam (BnB) system due to its under-actuation, instability, and nonlinearity. To address these challenges, this paper presents an application of using the nonlinear synergetic control (SC) algorithm for position tracking control of the BnB system. A swarm optimization method based on sea lion optimization (SLO) has also been used to achieve an optimum dynamic performance by adjusting the suggested controller’s parameter. The Integral Time of Absolute Errors (ITAE) is employed by the SLO as an objective function to adjust the design parameters of the suggested SC. Using MATLAB software, a comparison has been made between the SC controller and the classical state feedback controller (SFC) to test the effectiveness of the suggested control algorithm. The findings illustrate that the suggested SC offers better transient response in terms of reducing the settling time and the overshoot than SFC. The effect of the external disturbance has also been examined. It has been found that SC provides more robustness performance than SFC
Photovoltaic Model Parameters Estimation Via the Fully Informed Search Algorithm
Full text linkEffective parameter estimation for photovoltaic (PV) systems holds significant importance for both researchers and industry professionals. An accurate understanding of PV models, achieved through modeling and simulation, plays a pivotal role in optimizing the design, control, testing, and forecasting of PV system performance. Developing a precise and robust parameter identification method significantly contributes to enhancing the modeling, control, and optimization of photovoltaic systems. In this context, our research contribution introduces a novel version of Rao metaheuristic algorithm named the Fully Informed Search Algorithm (FISA). Which demonstrate acceptable performance to solving optimization problems in several applied fields. While, maintaining the simplicity and non-parametric nature of the original algorithm. The proposed algorithm holds promise for various industrial applications, particularly in optimizing complex systems such as photovoltaic (PV) systems. For which, we used it to efficiently identifying the parameters of the single-diode model (SDM). Thus, we demonstrate its effectiveness through the application in two distinct case studies within our simulation research. in the end, we compared the results of FISA algorithm to seven other well-known algorithms, the obtained results indicate the superiority of the proposed algorithm in term of the stability of the system, a faster convergence and higher accuracy
Corrosion Prediction in the Oil Industry Using Deep Learning Techniques
Full text linkCorrosion presents a significant challenge in the oil industry, causing both immediate and long-term damage. Effective early prediction and monitoring of corrosion are crucial to mitigating economic losses and environmental impacts. However, traditional methods for predicting and detecting corrosion are often time-consuming and inefficient. This study leverages convolutional neural networks (CNNs) within a deep learning framework to develop two automated detection models for internal and external corrosion. These models can extract hierarchical features directly from raw pixel data, enhancing prediction accuracy and efficiency. Our dataset, provided by the Iraqi Oil Company, includes drone-captured images (162 photos: 91 depicting corrosion and 71 showing no signs of corrosion) and ultrasonic sensor readings (250 rows of oil pipeline thickness measurements). We assess the performance of our CNN models using metrics such as accuracy, precision, recall, and F-score, and we perform regression analysis to evaluate prediction errors. This research introduces two innovative systems: a 2D CNN for classifying the presence or absence of external corrosion, and a 1D CNN for assessing internal corrosion levels, identifying areas with the highest corrosion rates, and estimating the remaining operational lifespan based on these rates. Additionally, we develop a user-friendly interface for these systems. Comparative analysis demonstrates the superior efficiency of our proposed approach over traditional and alternative methods. Our findings advance the understanding of artificial intelligence applications in corrosion prediction, offering robust models to prevent unexpected corrosion failures. Future work will explore the integration of additional factors, such as humidity and temperature sensors, to further enhance the system's accuracy and reliability
Enhancing Hybrid Power System Performance with GWO-Tuned Fuzzy-PID Controllers: A Comparative Study
Full text linkThis study explores the implementation of a novel control strategy within hybrid power systems, leveraging a Grey Wolf Optimization (GWO)-tuned Fuzzy Proportional-Integral-Derivative (Fuzzy-P.I.D.) controller to enhance the integration of renewable energy sources. By addressing the critical challenge of grid frequency deviations, this approach significantly bolsters the stability and efficiency of power flow, ensuring a more reliable electricity supply. Employing MATLAB simulations, the research underscores the superior performance of the GWO-tuned Fuzzy-P.I.D. controller, which necessitates fewer control interventions and yields lower oscillation frequencies than its conventional P.I.D. and Fuzzy-P.I.D. counterparts. The robustness of this optimized controller is further validated through extensive tests, demonstrating its resilience across a spectrum of parameter adjustments and operational scenarios, including the hypothetical removal of system components. The findings reveal that this advanced control method markedly surpasses traditional solutions in maintaining stable electricity flow and enhancing the system's overall resilience and adaptability to the variable nature of renewable energy. Thus, the GWO-tuned Fuzzy-P.I.D. controller emerges as a significant innovation in hybrid power system management, heralding a new era of optimization and efficiency in renewable energy integration
Dynamic Performance Evaluation of a Brushless AC Motor Drive Using Different Sensorless Schemes
Full text linkThe presented study concerns with evaluating the dynamic performance of an isotropic sinusoidal brushless motor drive while utilizing different sensorless schemes. Three estimation algorithms are considered: the first depends on extracting the speed and position via comparing two values of motor's voltage in two co-ordinate systems; the second extracts the speed and position signal via comparing two different values of motor's current defined in two co-ordinates; while the third depends on estimating the motor's flux and use it to get the speed and position. The vector control is adopted to manage the drive dynamics. The detailed mathematical derivations for all system components are presented to facilitate the performance analysis. The theoretical base of each sensorless scheme is also described in detail. The target of the provided comparative analysis is to outline the weakness and strength points of each adopted sensorless schemes while estimating the speed and rotor position for a wide operating speed range. The judgment is measured in terms of the speed and rotor position estimation errors and the dynamic response as well. The performance evaluation process is carried out using MATLAB/Simulink software in which all system parts are simulated using their mathematical models. The findings from the study state that when it comes to dynamic speed behaviour, the voltage-based sensorless technique dominates, while the current-based sensorless approach gives stability in speed estimate priority. Alternatively, the third adopted sensorless scheme offers an acceptable high-speed performance and respectable performance at lower speeds. Statistically, it is found that the voltage-based estimation technique gives respectively lower speed and position estimation errors with percentages of 35% and 10% lower than their values under the current-based estimation technique, and with percentages of 35% and 30% lower than their values under the third adopted scheme
Enhanced data augmentation for predicting consumer churn rate with monetization and retention strategies: a pilot study
Full text linkCustomer retention and monetization have since been the pillar of many successful firms and businesses as keeping an old customer is far more economical than gaining a new one – which, in turn, reduce customer churn rate. Previous studies have focused on the use of single heuristics as well as provisioned no retention strategy. To curb this, our study posits the use of the recen-cy-frequency-monetization framework as strategy for customer retention and monetization impacts. With dataset retrieved from Kaggle, and partitioned into train and test dataset/folds to ease model construction and training. Study adopt a tree-based Random Forest ensemble with synthetic minority oversampling technique edited nearest neighbor (SMOTEEN). Various benchmark models were trained to asssess how well each performs against our proposed ensemble. The application was tested using an application programming interface Flask and integrated using streamlit into a device. Our RF-ensemble resulted in a 0.9902 accuracy prior to applying SMOTEENN; while, LR, KNN, Naïve Bayes and SVM yielded an accuracy of 0.9219, 0.9435, 0.9508 and 0.9008 respectively. With SMOTEENN applied, our ensemble had an accuracy of 0.9919; while LR, KNN, Naïve Bayes, and SVM yielded an accuracy of 0.9805, 0.921, 0.9125, and 0.8145 respectively. RF has shown it can be implemented with SMOTEENN to yield enhanced prediction for customer churn prediction using Pytho
Analysis of horizontal milling machine vibration on the influence of gear module cutters with sizes M1 and M1.5
Full text linkThis study examines the effect of vibrations on the horizontal milling machine type 1216 during gear manufacturing using cutter modules with diameters of 50 mm and 55.25 mm, each at a cutting depth of 1 mm. Displacement, velocity, and acceleration measurements were conducted in vertical, horizontal, and axial directions using a VM-6370 vibration meter, with the average vibration amplitudes analyzed. The results revealed that the 55.25 mm cutter produced the highest vibration amplitude in the horizontal direction, reaching 353.270 mm/s², while the lowest was in the vertical direction at 171.293 mm/s². For the 50 mm cutter, the highest amplitude occurred in the vertical direction at 0.1336 mm and the lowest in the horizontal direction at 0.0583 mm. These findings demonstrate that larger cutter modules generate higher vibration amplitudes, significantly affecting the precision and surface quality of gear manufacturing. The study emphasizes the importance of selecting appropriate cutter sizes to minimize vibrations, optimize manufacturing processes, and improve product quality. By providing a detailed analysis of the relationship between cutter size and vibration levels, this research is a valuable reference for enhancing the efficiency and accuracy of gear cutting in industrial applications
UAV Logistics Pattern Language for Rural Areas
Full text linkThe logistical challenges in rural areas, which often face limited infrastructure, varied terrains, and dispersed populations, often lead to inefficient and costly delivery systems. Recent developments in Unmanned Aerial Vehicle (UAV) technology offer a theoretical framework for overcoming these challenges. This research proposes a comprehensive pattern language specifically designed for multi-UAV logistics operations in rural settings. The proposed system integrates critical components such as LiDAR-based map generation, altitude information storage, partial goal estimation, and collision avoidance into a unified framework. Unlike existing research that typically focuses on isolated aspects like route optimization or payload management, this system features an advanced path planning algorithm capable of real-time environmental assessment and direction-aware navigation. Focus group discussions with logistics experts from Talaud Island, North Sulawesi, Indonesia informed the design and refinement of the proposed patterns, ensuring that they address the practical needs of rural logistics. Our analysis suggests that this system offers a theoretical foundation for significantly improving the efficiency, reliability, and sustainability of delivering essential goods and services to rural areas, thereby supporting equitable development and improving the quality of life in these communities. While no empirical data is presented, the framework serves as a scalable foundation for future implementations of UAV-based rural logistics systems