Mathematical and Computational Applications

Editorial

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4 pages, 153 KB

Open AccessEditorial

Advances in Applied Optimization in Automatic Control and Systems Engineering

by Guillermo Valencia-Palomo

Math. Comput. Appl. 2026, 31(2), 61; https://doi.org/10.3390/mca31020061 - 11 Apr 2026

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Abstract

Applied optimization in automatic control and systems engineering involves developing and implementing mathematical methods to improve the performance and efficiency of automated systems [...] Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

Research

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24 pages, 1112 KB

Open AccessArticle

Reliable Emergency Facility Location Planning Under Complex Polygonal Barriers and Facility Failure Risks

by Mingyuan Liu, Lintao Liu, Zhujia Yu, Futai Liang and Guocheng Wang

Math. Comput. Appl. 2026, 31(2), 50; https://doi.org/10.3390/mca31020050 - 18 Mar 2026

Cited by 1 | Viewed by 432

Abstract

Emergency facility location and layout are critical to the efficiency of emergency rescue and resource allocation. However, practical emergency scenarios are plagued by two key challenges: the risk of facility failure due to various uncertain factors and the presence of complex polygonal barriers [...] Read more.

Emergency facility location and layout are critical to the efficiency of emergency rescue and resource allocation. However, practical emergency scenarios are plagued by two key challenges: the risk of facility failure due to various uncertain factors and the presence of complex polygonal barriers (including convex and concave polygons) that hinder transportation. Existing studies often overlook concave polygonal barriers or fail to prioritize time satisfaction, a core demand in emergency response. To address these gaps, this paper proposes a reliable emergency facility location optimization model with the objective of maximizing time satisfaction, considering constraints such as capacity, cost, and demand. The model integrates three key methods: a convex hull algorithm to convert concave barriers into convex ones for simplified calculation, a path optimization algorithm to find the shortest bypass routes around barriers, and an Artificial Ecosystem Optimization (AEO) algorithm to solve the nonlinear programming model. Through numerical experiments (single-facility, multi-facility, and medium-scale scenarios) and a practical case study in the Meknès region of Morocco for ambulance deployment, the feasibility and effectiveness of the model and algorithms are verified. The results show that the model achieves high time satisfaction (all above 0.8, with most exceeding 0.9) and efficiently optimizes facility locations and resource allocation. Sensitivity analysis indicates that increased failure risk parameters (

α

and

θ

) lead to a gradual decrease in average time satisfaction. This research provides a systematic mathematical model and practical method for emergency facility location decision-making, effectively addressing the challenges of complex barriers and facility failure. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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12 pages, 756 KB

Open AccessCommunication

Revised Long-Term Scheduling Model for Multi-Stage Biopharmaceutical Processes

by Vaibhav Kumar and Munawar A. Shaik

Math. Comput. Appl. 2026, 31(1), 32; https://doi.org/10.3390/mca31010032 - 15 Feb 2026

Cited by 1 | Viewed by 590

Abstract

Biopharmaceuticals are therapeutic drugs engineered to target specific sites within the body. Their manufacturing process comprises two primary stages: upstream processing (USP) and downstream processing (DSP). USP primarily involves cell culture and growth, whereas DSP focuses on purifying and packaging the final product. [...] Read more.

Biopharmaceuticals are therapeutic drugs engineered to target specific sites within the body. Their manufacturing process comprises two primary stages: upstream processing (USP) and downstream processing (DSP). USP primarily involves cell culture and growth, whereas DSP focuses on purifying and packaging the final product. The recent literature only reports a few studies addressing production planning and scheduling in biopharmaceutical manufacturing. In this work, we address a long-term scheduling and midterm planning problem incorporating on-time or late delivery of final products with unknown finite delivery rates. Early delivery is prohibited, and late delivery incurs a penalty cost. Published models and evolutionary algorithms exhibit key limitations in areas such as shelf-life modeling, inventory management, and product delivery. To overcome these shortcomings, we propose a revised mixed-integer linear programming (MILP) model implemented using the General Algebraic Modeling System (GAMS). When applied to two illustrative examples, the model reduces optimum event counts by two to three, improving computational efficiency through fewer binary variables, continuous variables, and constraints. Furthermore, it achieves up to 7% improvement over two published benchmarks, underscoring its potential to enhance scheduling strategies for multiproduct biopharmaceutical facilities. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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13 pages, 717 KB

Open AccessArticle

Gaining Understanding of Neural Networks with Programmatically Generated Data

by Eric O’Sullivan, Ken Kennedy and Jean Mohammadi-Aragh

Math. Comput. Appl. 2026, 31(1), 16; https://doi.org/10.3390/mca31010016 - 22 Jan 2026

Cited by 1 | Viewed by 410

Abstract

The performance of convolutional neural networks (CNNs) depends not only on model architecture but also on the structure and quality of the training data. While most artificial network interpretability methods focus on explaining trained models, less attention has been given to understanding how [...] Read more.

The performance of convolutional neural networks (CNNs) depends not only on model architecture but also on the structure and quality of the training data. While most artificial network interpretability methods focus on explaining trained models, less attention has been given to understanding how dataset composition itself shapes learning outcomes. This work introduces a novel framework that uses programmatically generated synthetic datasets to isolate and control visual features, enabling systematic evaluation of their contribution to CNN performance. Guided by principles from set theory, Shapley values, and the Apriori algorithm, we formalize an equivalence between CNN kernel weights and pattern frequency counts, showing that feature overlap across datasets predicts model generalization. Methods include the construction of four synthetic digit datasets with controlled object and background features, training lightweight CNNs under K-fold validation, and statistical evaluation of cross-dataset performance. The results show that internal object patterns significantly improve accuracy and F1 scores compared to non-object background features, and that a dataset similarity prediction algorithm achieves near-perfect correlation (

ρ = 0.97

) between the predicted and observed performance. The conclusions highlight that dataset feature composition can be treated as a measurable proxy for model behavior, offering a new path for dataset evaluation, pruning, and design optimization. This approach provides a principled framework for predicting CNN performance without requiring full-scale model training. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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15 pages, 2563 KB

Open AccessArticle

Eigenstructure-Oriented Optimization Design of Active Suspension Controllers

by Yulong Du and Huping Mao

Math. Comput. Appl. 2026, 31(1), 5; https://doi.org/10.3390/mca31010005 - 1 Jan 2026

Cited by 1 | Viewed by 501

Abstract

Active suspension systems can significantly enhance vehicle ride comfort and attitude stability, but often at the cost of increased energy consumption. To achieve both high dynamic performance and reduced energy usage, this study proposes an eigenstructure-oriented optimization method for active suspensions. Controller design [...] Read more.

Active suspension systems can significantly enhance vehicle ride comfort and attitude stability, but often at the cost of increased energy consumption. To achieve both high dynamic performance and reduced energy usage, this study proposes an eigenstructure-oriented optimization method for active suspensions. Controller design is reformulated as a synergistic process of modal regulation and dynamic response optimization, in which partial eigenstructure assignment redistributes the dominant modes and system responses are computed using fourth-order Runge–Kutta integration. An energy-minimization optimization problem with performance constraints is then solved via the sequential quadratic programming (SQP) algorithm. Simulation results show that the proposed method markedly improves vibration performance: peak body acceleration is reduced from 3.48 m/s² to 1.70 m/s² (a 51.1% reduction), and the root mean square (RMS) acceleration decreases from 0.74 to 0.40 (a 45.6% reduction), while body displacement is also significantly suppressed. Compared with passive suspension and proportional–integral–derivative (PID) active suspension, the proposed system achieves superior performance in key indices such as body acceleration and displacement, leading to noticeably improved ride comfort and attitude stability. Furthermore, robustness analysis indicates that the method remains effective under variations in the receptance matrix, with only minor influence on system performance, demonstrating the practical applicability of the proposed control strategy. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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15 pages, 457 KB

Open AccessArticle

Adaptive Observer Design with Fixed-Time Convergence, Online Disturbance Learning, and Low-Conservatism Linear Matrix Inequalities for Time-Varying Perturbed Systems

by Essia Ben Alaia, Slim Dhahri and Omar Naifar

Math. Comput. Appl. 2025, 30(5), 112; https://doi.org/10.3390/mca30050112 - 8 Oct 2025

Cited by 1 | Viewed by 1063

Abstract

This paper proposes a finite-time adaptive observer with online disturbance learning for time-varying disturbed systems. By integrating parameter-dependent Lyapunov functions and slack matrix techniques, the method eliminates conservative static disturbance bounds required in prior work while guaranteeing fixed-time convergence. The proposed approach features [...] Read more.

This paper proposes a finite-time adaptive observer with online disturbance learning for time-varying disturbed systems. By integrating parameter-dependent Lyapunov functions and slack matrix techniques, the method eliminates conservative static disturbance bounds required in prior work while guaranteeing fixed-time convergence. The proposed approach features a non-diagonal gain structure that provides superior noise rejection capabilities, demonstrating 41% better performance under measurement noise compared to conventional methods. A power systems case study demonstrates significantly improved performance, including 62% faster convergence and 63% lower steady-state error. These results are validated through LMI-based synthesis and adaptive disturbance estimation. Implementation analysis confirms the method’s feasibility for real-time systems with practical computational requirements. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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27 pages, 2748 KB

Open AccessArticle

Energy Optimization of Compressed Air Systems with Screw Compressors Under Variable Load Conditions

by Guillermo José Barroso García, José Pedro Monteagudo Yanes, Luis Angel Iturralde Carrera, Carlos D. Constantino-Robles, Brenda Juárez Santiago, Juan Manuel Olivares Ramírez, Omar Rodriguez Abreo and Juvenal Rodríguez-Reséndiz

Math. Comput. Appl. 2025, 30(5), 107; https://doi.org/10.3390/mca30050107 - 1 Oct 2025

Cited by 2 | Viewed by 1968

Abstract

This study evaluates the energy performance of a BOGE C 22-2 oil-injected rotary screw compressor under real industrial conditions. Using direct measurements with a power quality analyzer and thermodynamic modeling, key performance indicators such as compression work, mass flow rate, compressor efficiency, and [...] Read more.

This study evaluates the energy performance of a BOGE C 22-2 oil-injected rotary screw compressor under real industrial conditions. Using direct measurements with a power quality analyzer and thermodynamic modeling, key performance indicators such as compression work, mass flow rate, compressor efficiency, and motor efficiency were determined. The results revealed actual efficiencies of 27–48%, significantly lower than the expected 60–70% for this type of equipment, mainly due to partial-load operation and low airflow demand. A low power factor of approximately 0.72 was also observed, caused by a high share of reactive power consumption. To address these inefficiencies, the study recommends the installation of an automatic capacitor bank to improve power quality and the integration of a secondary variable speed compressor to enhance performance under low-demand conditions. These findings underscore the importance of assessing compressor behavior in real-world environments and implementing techno-economic strategies to increase energy efficiency and reduce industrial electricity consumption. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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23 pages, 4451 KB

Open AccessArticle

Energy Management and Power Distribution for Battery/Ultracapacitor Hybrid Energy Storage System in Electric Vehicles with Regenerative Braking Control

by Abdelsalam A. Ahmed, Young Il Lee, Saleh Al Dawsari, Ahmed A. Zaki Diab and Abdelsalam A. Ezzat

Math. Comput. Appl. 2025, 30(4), 82; https://doi.org/10.3390/mca30040082 - 3 Aug 2025

Cited by 3 | Viewed by 2347

Abstract

This paper presents an advanced energy management system (EMS) for optimizing power distribution in a battery/ultracapacitor (UC) hybrid energy storage system (HESS) for electric vehicles (EVs). The proposed EMS accounts for all energy flow scenarios within a practical driving cycle. A regenerative braking [...] Read more.

This paper presents an advanced energy management system (EMS) for optimizing power distribution in a battery/ultracapacitor (UC) hybrid energy storage system (HESS) for electric vehicles (EVs). The proposed EMS accounts for all energy flow scenarios within a practical driving cycle. A regenerative braking control strategy is developed to maximize kinetic energy recovery using an induction motor, efficiently distributing the recovered energy between the UC and battery. Additionally, a power flow management approach is introduced for both motoring (discharge) and braking (charge) operations via bidirectional buck–boost DC-DC converters. In discharge mode, an optimal distribution factor is dynamically adjusted to balance power delivery between the battery and UC, maximizing efficiency. During charging, a DC link voltage control mechanism prioritizes UC charging over the battery, reducing stress and enhancing energy recovery efficiency. The proposed EMS is validated through simulations and experiments, demonstrating significant improvements in vehicle acceleration, energy efficiency, and battery lifespan. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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23 pages, 8795 KB

Open AccessArticle

Secure Communication of Electric Drive System Using Chaotic Systems Base on Disturbance Observer and Fuzzy Brain Emotional Learning Neural Network

by Huyen Chau Phan Thi, Nhat Quang Dang and Van Nam Giap

Math. Comput. Appl. 2025, 30(4), 73; https://doi.org/10.3390/mca30040073 - 14 Jul 2025

Cited by 1 | Viewed by 1187 | Correction

Abstract

This paper presents a novel wireless control framework for electric drive systems by employing a fuzzy brain emotional learning neural network (FBELNN) controller in conjunction with a Disturbance Observer (DO). The communication scheme uses chaotic system dynamics to ensure data confidentiality and robustness [...] Read more.

This paper presents a novel wireless control framework for electric drive systems by employing a fuzzy brain emotional learning neural network (FBELNN) controller in conjunction with a Disturbance Observer (DO). The communication scheme uses chaotic system dynamics to ensure data confidentiality and robustness against disturbance in wireless environments. To be applied to embedded microprocessors, the continuous-time chaotic system is discretized using the Grunwald–Letnikov approximation. To avoid the loss of generality of chaotic behavior, Lyapunov exponents are computed to validate the preservation of chaos in the discrete-time domain. The FBELNN controller is then developed to synchronize two non-identical chaotic systems under different initial conditions, enabling secure data encryption and decryption. Additionally, the DOB is introduced to estimate and mitigate the effects of bounded uncertainties and external disturbances, enhancing the system’s resilience to stealthy attacks. The proposed control structure is experimentally implemented on a wireless communication system utilizing ESP32 microcontrollers (Espressif Systems, Shanghai, China) based on the ESP-NOW protocol. Both control and feedback signals of the electric drive system are encrypted using chaotic states, and real-time decryption at the receiver confirms system integrity. Experimental results verify the effectiveness of the proposed method in achieving robust synchronization, accurate signal recovery, and a reliable wireless control system. The combination of FBELNN and DOB demonstrates significant potential for real-time, low-cost, and secure applications in smart electric drive systems and industrial automation. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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28 pages, 7329 KB

Open AccessArticle

Causal Diagnosability Optimization Design for UAVs Based on Maximum Mean Covariance Difference and the Gray Wolf Optimization Algorithm

by Xuping Gu and Xianjun Shi

Math. Comput. Appl. 2025, 30(3), 55; https://doi.org/10.3390/mca30030055 - 14 May 2025

Cited by 1 | Viewed by 1618

Abstract

Given the growing complexity and variability of application scenarios, coupled with increasing operational demands, unmanned aerial vehicles (UAVs) are prone to faults. To enhance diagnosability and reliability in this context, this study proposes a causal diagnosability optimization strategy based on the Maximum Mean [...] Read more.

Given the growing complexity and variability of application scenarios, coupled with increasing operational demands, unmanned aerial vehicles (UAVs) are prone to faults. To enhance diagnosability and reliability in this context, this study proposes a causal diagnosability optimization strategy based on the Maximum Mean and Covariance Discrepancy (MMCD) metric and the Grey Wolf Optimization (GWO) algorithm. First, a qualitative assessment method for causal diagnosability is introduced, leveraging structural analysis to evaluate the detectability and isolability of faults. Next, residuals are generated using Minimal Structurally Overdetermined (MSO) sets, and a quantitative diagnosability assessment framework is developed based on the MMCD metric. This framework measures the complexity of diagnosability through the analysis of residual deviations under fault conditions. Finally, a diagnosability optimization technique utilizing the GWO algorithm is proposed. This approach minimizes diagnostic system design costs while maximizing its performance. Simulation results for a UAV structural model demonstrate that the proposed strategy achieves a 100% fault detection rate and fault isolation rate while reducing design costs by 70.59%. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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20 pages, 350 KB

Open AccessArticle

A Family of Newton and Quasi-Newton Methods for Power Flow Analysis in Bipolar Direct Current Networks with Constant Power Loads

by Oscar Danilo Montoya, Juan Diego Pulgarín Rivera, Luis Fernando Grisales-Noreña, Walter Gil-González and Fabio Andrade-Rengifo

Math. Comput. Appl. 2025, 30(3), 50; https://doi.org/10.3390/mca30030050 - 6 May 2025

Cited by 2 | Viewed by 1615

Abstract

This paper presents a comprehensive study on the formulation and solution of the power flow problem in bipolar direct current (DC) distribution networks with unbalanced constant power loads. Using the nodal voltage method, a unified nonlinear model is proposed which accurately captures both [...] Read more.

This paper presents a comprehensive study on the formulation and solution of the power flow problem in bipolar direct current (DC) distribution networks with unbalanced constant power loads. Using the nodal voltage method, a unified nonlinear model is proposed which accurately captures both monopolar and bipolar load configurations as well as the voltage coupling between conductors. The model assumes a solid grounding of the neutral conductor and known system parameters, ensuring reproducibility and physical consistency. Seven iterative algorithms are developed and compared, including three Newton–Raphson-based formulations and four quasi-Newton methods with constant Jacobian approximations. The proposed techniques are validated on two benchmark networks comprising 21 and 85 buses. Numerical results demonstrate that Newton-based methods exhibit quadratic convergence and high accuracy, while quasi-Newton approaches significantly reduce computational time, making them more suitable for large-scale systems. The findings highlight the trade-offs between convergence speed and computational efficiency, and they provide valuable insights for the planning and operation of modern bipolar DC grids. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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21 pages, 519 KB

Open AccessArticle

Learning Deceptive Tactics for Defense and Attack in Bayesian–Markov Stackelberg Security Games

by Julio B. Clempner

Math. Comput. Appl. 2025, 30(2), 29; https://doi.org/10.3390/mca30020029 - 17 Mar 2025

Cited by 4 | Viewed by 2111

Abstract

In this paper, we address the challenges posed by limited knowledge in security games by proposing a novel system grounded in Bayesian–Markov Stackelberg security games (SSGs). These SSGs involve multiple defenders and attackers and serve as a framework for managing incomplete information effectively. [...] Read more.

In this paper, we address the challenges posed by limited knowledge in security games by proposing a novel system grounded in Bayesian–Markov Stackelberg security games (SSGs). These SSGs involve multiple defenders and attackers and serve as a framework for managing incomplete information effectively. To tackle the complexity inherent in these games, we introduce an iterative proximal-gradient approach to compute the Bayesian Equilibrium, which captures the optimal strategies of both defenders and attackers. This method enables us to navigate the intricacies of the game dynamics, even when the specifics of the Markov games are unknown. Moreover, our research emphasizes the importance of Bayesian approaches in solving the reinforcement learning (RL) algorithm, particularly in addressing the exploration–exploitation trade-off. By leveraging Bayesian techniques, we aim to minimize the expected total discounted costs, thus optimizing decision-making in the security domain. In pursuit of effective security game implementation, we propose a novel random walk approach tailored to fulfill the requirements of the scenario. This innovative methodology enhances the adaptability and responsiveness of defenders and attackers, thereby improving overall security outcomes. To validate the efficacy of our proposed strategy, we provide a numerical example that demonstrates its benefits in practice. Through this example, we showcase how our approach can effectively address the challenges posed by limited knowledge, leading to more robust and efficient security solutions. Overall, our paper contributes to advancing the understanding and implementation of security strategies in scenarios characterized by incomplete information. By combining Bayesian and Markov Stackelberg games, reinforcement learning algorithms, and innovative random walk techniques, we offer a comprehensive framework for enhancing security measures in real-world applications. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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30 pages, 858 KB

Open AccessArticle

Sliding Mode Fault-Tolerant Control for Nonlinear LPV Systems with Variable Time-Delay

by Omayma Mansouri, Ali Ben Brahim, Fayçal Ben Hmida and Anis Sellami

Math. Comput. Appl. 2024, 29(6), 96; https://doi.org/10.3390/mca29060096 - 26 Oct 2024

Cited by 2 | Viewed by 1963

Abstract

This paper presents a robust sliding mode fault-tolerant control (FTC) strategy for a class of linear parameter variant (LPV) systems with variable time-delays and uncertainties. First fault estimation (FE) is conducted using a robust sliding mode observer, synthesized to simultaneously estimate the states [...] Read more.

This paper presents a robust sliding mode fault-tolerant control (FTC) strategy for a class of linear parameter variant (LPV) systems with variable time-delays and uncertainties. First fault estimation (FE) is conducted using a robust sliding mode observer, synthesized to simultaneously estimate the states and actuator faults of LPV polytopic delayed systems. Second, a sliding mode FTC is developed, ensuring all states of the closed-loop system converge to the origin. This paper presents an integrated sliding mode FTC strategy to achieve optimal robustness between the observer and controller models. The integrated design approach offers several advantages over traditional separated FTC methods. Our novel approach is based on incorporating adaptive law into the design of the Lyapunov–Krasovskii functional to improve both robustness and performance. This is achieved by combining the concept of sliding mode control (SMC) with the Lyapunov–Krasovskii function under the

H_{\infty}

criteria, which plays a key role in guaranteeing the stability of this class of system. The effectiveness of the proposed method is demonstrated through a diesel engine example, which highlights the validity and benefits of the integrated and separated FTC strategy for uncertain nonlinear systems with time delays and the sliding mode control. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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14 pages, 3165 KB

Open AccessArticle

Optimized Nonlinear PID Control for Maximum Power Point Tracking in PV Systems Using Particle Swarm Optimization

by Maeva Cybelle Zoleko Zambou, Alain Soup Tewa Kammogne, Martin Siewe Siewe, Ahmad Taher Azar, Saim Ahmed and Ibrahim A. Hameed

Math. Comput. Appl. 2024, 29(5), 88; https://doi.org/10.3390/mca29050088 - 2 Oct 2024

Cited by 10 | Viewed by 2614 | Correction

Abstract

This paper proposes a high-performing, hybrid method for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. The approach is based on an intelligent Nonlinear Discrete Proportional–Integral–Derivative (N-DPID) controller with the Perturb and Observe (P&O) method. The feedback gains derived are optimized by [...] Read more.

This paper proposes a high-performing, hybrid method for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. The approach is based on an intelligent Nonlinear Discrete Proportional–Integral–Derivative (N-DPID) controller with the Perturb and Observe (P&O) method. The feedback gains derived are optimized by a metaheuristic algorithm called Particle Swarm Optimization (PSO). The proposed methods appear to present adequate solutions to overcome the drawbacks of existing methods despite various weather conditions considered in the analysis, providing a robust solution for dynamic environmental conditions. The results showed better performance and accuracy compared to those encountered in the literature. We also recall that this technique provides a systematic design procedure in the search for the MPPT in photovoltaic (PV) systems that has not yet been documented in the literature to the best of our knowledge. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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Review

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25 pages, 1607 KB

Open AccessReview

Optimizing Power Flow and Stability in Hybrid AC/DC Microgrids: AC, DC, and Combined Analysis

by Ghanshyam Meena, Veerpratap Meena, Akhilesh Mathur, Vinay Pratap Singh, Ahmad Taher Azar and Ibrahim A. Hameed

Math. Comput. Appl. 2024, 29(6), 108; https://doi.org/10.3390/mca29060108 - 24 Nov 2024

Cited by 12 | Viewed by 3846

Abstract

A microgrid (MG) is a unique area of a power distribution network that combines distributed generators (conventional as well as renewable power sources) and energy storage systems. Due to the integration of renewable generation sources, microgrids have become more unpredictable. MGs can operate [...] Read more.

A microgrid (MG) is a unique area of a power distribution network that combines distributed generators (conventional as well as renewable power sources) and energy storage systems. Due to the integration of renewable generation sources, microgrids have become more unpredictable. MGs can operate in two different modes, namely, grid-connected and islanded modes. MGs face various challenges of voltage variations, frequency deviations, harmonics, unbalances, etc., due to the uncertain behavior of renewable sources. To study the impact of these issues, it is necessary to analyze the behavior of the MG system under normal and abnormal operating conditions. Two different tools are used for the analysis of microgrids under normal and abnormal conditions, namely, power flow and short-circuit analysis, respectively. Power flow analysis is used to determine the voltages, currents, and real and reactive power flow in the MG system under normal operating conditions. Short-circuit analysis is carried out to analyze the behavior of MGs under faulty conditions. In this paper, a review of power flow and short-circuit analysis algorithms for MG systems under two different modes of operation, grid-connected and islanded, is presented. This paper also presents a comparison of various power flow as well as short-circuit analysis techniques for MGs in tabular form. The modeling of different components of MGs is also discussed in this paper. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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Other

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25 pages, 2450 KB

Open AccessSystematic Review

The Preference Selection Index (PSI) in Multi-Criteria Decision-Making: A Systematic and Critical Review of Applications, Integrations, and Future Directions

by Mohammed Said Obeidat, Hala Al Sliti and Abdullah Obeidat

Math. Comput. Appl. 2025, 30(6), 124; https://doi.org/10.3390/mca30060124 - 13 Nov 2025

Cited by 2 | Viewed by 1944

Abstract

This paper presents a systematic review of the performance of the Preference Selection Index (PSI) application in multi-criteria decision-making (MCDM) problems based on PRISMA. This work extensively reviewed more than 100 studies to investigate the methodological bases of the PSI and its synergistic [...] Read more.

This paper presents a systematic review of the performance of the Preference Selection Index (PSI) application in multi-criteria decision-making (MCDM) problems based on PRISMA. This work extensively reviewed more than 100 studies to investigate the methodological bases of the PSI and its synergistic combination with other decision-making methodologies. Interestingly, the PSI is highly commended as one of the most straightforward applications with low computational effort, which implies that the PSI in this context is receiving wide attention for complex decisions and sensitive judgments, where assigning criteria weights is challenging. However, in some circumstances, the PSI mechanism in assigning weights becomes a drawback when the accuracy of the decision is crucial. However, despite the increased use of the PSI, there is still a lack of systematic evaluation of its methodological sensitivity of weighting assumptions, consistency, and comparative performance in the hybrid MCDM problems. Addressing these gaps will help make the PSI more accurate in the evolving landscape of decision-making techniques. This review underscores the wide use of the PSI, encouraging further research in terms of its applications and methodology enhancement, ensuring that the PSI remains a relevant option that evolves the complexity and sensitivity of decision-making in various areas. Full article

(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)

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