2DOF-PID-TD: A new hybrid control approach of load frequency control in an interconnected thermal-hydro power system.

The Load Frequency Control (LFC) scheme, with its primary aim being the maintenance of uniform frequency, has been a heavily researched topic for decades. Achieving a consistent frequency necessitates a delicate balance between load demand and power generation. Researchers strive to find an optimal solution within the LFC domain-one that can effectively withstand drastic load fluctuations. Despite a plethora of efforts, the LFC dilemma remains unresolved, complicated by factors such as dwindling demand-supply and the rapid integration of renewables. Furthermore, the lack of innovation in controller structure design exacerbates the complexity of solving modern LFC problems. Consequently, a robust control approach capable of handling uncertainties while simultaneously regulating system frequency becomes crucial. In light of this, we propose a novel hybrid control architecture called 2DOF-PID-TD. This architecture combines Two Degrees Of Freedom Proportional-Integral-Derivative (2DOF-PID) and Tilt-Derivative (TD) controllers. To optimize the proposed controller, we employ a metaheuristic called the Artificial Gorilla Troops Optimizer (AGTO), which mimics the social behavior and intelligence of gorilla troops. The proposed approach is analyzed in a realistic multi-area multi-source hydro-thermal system, accounting for nonlinearities, random load perturbations, and system parametric uncertainties. Experimental results, when compared with current state-of-the-art optimization algorithms and traditional controller structures, demonstrate the prowess of our approach in terms of precision, robustness, and resilience.

Power quality improvement of a proposed grid-connected hybrid system by load flow analysis using static var compensator.

Renewable resources are most effective for sustainable development of society and economically efficient for small-scale power generation. However, grid integration is challenging because of the randomness of the source effects on power system parameters. This work proposes power quality enhancement by incorporating Static VAR Compensator (SVC) in a grid-integrated renewable hybrid power system. SVC is one of the shunt type Flexible AC Transmission Systems (FACTS) devices that is adopted in this system for the compensation of reactive power requirement. The proposed hybrid system for the Rohingya Refugee camp is energized by a wind and solar based sources. The objective is to enhance the overall bus voltage profile by minimizing both real and reactive power losses as well as boost the power transmission capability of the entire system. Different case studies have been considered by changing the source availability and generation supply for load flow analysis using ETAP software. Moreover, critical system parameters such as bus voltage, power transfer capacity, and power losses have been reported during the inactive time of one or both renewable sources. The results obtained without SVC have been compared against the ones with the presence of SVC. Our analysis reveals that, as a result of using SVC, the voltage profile improves by 2.9-3.3%, branch loss reduces by 2.1-2.4%, and power transfer capability enhances by 7.5-9 units.

Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach.

The COVID-19 pandemic has worsened the psychological and social stress levels of university students due to physical illness, enhanced dependence on mobile devices and internet, a lack of social activities, and home confinement. Therefore, early stress detection is crucial for their successful academic performance and mental well-being. The advent of machine learning (ML)-based prediction models can have a crucial impact in predicting stress at its early stages and taking necessary steps for the well-being of individuals. This study aims to develop a reliable machine learning-based prediction model for perceived stress prediction and validate the model using real-world data collected through an online survey among 444 university students from different ethnicity. The machine learning models were built using supervised machine learning algorithms. Principal Component Analysis (PCA) and the chi-squared test were employed as feature reduction techniques. Moreover, Grid Search Cross-Validation (GSCV) and Genetic Algorithm (GA) were employed for hyperparameter optimization (HPO). According to the findings, around 11.26% of individuals were identified with high levels of social stress. In comparison, approximately 24.10% of people were found to be suffering from extremely high psychological stress, which is quite alarming for students' mental health. Furthermore, the prediction results of the ML models demonstrated the most remarkable accuracy (80.5%), precision (1.000), F1 score (0.890), and recall value (0.826). The Multilayer Perceptron model was shown to have the maximum accuracy when combined with PCA as a feature reduction approach and GSCV for HPO. The convenience sampling technique used in this study only considers self-reported data, which may have biased results and lack generalizability. Future research should consider a large sample of data and focus on tracking long-term impacts with coping strategies and interventions. The results of this study can be used to develop strategies to mitigate adverse effects of the overuse of mobile devices and promote student well-being during pandemics and other stressful situations.

Impact of mobile connectivity on students' wellbeing: Detecting learners' depression using machine learning algorithms.

Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people's lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.

Optimal damping for generalized unified power flow controller equipped single machine infinite bus system for addressing low frequency oscillation.

Low frequency oscillation (LFO) is one of the major concerns for reliable operation of the power system. This LFO occurs due to the failure of the rotor to supply sufficient damping torque to compensate the imbalance between mechanical input and electrical output. Hence, in this paper, we adopt a third generation flexible AC transmission system (FACTS) device named generalized unified power flow controller (GUPFC) based damping controller in order to investigate its effect for mitigating LFO for an single machine infinite bus (SMIB) system. To find an effective damping controller-optimizer pair, we integrate proportional-integral (PI) or lead-lag as a controller and grey wolf optimizer (GWO), differential evolution (DE), particle swarm optimization (PSO), whale optimization algorithm (WOA), and chaotic whale optimization algorithm (CWOA) as an optimizer. Later, we investigate the performances for the above mentioned controller-optimizer pairs through time domain simulation, eigenvalue analysis, nyquist stability test, and quantitative analysis. Moreover, we carry out two non-parametric statistical tests named as one sample Kolmogorov-Smirnov (KS) test and paired sample t-test to identify statistical distribution as well as uniqueness of our optimization algorithms. Our analyses reveal that the GWO tuned lead-lag controller surpasses all other controller-optimizer combinations.

Optimal design of proportional-integral controllers for stand-alone solid oxide fuel cell power plant using differential evolution algorithm.

This paper proposes the application of differential evolution (DE) algorithm for the optimal tuning of proportional-integral (PI) controller designed to improve the small signal dynamic response of a stand-alone solid oxide fuel cell (SOFC) system. The small signal model of the study system is derived and considered for the controller design as the target here is to track small variations in SOFC load current. Two PI controllers are incorporated in the feedback loops of hydrogen and oxygen partial pressures with an aim to improve the small signal dynamic responses. The controller design problem is formulated as the minimization of an eigenvalue based objective function where the target is to find out the optimal gains of the PI controllers in such a way that the discrepancy of the obtained and desired eigenvalues are minimized. Eigenvalue and time domain simulations are presented for both open-loop and closed loop systems. To test the efficacy of DE over other optimization tools, the results obtained with DE are compared with those obtained by particle swarm optimization (PSO) algorithm and invasive weed optimization (IWO) algorithm. Three different types of load disturbances are considered for the time domain based results to investigate the performances of different optimizers under different sorts of load variations. Moreover, non-parametric statistical analyses, namely, one sample Kolmogorov-Smirnov (KS) test and paired sample t test are used to identify the statistical advantage of one optimizer over the other for the problem under study. The presented results suggest the supremacy of DE over PSO and IWO in finding the optimal solution.

Design of static synchronous series compensator based damping controller employing invasive weed optimization algorithm.

This paper proposes designing of Static Synchronous Series Compensator (SSSC) based damping controller to enhance the stability of a Single Machine Infinite Bus (SMIB) system by means of Invasive Weed Optimization (IWO) technique. Conventional PI controller is used as the SSSC damping controller which takes rotor speed deviation as the input. The damping controller parameters are tuned based on time integral of absolute error based cost function using IWO. Performance of IWO based controller is compared to that of Particle Swarm Optimization (PSO) based controller. Time domain based simulation results are presented and performance of the controllers under different loading conditions and fault scenarios is studied in order to illustrate the effectiveness of the IWO based design approach.