Enhanced RSA Optimized TID Controller for Frequency Stabilization in a Two-Area Power System

This study presents an enhanced reptile search algorithm (ImRSA) optimized tilt-integral-derivative (TID) controller for load frequency control (LFC) in a two-area power system consisting of photovoltaic (PV) and thermal power units. The ImRSA integrates Lévy flight and logarithmic spiral search mechanisms to improve the balance between exploration and exploitation, resulting in more efficient optimization performance. The proposed controller is tested against the original reptile search algorithm (RSA) and other state-of-the-art optimization methods, such as modified grey wolf optimization with cuckoo search, black widow optimization, and gorilla troops optimization. Simulation results show that the ImRSA-optimized TID controller outperforms these approaches in terms of undershoot, overshoot, settling time, and the integral of time-weighted absolute error metric. Additionally, the ImRSA demonstrates robustness in managing frequency deviations caused by solar radiation fluctuations in PV systems. The results highlight the superior efficiency and reliability of the proposed method, especially for renewable energy integration in modern power systems

A Hybrid PSO-GCRA Framework for Optimizing Control Systems Performance

Optimization is essential for improving the performance of control systems, particularly in scenarios that involve complex, non-linear, and dynamic behaviors. This paper introduces a new hybrid optimization framework that merges Particle Swarm Optimization (PSO) with the Greater Cane Rat Algorithm (GCRA), which we call the PSO-GCRA framework. This hybrid approach takes advantage of PSO's global exploration capabilities and GCRA's local refinement strengths to overcome the shortcomings of each algorithm, such as premature convergence and ineffective local searches. We apply the proposed framework to a real-world load forecasting challenge using data from the Australian Energy Market Operator (AEMO). The PSO-GCRA framework functions in two sequential phases: first, PSO conducts a global search to explore the solution space, and then GCRA fine-tunes the solutions through mutation and crossover operations, ensuring convergence to high-quality optima. We evaluate the performance of this framework against benchmark methods, including EMD-SVR-PSO, FS-TSFE-CBSSO, VMD-FFT-IOSVR, and DCP-SVM-WO. Comprehensive experiments are carried out using metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and convergence rate.  The proposed PSO-GCRA framework achieves a MAPE of 2.05% and an RMSE of 3.91, outperforming benchmark methods, such as EMD-SVR-PSO (MAPE: 2.85%, RMSE: 4.49) and FS-TSFE-CBSSO (MAPE: 2.98%, RMSE: 4.69), in terms of accuracy, stability, and convergence efficiency. Comprehensive experiments were conducted using Australian Energy Market Operator (AEMO) data, with specific attention to normalization, parameter tuning, and iterative evaluations to ensure reliability and reproducibility

Performance and emission analysis of cotton seed biodiesel enhanced with nano-MgO as a sustainable fuel in a compression ignition engine

Biofuels present a sustainable alternative to fossil fuels, offering comparable engine performance while significantly reducing emissions. This study examines cottonseed oil, a renewable biomass feedstock, as a source for converting biodiesel and testing it in a single-cylinder diesel engine operating at 1500 rpm under five load conditions, ranging from 0 to 100 percent. Five different fuel blends were evaluated, with a particular focus on the B30 blend. Additionally, nano-magnesium oxide was added at concentrations of 10, 40, and 70 ppm to enhance engine performance and reduce emissions. Among all the fuels tested, B30 exhibited the best engine characteristics. The addition of 70 ppm of nano-magnesium oxide led to a 4.5 % increase in brake thermal efficiency (BTE) and an 11 % reduction in brake-specific fuel consumption (BSFC) compared to the standard B30. Emission measurements revealed significant reductions in carbon monoxide (30 %), hydrocarbons (26 %), nitrogen oxides (23 %), and smoke (40 %) compared to regular diesel. These improvements stem from nano-magnesium oxide’s catalytic activity, which enhances combustion and oxidation processes. A life cycle analysis (LCA), in conjunction with economic and environmental evaluations based on circular economy principles, confirmed the long-term sustainability and practicality of nano-magnesium oxide enhanced cottonseed biodiesel. Although production costs are higher due to the nano-additives, the gains in engine efficiency and reduced emissions justify the expense. This study highlights nano-magnesium oxide-doped cottonseed biodiesel as a promising, eco-friendly fuel for internal combustion engines, aligning with future policies aimed at sustainable energy transitions

Human pose estimation in physiotherapy fitness exercise correction using novel transfer learning approach

Objective To introduce and evaluate an efficient neural network approach for human pose estimation and correction during physical therapy exercises using wearable sensor data. Methods We leveraged benchmark data consisting of 276,625 records from wearable inertial and magnetic sensors. A novel method termed Random Forest Long Short-Term Memory (RFL), which integrates long short-term memory and Random Forest neural networks, was implemented for transfer feature engineering. The smartphone sensor data was used to generate new temporal and probabilistic features. These features were then utilized in machine learning methods to classify physical therapy exercises. Rigorous experiments, including k-fold validation and hyperparameter optimization, were conducted to validate the performance of the RFL approach. Results The RFL approach demonstrated superior performance, achieving a remarkable 99% accuracy with the Random Forest method. The rigorous experiments confirmed the efficacy and reliability of the method in classifying physical therapy exercises. Conclusions The proposed RFL method introduces a novel feature generation approach enhancing the accuracy of physical therapy exercise classification and correction. This innovative integration not only improves rehabilitation monitoring but also paves the way for more adaptive and intelligent physiotherapy assistance systems. By leveraging sensor data and advanced machine learning techniques, it has the potential to mitigate risks associated with disabilities and major diseases, thereby offering a feasible alternative to frequent clinic visits for consistent therapist guidance

Salp Navigation and Competitive based Parrot Optimizer (SNCPO) for efficient extreme learning machine training and global numerical optimization

Abstract Metaheuristic optimization algorithms play a crucial role in solving complex real-world problems, including machine learning parameter tuning, yet many existing approaches struggle with maintaining an effective balance between exploration and exploitation, leading to premature convergence and suboptimal solutions. The traditional Parrot Optimizer (PO) is an efficient swarm-based technique; however, it suffers from inadequate adaptability in transitioning between exploration and exploitation, limiting its ability to escape local optima. To address these challenges, this paper introduces the Salp Navigation and Competitive based Parrot Optimizer (SNCPO), a novel hybrid algorithm that integrates Competitive Swarm Optimization (CSO) and the Salp Swarm Algorithm (SSA) into the PO framework. Specifically, SNCPO employs a pairwise competitive learning strategy from CSO, which divides the population into winners and losers. Winners are refined using SSA-inspired salp navigation, enabling enhanced global search in the early stages and a dynamic transition to exploitation. Meanwhile, losers are updated using PO’s communication strategy, reinforcing solution diversity and exploration. To validate the efficacy of SNCPO, rigorous experimental evaluations were conducted on CEC2015 and CEC2020 benchmark functions, four engineering design optimization problems, and Extreme Learning Machine (ELM) training tasks across 14 datasets. The results demonstrate that SNCPO consistently outperforms existing state-of-the-art algorithms, achieving superior convergence speed, solution quality, and robustness while effectively avoiding local optima. Notably, SNCPO exhibits strong adaptability to diverse optimization landscapes, reinforcing its potential for real-world engineering and machine learning applications

Adaptive neural network based leader-following consensus control for a class of second-order nonlinear multi-agent systems

Abstract The leader-following consensus problem for a type of second-order nonlinear multi-agent systems (MASs) with input saturation, actuator faults, and sensor faults is examined in this study. An adaptive control strategy based on neural networks (NNs) is suggested to overcome the difficulties brought on by unknown nonlinear dynamics and real-world limitations. To handle the unknown nonlinear factors more precisely and flexibly than traditional approaches that depend on global Lipschitz requirements, we use the differential mean value theorem. This improvement enhances the system’s capacity to deal with nonlinear behaviors that change quickly. Additionally, sensor faults that affect location and velocity readings and actuator faults that may decrease or deform the input signal are taken into consideration in the control design. The suggested design also clearly addresses input saturation, which may restrict the control authority. Complete state measurements are not required because a distributed adaptive NN-based controller is created only on relative location and velocity data. Using stability theory and appropriate Lyapunov function construction, we rigorously demonstrate that the suggested approach leads to leader-following consensus. The efficiency and resilience of the suggested control strategy against nonlinear uncertainty, actuator and sensor failures, and saturation effects in complicated multi-agent networks are confirmed by simulation results

The quick crisscross sine cosine algorithm for optimal FACTS placement in uncertain wind integrated scenario based power systems

The Quick Crisscross Sine Cosine Algorithm (QCSCA) was developed to address the challenges of solving the Optimal Power Flow (OPF) problem in power systems that integrate renewable energy sources and Flexible AC Transmission Systems (FACTS) devices. Traditional optimization methods, such as linear programming, often struggle with the non-linear, multi-dimensional nature of modern power grids, leading to inefficiencies. QCSCA enhances the original Sine Cosine Algorithm (SCA) by incorporating adaptive parameter control, a Crisscross (CC) selection mechanism, and a Quick Move (QM) mechanism, effectively balancing exploration and exploitation. These improvements help avoid local optima and enhance convergence. Evaluated on the IEEE 30-bus test system under fixed and dynamic loading conditions, QCSCA outperformed various SCA variants, consistently minimizing generation costs, power losses, and gross costs. For instance, in Case 4, QCSCA achieved a gross cost reduction of 515.2580 $/h, outperforming competing algorithms by up to 1.29 %, while also achieving significant power loss reduction across multiple scenarios. Although its voltage deviation was slightly higher in some cases, the overall performance gains in cost and power loss optimization justified the trade-off. QCSCA superior performance was further validated by its top rankings in the Friedman Rank Test, reinforcing its applicability for real-world power flow optimization in renewable energy-integrated grids

Optimized soft-voting CNN ensemble using particle swarm optimization for endometrial cancer histopathology classification

The heterogeneity of endometrial cancer tissue presents a significant obstacle to accurate automated classification using histopathological images. While ensemble methods are a promising alternative to single Convolutional Neural Networks (CNNs), we introduce PSO-SV (Particle Swarm Optimization–Soft Voting), a novel framework that adaptively fuses the outputs of MobileNetV2, VGG19, DenseNet121, Swin Transformer, and Vision Transformer (ViT). Our key innovation is the use of Particle Swarm Optimization to dynamically determine the optimal contribution of each model in a soft-voting ensemble. We validated PSO-SV on two datasets, the first one consists from 11,977 tiles from 95 whole-slide images (WSIs) obtained from The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) project, the other dataset consists of 3,302 images from 498 patients, which are categorized into four classes. The proposed framework achieved outstanding results, including 99.67% accuracy, a 99.67% F1-score, and an Area Under the Curve (AUC) of 99.9% on the first dataset and 99% for all metrics for the second dataset. It consistently outperformed all three individual CNNs and a traditional hard-voting ensemble, highlighting its ability to synergistically combine complementary model strengths. The PSO-SV framework offers a powerful and clinically promising approach for robust endometrial cancer classification

Optimizing Photovoltaic Grid‐Connected Power Systems Through Artificial Intelligence and Robust Nonlinear Control

ABSTRACT Photovoltaic (PV) systems are highly sensitive to stochastic environmental variations, particularly irradiance and temperature, which complicate the task of consistently operating at the maximum power point (MPP). This paper presents a novel dual‐layer control strategy to optimize the performance of a single‐phase grid‐connected PV system. The proposed system integrates an Artificial Neural Network (ANN) model for real‐time estimation of MPP voltage with a Lyapunov‐stable nonlinear backstepping controller to regulate a DC‐DC boost converter. To ensure seamless grid integration, a Phase‐Locked Loop (PLL)‐based Proportional Resonant (PR) controller and a PI regulator are implemented to maintain DC‐link stability and minimize harmonic distortion. Unlike conventional ANN‐MPPT approaches, the proposed method decouples MPP prediction from control regulation, allowing robust and fast dynamic response under varying climatic and load conditions. Extensive simulations in MATLAB/Simulink validate the effectiveness of the approach, demonstrating fast convergence to the MPP (within 30 ms), precise DC‐link regulation, and excellent grid synchronization. Notably, the system achieves a low Total Harmonic Distortion (THD) of 0.2148%, outperforming existing benchmarks. Comparative analysis against P&O and sliding mode controllers confirms the superior efficiency, robustness, and power quality of the proposed architecture, highlighting its potential for scalable deployment in real‐world grid‐connected PV applications

A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction

Abstract Accurate very short-term wind power forecasting is critical for the reliable integration of renewable energy into modern power systems. However, the inherent variability and non-linearity of wind power data pose significant challenges. To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. The model is optimized using an enhanced Artificial Protozoa Optimizer (IAPO) augmented with an Adaptive Environmental Response Mechanism (AERM), which dynamically adjusts exploration and exploitation strategies based on the problem landscape to improve convergence and hyperparameter tuning efficiency. The proposed IAPO-LSTM model was evaluated on four real-world datasets—NREL WIND, EMD WIND, WWSIS, and ERCOT GRID—and benchmarked against six state-of-the-art forecasting models. Results demonstrate that IAPO-LSTM achieved the lowest forecasting errors across all datasets, with Mean Absolute Error (MAE) as low as 2.78, Root Mean Square Error (RMSE) of 4.50, and Theil’s Inequality Coefficient (TIC) of 0.0292 on the ERCOT dataset. Additionally, the model demonstrated faster inference times and better statistical significance (p < 0.005) compared to baseline methods. These outcomes confirm that IAPO-LSTM is not only highly accurate but also efficient and robust for real-time wind power forecasting applications