Search Results (23)

The combined application of Load Frequency Control (LFC) and Automatic Voltage Regulation (AVR), known as Automatic Generation Control (AGC), manages active and reactive power to ensure system stability. This study presents a novel hybrid controller with a (1+PDD²)-(1+TI) structure, optimized using the Mirage Search Optimization (MSO) algorithm. Designed for dual-area power systems, the controller enhances both LFC and AVR by coordinating voltage and frequency loops. MSO was chosen after outperforming five algorithms (ChOA, DOA, PSO, GTO, and GBO), achieving the lowest fitness value (ITSE = 0.028). The controller was tested under various challenging conditions: sudden load disturbances, stochastic variations, nonlinearities like Generation Rate Constraints (GRC) and Governor Dead Band (GDB), time-varying reference voltages, and ±20% to ±40% parameter deviations. Across all scenarios, the (1+PDD²)-(1+TI) controller consistently outperformed MSO-tuned TID, FOPID, FOPI-PIDD², (1+PD)-PID, and conventional PID controllers. It demonstrated superior performance in regulating frequency, tie-line power, and voltage, achieving approximately a 50% improvement in dynamic response. MATLAB/SIMULINK results confirm its effectiveness in enhancing overall system stability. Full article

This study addresses the optimization of control performance for automatic voltage regulator systems by proposing a fractional-order PIDD² (FOPIDD²) controller design method based on the hybrid Black-winged Kite Algorithm (BWOA). To overcome the challenges of complex parameter tuning and adaptability to high-dimensional nonlinear optimization in PID controllers, the BWOA integrates the precise search mechanism of the Black-winged Kite Algorithm (BKA) with the spiral encircling strategy of the Whale Optimization Algorithm (WOA). By dividing high-fitness individuals into subgroups for parallel optimization, combined with an elitism preservation mechanism and Levy flight perturbation, the BWOA effectively balances global exploration and local exploitation capabilities, preventing premature convergence. Furthermore, a multi-factor objective function is adopted to optimize the six parameters of the FOPIDD² controller. Numerical simulations in MATLAB evaluate the controller’s performance across multiple dimensions, including transient response, frequency-domain stability, trajectory tracking, parameter uncertainty, and disturbance rejection, with comparisons to other recent controllers. Simulation results demonstrate that the BWOA-FOPIDD² controller achieves superior performance in most metrics. Therefore, the proposed method provides an efficient hybrid optimization framework for AVR system controller design. Full article

The increasing complexity and importance of medical data in improving patient care, advancing research, and optimizing healthcare systems led to the proposal of this study, which presents a novel methodology by evaluating the sensitivity of artificial intelligence (AI) algorithms when provided with real data, synthetic data, a mix of both, and synthetic features. Two medical datasets, the Pima Indians Diabetes Database (PIDD) and the Breast Cancer Wisconsin Dataset (BCWD), were used, employing the Gaussian Copula Synthesizer (GCS) and the Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic data. We classified the new datasets using fourteen machine learning (ML) models incorporated into PyCaret AutoML (Automated Machine Learning) and two deep neural networks, evaluating performance using accuracy (ACC), F1-score, Area Under the Curve (AUC), Matthews Correlation Coefficient (MCC), and Kappa metrics. Local Interpretable Model-agnostic Explanations (LIME) provided the explanation and justification for classification results. The quality and content of the medical data are very important; thus, when the classification of original data is unsatisfactory, a good recommendation is to create synthetic data with the SMOTE technique, where an accuracy of 0.924 is obtained, and supply the AI algorithms with a combination of original and synthetic data. Full article

This study develops a novel Lyrebird Optimization Algorithm (LOA), a technique inspired by the wild behavioral strategies of lyrebirds in response to potential threats. In a two-area interconnected power system that includes non-reheat thermal stations, this algorithm is applied to handle load frequency control (LFC) by optimizing the parameters of a Proportional–Integral–Derivative controller with a filter (PIDn). This study incorporates generation rate constraints (GRCs). The efficiency of the provided LOA-PIDn is evaluated through simulations under various disturbance scenarios and is compared against other well-established optimization techniques, including the Ziegler–Nichols (ZN), genetic algorithm (GA), Bacteria Foraging Optimization Algorithm (BFOA), Firefly Approach (FA), hybridized FA and pattern search (hFA–PS), self-adaptive multi-population elitist Jaya (SAMPE-Jaya)-based PI/PID controllers, and Teaching–Learning-Based Optimizer (TLBO) IDD/PIDD controllers. The results demonstrate the LOA’s ability to minimize the integral of time multiplied by absolute error (ITAE) and achieve significantly lower settling times for the two-area frequencies and transferred power variances in comparison with other methods. The comprehensive comparison and the inclusion of real-world constraints validate the LOA as a robust and effective tool for addressing complex optimization challenges in modern power systems. Full article

Background: Hepatocellular carcinoma (HCC) is the most prevalent malignant tumor, ranking fifth in terms of fatality with poor prognosis and a low survival rate. Rhoifolin (ROF), a flavonoid constituent, has previously been shown to suppress the proliferation of breast and pancreatic cancer cells. However, its inhibitory effect on HCC has remained unexplored. Objectives: Exploring the potent inhibitory activities and underlying mechanisms of ROF on HCC cells. Methods: The suppressive effect of ROF on HCC cells were assessed via CCK8 assay, apoptosis assay, cell cycle analysis and xenograft tumor mouse model. Furthermore, quantitative real-time PCR and western blot were applied to analyze the underlying mechanisms of ROF on HCC cells. Results: Firstly, the IC₅₀ values of ROF in HepG2 and HuH7 cells were 373.9 and 288.7 µg/mL at 24 h and 208.9 and 218.0 µg/mL at 48 h, respectively. Moreover, the apoptosis rates of HepG2 and HuH7 cells increased from 6.63% and 6.59% to 17.61% and 21.83% at 24 h and increased from 6.63% and 6.59% to 30.04% and 37.90% at 48 h, respectively. Additionally, ROF induced cell cycle arrest at the S phase in HCC cells. Furthermore, ROF suppressed the tumor growth of HCC cells in vivo without obvious toxicity. Mechanically, ROF facilitated apoptosis by upregulating the expression of PIDD1, CASP8, CASP9, BID, BAX, BIM, and BAK1 in HCC cells. Conclusions: ROF significantly restrains the growth of HCC cells in vitro and in vivo, which could be an effective supplement for HCC therapy. Full article

Integrated heat and roll forming (IHMRF) is a process that uses thermal and mechanical loads to produce localized plastic strains in plates to form complex curvature hull plates. The magnitude of the resulting plastic strain depends mainly on the following forming parameters: the machining parameters (power of the heat source, speed of the heat source, and the forming depth of the rollers), the thickness of the plate, and the thermo-physical and mechanical properties of the plate. Finding the correspondence between the plastic strain and forming parameters is the key to selecting the appropriate machining parameters for forming. A data-driven approach is ideal for this purpose. However, due to the characteristics of the IHMRF process, the forming process involves a large number of variables, and different materials have different temperature-dependent yield strengths. These high-dimensional input characteristics create a conflict between the required number of samples and the model training requirements. This paper presents a physically informed data-driven (PIDD) approach for modeling the relationship between forming parameters and plastic strains in IHMRF. Based on dimensional analysis and domain knowledge, the proposed method derives the basic thermal and mechanical relationships between the forming parameters, obtaining a much smaller number of physical parameters. These physical parameters are expressions of the physical knowledge of forming in low-dimensional space. Using the physical parameters yields higher accuracy on fewer sample data points than directly using the forming parameters as input features. Furthermore, the models trained on a variety of commonly used materials and plate thicknesses achieved comparable accuracy to the numerical simulation with unseen materials and plate thicknesses. Experimental and numerical simulations further verify the effectiveness of the proposed method by machining plates of various materials to the same shape. Full article

The ball and balancer system is a popular research platform for studying underactuated mechanical systems and developing control algorithms. It is a well-known two-dimensional balancing problem that has been addressed by a variety of controllers. This research work proposes two controllers that are proportional integral derivative-second derivative-proportional integrator (${\mathrm{PIDD}}^2$-PI) controller and tilt integral derivative with filter (TID-F) controller in a multivariate, electromechanical, and nonlinear under-actuated ball and balancer system. Integral Time Absolute Error (ITAE) is an objective function used for designing controllers because of its ability to be more sensitive to overshooting as well as reduced settling time and steady-state error. As part of the analysis, four metaheuristic optimization algorithms are compared in the optimization of proposed control strategies for cascaded control of the ball and balancer system. The algorithms are the Grey Wolf optimization algorithm (GWO), Cuckoo Search algorithm (CSA), Gradient Base Optimization (GBO), and Whale Optimization Algorithm (WOA). The effectiveness of proposed controllers ${\mathrm{PIDD}}^2$-PI and TID-F is investigated to be better in terms of transient time response than proportional integral derivative (PID), proportional integral-derivative (PI-D), proportional integral-proportional derivative (PI-PD) and proportional integral derivative-second derivative-proportional derivative (${\mathrm{PIDD}}^2$-PD). Moreover, these two proposed controllers have also been compared with recently published work. During the analysis, it is shown that the proposed control strategies exhibit significantly greater robustness and dynamic responsiveness compared to other structural controllers. The proposed controller WOA-${\mathrm{PIDD}}^2$-PI reduced the 73.38% settling time and 88.16% rise time compared to classical PID. The other proposed controller GWO-TID-F reduced 58.06% the settling time and 26.96% rise time compared to classical PID. These results show that proposed controllers are particularly distinguished in terms of rise time, settling time, maximum overshoot, and set-point tracking. Full article

In the literature, different approaches that are employed in designing automatic voltage regulators (AVRs) usually model the AVR as a single-input-single-output system, where the input is the generator reference voltage, and the output is the generator voltage. Alternately, it could be thought of as a double-input, single-output system, with the excitation voltage change serving as the additional input. In this paper, unlike in the existing literature, we designed the AVR system as a sextuple-input single-output (6ISO) system. The inputs in the model include the generator reference voltage, regulator signal change, exciter signal change, amplifier signal change, generator output signal change, and the sensor signal change. We also compared the generator voltage responses for various structural configurations and regulator parameter choices reported in the literature. The effectiveness of numerous controllers is investigated; the proportional, integral and differential (PID) controller, the PID with second-order derivative (PIDD2) controller, and the fractional order PID (FOPID) controller are the most prevalent types of controllers. The findings reveal that the regulator signal change and the generator output signal change significantly impact the generator voltage. Based on these findings, we propose a new approach to design the regulator parameter to enhance the response to generator reference voltage changes. This approach takes into consideration changes in the generator reference voltage as well as the regulator signal. We calculate the regulator settings using a cutting-edge hybrid technique called the Particle Swarm Optimization African Vultures Optimization algorithm (PSO–AVOA). The effectiveness of the regulator design technique and the proposed optimization algorithm are demonstrated. Full article

Managing industrial processes in real-time is challenging due to the nonlinearity and sensitivity of these processes. This unpredictability can cause delays in the regulation of these processes. The PID controller family is commonly used in these situations, but their performance is inadequate in systems and surroundings with varying set-points, longer dead times, external noises, and disturbances. Therefore, this research has developed a novel controller structure for PIDD${}^{\alpha }$ that incorporates the second derivative term from PIDD${}^2$ while exclusively using fractional order parameters for the second derivative term. The controllers’ robust performance has been evaluated on four simulation plants: first order, second order with time delay, third-order magnetic levitation systems, and fourth-order automatic voltage regulation systems. The controllers’ performance has also been evaluated on experimental models of pressure and flow processes. The proposed controller exhibits the least overshoot among all the systems tested. The overshoot for the first-order systems is 9.63%, for the third-order magnetic levitation system, it is 12.82%, and for the fourth-order automatic voltage regulation system, it is only 0.19%. In the pressure process plant, the overshoot is only 4.83%. All controllers for the second-order systems have a time delay, while the flow process plant has no overshoot. The proposed controller demonstrates superior settling times in various systems. For first-order systems, the settling time is 14.26 s, while in the pressure process plant, the settling time is 8.9543 s. Similarly, the proposed controllers for the second-order system with a time delay and the flow process plant have the same settling time of 46.0495 s. In addition, the proposed controller results in the lowest rise time for three different systems. The rise time is only 0.0075 s for the third-order magnetic levitation system, while the fourth-order automatic voltage regulation system has a rise time of 0.0232 s. Finally, for the flow process plant, the proposed controller has the least rise time of 25.7819 s. Thus, in all the cases, the proposed controller results in a more robust controller structure that provides the desired performance of a regular PIDD${}^2$ controller, offering better dynamic responses, shorter settling times, faster rise times, and reduced overshoot. Based on the analysis, it is evident that PIDD${}^{\alpha }$ outperforms both PID and FOPID control techniques due to its ability to produce a more robust control signal. Full article

Managing passenger information during disruption (PIDD) is a significant factor in running effective and quick-to-recover rail operations. Disruptions are unpredictable, and their timely resolution is ultimately dependent on the expert knowledge of experienced frontline staff. The development of frontline employees by their employers usually takes the form of practice reviews and ‘on-the-job’ learning, while academic education majors on theoretical approaches and classroom-based teaching. This paper reports on a novel industry-funded project that has developed a serious game (the ‘Rail Disruption Game’) that combines theory and practice to better manage PIDD for frontline staff in a UK train operating company (TOC). It defines challenges and the development method for the Rail Disruption Game; it also incorporates developer and user feedback. This paper provides insight into how to design, make and deploy a serious game as part of a gamified management process. Full article

Sustainability is important in voltage regulation control in grids and must be done successfully. In this paper, a novel tilt-fractional order integral-derivative with a second order derivative and low-pass filters controller, referred to as TI^λDND²N² controller, is proposed to enhance the control performance of an automatic voltage regulator (AVR). In this article, the equilibrium optimizer (EO) algorithm is used to optimally determine the eight parameters of the proposed controller. In this study, a function consisting of time domain specifications is used as the objective function. To evaluate the performance of the proposed controller, it is compared with the proportional-integral-derivative (PID), fractional order PID (FOPID), PID accelerator (PIDA), PID plus second order derivative (PIDD²), and hybrid controllers used in previous studies. Then, Bode analysis is performed to determine the achievement of the proposed controller in the frequency domain. Finally, the robustness test is realized to assess the response of the proposed controller against the deterioration of the system parameters. As a result, the proposed controller demonstrates outstanding control performance compared to studies in terms of settling time, rise time and overshoot. The proposed controller shows superior performance not only in frequency domain analysis but also in perturbed system parameters. Full article

PID control is the longest history and the most vital basic control mode that has been widely applied in the production process. The oscillatory dynamics of the process have various features, and parameter tuning is complicated. To reduce the complexity of the parameter tuning process and improve the performance of the system, in this research, we propose a new tuning method for the PID/PIDD2 controllers for second-order oscillatory systems with time delays under the constraint of certain robustness. In comparison to existing PID for second-order oscillatory systems with time delays, simulation findings demonstrate that the tuning method of the proposed PID/PIDD2 controllers trades off robustness and disturbance rejection performance. Full article

The pitch control system has a profound impact on the development of wind energy, and yet a delay or non-minimum phase can weaken its performance. Thus, there is a strong incentive to enhance pitch control technology in order to counteract the negative effects of unidentified delays and non-minimum phase characteristics. To reduce the complexity of the parameter-tuning process and improve the performance of the system, in this paper, we propose a novel control method for wind turbine pitch angle with time delays. Specifically, the proposed control method is state-space PIDD2, which is based on internal model control (IMC) and the open-loop system step response. Then, considering the tracking, disturbance rejection and measurement noise, the proposed controller is verified through simulations. The simulation results demonstrate that the state-space PIDD2 (SS-PIDD2) can provide a trade-off between robustness, time domain performance and measurement noise attenuation and effectively improve pitch control performance in contrast to series PID and PI control methods. Full article

In this article, the problem of voltage and frequency stability in a hybrid multi-area power system including renewable energy sources (RES) and electric vehicles has been investigated. Fractional order systems have been used to design innovative controllers for both load frequency control (LFC) and automatic voltage regulator (AVR) based on the combination of fractional order proportional-integral and proportional-integral-derivative plus double derivative (FOPI–PIDD²). Here, the dandelion optimizer (DO) algorithm is used to optimize the proposed FOPI–PIDD² controller to stabilize the voltage and frequency of the system. Finally, the results of simulations performed on MATLAB/Simulink show fast, stable, and robust performance based on sensitivity analysis, as well as the superiority of the proposed optimal control strategy in damping frequency fluctuations and active power, exchanged between areas when faced with step changes in load, the changes in the generation rate of units, and the uncertainties caused by the wide changes of dynamic values. Full article

This study investigates the use of transfer learning and modular design for adapting a pretrained model to optimize energy efficiency and heat reuse in edge data centers while meeting local conditions, such as alternative heat management and hardware configurations. A Physics-Informed Data-Driven Recurrent Neural Network (PIDD RNN) is trained on a small scale-model experiment of a six-server data center to control cooling fans and maintain the exhaust chamber temperature within safe limits. The model features a hierarchical regularizing structure that reduces the degrees of freedom by connecting parameters for related modules in the system. With a RMSE value of 1.69, the PIDD RNN outperforms both a conventional RNN (RMSE: 3.18), and a State Space Model (RMSE: 2.66). We investigate how this design facilitates transfer learning when the model is fine-tuned over a few epochs to small dataset from a second set-up with a server located in a wind tunnel. The transferred model outperforms a model trained from scratch over hundreds of epochs. Full article