The goal of this paper is to introduce an adaptive load frequency control (LFC) technique for power systems. The concept of the proposed adaptive technique is built on the on-line tuning of the gain of an integral controller using Electro-Search optimization (ESO) supported by a modification called the ‘balloon effect’. The main target is to regulate the frequency of isolated and interconnected power systems. The balloon effect is designed to obtain the input/output signals of the power system plant at any moment; then, these signals are utilized to calculate the value of the open loop transfer function of the plant at the same time. This will emphatically affect the specified objective function of the ESO approach and increase its sensitivity to and effect on system difficulties such as system step load changes, parameter uncertainties and the effect of the penetrations resulting from renewable sources. Delayed time resulting from the communication process between the area and control center is considered in the dynamic model of interconnected MGs, and nonlinearities, such as governor dead bands (GDBs) and generation rate constraints (GRCs), are included in the simulation model. The simulation results demonstrate the effectiveness and success of the proposed controller tuned by the ESO approach with a balloon effect and provide better performance of frequency regulation than a fixed controller, as well as other methods such as standard Jaya and ESO methods.

This research investigates a new coordination strategy for both isolated single-area and interconnected multi-area microgrids (MGs) using a modified virtual rotor-based derivative technique supported with Jaya optimizer based on balloon effect modulation (BE). Accordingly, the main concept of BE is to assist the classic Jaya to be more sensitive and trackable in the event of disturbances, as well as to provide optimum integral gain value on the secondary frequency controller adaptively for both suggested MGs. The proposed modified virtual rotor mechanism is consisting of virtual inertia and virtual damping that are added as a tertiary controller within proposed MGs considering full participation of the inverter-based energy storage systems. The proposed virtual rotor mechanism is consisting of virtual inertia and virtual damping that are added as a tertiary controller within proposed MGs to emulate the reduction in system inertia and the enhanced damping properties. Several nonlinearities were proposed in this work such as a dead band of governor, generation rate constraints, and communication time-delay are considered within the dynamic model of the suggested MGs. In addition, the proposed design of multi-area MGs takes the interval time-varying communication delays into account for stability conditions. In this study, A comparative study using unimodal (i.e., Sphere) and multimodal (i.e., Rastrigin) benchmark test functions are conducted to validate the proposed direct adaptive Jaya-based BE. Furthermore, Wilcoxon’s rank-signed non-parametric statistical test using a pairwise comparison was performed at a 5 % risk level to judge whether the proposed algorithm output varies from those of the other algorithms in a statistically significant manner. Thence, the superiority and effectiveness of the proposed method have also been verified against a variety of other metaheuristics optimization techniques, including classic electro-search, particle swarm, multi-objective seagull, and Jaya optimizers. In addition, an operative performance is assessed against the conventional integral controller, coefficient diagram method, and classic Jaya with/without virtual inertia. The final findings emphasize the superiority of the proposed direct adaptive Jaya-based BE supported by a modified virtual rotor and state better performance and stability compared to existing controllers.

Due to the unpredictability of the majority of green energy sources (GESs), particularly in microgrids (μGs), frequency deviations are unavoidable. These factors include solar irradiance, wind disturbances, and parametric uncertainty, all of which have a substantial impact on the system’s frequency. An adaptive load frequency control (LFC) method for power systems is suggested in this paper to mitigate the aforementioned issues. For engineering challenges, soft computing methods like the bat algorithm (BA), where it proves its effectiveness in different applications, consistently produce positive outcomes, so it is used to address the LFC issue. For online gain tuning, an integral controller using an artificial BA is utilized, and this control method is supported by a modification known as the balloon effect (BE) identifier. Stability and robustness of analysis of the suggested BA+BE scheme is investigated. The system with the proposed adaptive frequency controller is evaluated in the case of step/random load demand. In addition, high penetrations of photovoltaic (PV) sources are considered. The standard integral controller and Jaya+BE, two more optimization techniques, have been compared with the suggested BA+BE strategy. According to the results of the MATLAB simulation, the suggested technique (BA+BE) has a significant advantage over other techniques in terms of maintaining frequency stability in the presence of step/random disturbances and PV source. The suggested method successfully keeps the frequency steady over I and Jaya+BE by 61.5% and 31.25%, respectively. In order to validate the MATLAB simulation results, real-time simulation tests are given utilizing a PC and a QUARC pid_e data acquisition card.

This work analyzes the stability and performance of an offshore solar-concentrated ocean thermal energy conversion system (SC-OTEC) tied to an onshore AC grid. The OTEC is a system where electricity is generated using small temperature differences between the warm surface and deep cold ocean water. Existing control methods for SC-OTEC systems lack coordination, hindering dynamic stability and effective damping for the synchronous generator (SG). These methods struggle to quickly adapt to sudden disturbances and lack the capability to adequately reject or compensate for such disturbances due to complex control constraints and computational demands. To this regard, a control strategy combining sliding mode control (SMC) and a power system stabilizer (PSS) to improve the SC-OTEC dynamic stability and damping features for the SG. Moreover, an auxiliary secondary automatic voltage regulator is assembled with a non-linear exciter system to provide damping features. The proposed PID-PSS and secondary AVR controller gains are adaptively tuned using a modified whale optimization algorithm with the balloon effect modulation. The studied SC-OTEC is tested through MATLAB/Simulink under a severe 3ϕ short-circuit fault, solar radiation variations, and a change in surface seawater temperature as well as changes in local loads. The final findings approved that the proposed control strategy preserves a strong performance and can mimic effectively the proposed SC-OTEC damping compared to the conventional system.

Globally, glaucoma is a leading cause of irreversible visual loss. Due to the lack of symptoms in the early stages of the disease, glaucoma is typically not diagnosed until severe vision loss has occurred. One of the most common ways to diagnose glaucoma is with a comprehensive eye exam. However, a substantial commitment of time, money, and specialist equipment and personnel is necessary to carry out such investigations. Using deep learning optic cup and disc segmentation based on retinal fundus images, this work aims to develop and evaluate the effectiveness of a novel, affordable glaucoma screening tool. The research made use of ensemble learning technique to enhance semantic segmentation models. A number of recognized deep learning architectures, including Unet 3Plus, Deep Lab V3P, PSPNET and UW-Net, are combined in the proposed semantic ensemble segmentation model. Ensemble combination involves merging predictions from several models using a weighted averaging technique that considers the accuracy and dependability of each individual model. Metrics such as accuracy, specificity, sensitivity, area under the curve, intersection over union, dice coefficient, and f1-score are used to evaluate the performance in the study. The proposed model was validated on three publicly available datasets, namely ORIGA, REFUGE and RIM-ONE DL. The experimental results show that the suggested method can estimate the Cup to Disc Ratio CDR for thorough glaucoma screening, and it is on par with the state-of-the-art architecture utilized for optic disc and cup segmentation.

Globally, glaucoma is a leading cause of irreversible visual loss. Due to the lack of symptoms in the early stages of the disease, glaucoma is typically not diagnosed until severe vision loss has occurred. One of the most common ways to diagnose glaucoma is with a comprehensive eye exam. However, a substantial commitment of time, money, and specialist equipment and personnel is necessary to carry out such investigations. Using deep learning optic cup and disc segmentation based on retinal fundus images, this work aims to develop and evaluate the effectiveness of a novel, affordable glaucoma screening tool. The research made use of ensemble learning technique to enhance semantic segmentation models. A number of recognized deep learning architectures, including Unet 3Plus, Deep Lab V3P, PSPNET and UW-Net, are combined in the proposed semantic ensemble segmentation model. Ensemble combination involves merging predictions from several models using a weighted averaging technique that considers the accuracy and dependability of each individual model. Metrics such as accuracy, specificity, sensitivity, area under the curve, intersection over union, dice coefficient, and f1-score are used to evaluate the performance in the study.The proposed model was validated on three publicly available datasets, namely ORIGA, REFUGE and RIM-ONE DL.The experimental results show that the suggested method can estimate the Cup to Disc Ratio CDR for thorough glaucoma screening, and it is on par with the state-of-the-art architecture utilized for optic disc and cup segmentation.