University of Mohaghegh Ardabili Journal of Operation and Automation in Power Engineering 2322-4576 12 Special Issue 2024 06 01 Leveraging Quantum Key Distribution for Data Security in Distributed Energy Resources 1 6 3237 10.22098/joape.2024.15383.2177 EN Asnal Effendi Department of Electrical Installation Engineering Technology, Institut Teknologi Padang, Indonesia Muntadher A. Hussein Department of Medical Laboratory Technics, Al-Manara College For Medical Sciences, Maysan, Iraq Nada Q. Mohammed Department of Medical Laboratories Technology, AL-Nisour University College, Baghdad, Iraq Hussam A. Abdulridui Department of Medical Laboratory Technics, Al-Hadi University College, Baghdad, 10011, Iraq Baydaa Sh.Z. Abood College of Health and Medical Technology, National University of Science and Technology, Dhi Qar, 64001, Iraq Saoud C. Mashkoor Department of Medical Laboratory Technics, Mazaya University College, Iraq Zahraa Y. Shaker Department of Medical Laboratory Technics, Al-Zahrawi University College, Karbala, Iraq 0009-0003-6958-4964 Kadhum Al-Majdi Department of biomedical engineering, Ashur University College, Baghdad, Iraq Khamdamov O. Nematullaevich Tashkent State University of Economics, Islam Karimov Street, 49, Tashkent, 100066, Uzbekistan Dahlan Abdullah Department of Informatics, Universitas Malikussaleh, Aceh, Indonesia Yerkin Yerzhigitov Kazakh National Agrarian Research University, Abai 8 Almaty, Kazakhstan Journal Article 2024 07 02 The rapid proliferation of Distributed Energy Resources (DERs) introduces substantial challenges in securing the vast volumes of data exchanged within these decentralized networks. While traditional cryptographic methods remain effective, they are increasingly susceptible to the threats posed by quantum computing, particularly in the realm of key distribution. This paper proposes Quantum Key Distribution (QKD) as an advanced solution, harnessing the principles of quantum mechanics to deliver unparalleled security for cryptographic key establishment. We explore the application of QKD within DER systems, addressing specific constraints such as limited bandwidth, resource-constrained devices, and dynamic network topologies. We assess the feasibility of incorporating QKD into existing communication frameworks by evaluating the BB84 QKD protocol and its integration with DER infrastructures. Our study also considers practical aspects such as scalability, interoperability, and cost-effectiveness. The findings reveal that QKD achieves a practical key efficiency of approximately 50%, underscoring its suitability for DER applications. Moreover, QKD provides robust security features, including minimal error rates in noiseless environments, manageable error rates in noisy conditions, and strong resilience against eavesdropping. These capabilities ensure the integrity and confidentiality of data within DER networks, marking a significant advancement in secure communication technologies. https://joape.uma.ac.ir/article_3237_38fbe2f19c88b102b3fa8e6ad5d6d97d.pdf

University of Mohaghegh Ardabili Journal of Operation and Automation in Power Engineering 2322-4576 12 Special Issue 2024 06 01 Enhancing Microgrid Resilience with LSTM and Fuzzy Logic for Predictive Maintenance 7 15 3602 10.22098/joape.2024.15909.2225 EN Nurgul Almuratova Power supply and electric drive department, Almaty University of Power Engineering and Telecommunications, Almaty 050013, Kazakhstan; Marat Mustafin Power supply and electric drive department, Almaty University of Power Engineering and Telecommunications, Almaty 050013, Kazakhstan; Kakimzhan Gali Power supply and electric drive department, Almaty University of Power Engineering and Telecommunications, Almaty 050013, Kazakhstan; Makpal Zharkymbekova Power supply and electric drive department, Almaty University of Power Engineering and Telecommunications, Almaty 050013, Kazakhstan; Danna Chnybayeva Power supply and electric drive department, Almaty University of Power Engineering and Telecommunications, Almaty 050013, Kazakhstan; Markhabat Sakitzhanov Power supply and electric drive department, Almaty University of Power Engineering and Telecommunications, Almaty 050013, Kazakhstan; Journal Article 2024 09 29 Microgrids have become integral to modern energy systems, providing decentralized and resilient energy solutions. However, ensuring the reliability of microgrid assets poses significant challenges, particularly given aging infrastructure and unpredictable environmental conditions. While existing methods—such as predictive maintenance, real-time monitoring, and fault detection utilizing Support Vector Machines (SVM), Random Forests, and Principal Component Analysis (PCA)—enhance reliability, they often fall short due to insufficient multidimensional data analysis and limited support for realistic decision-making. This underscores the need for advanced approaches in microgrid management. In this paper, we propose an innovative machine learning-based methodology that integrates Long Short-Term Memory (LSTM) networks with fuzzy logic for predictive maintenance of microgrid assets. The proposed approach effectively addresses the inherent fluctuations and dynamic behavior of microgrids, enhancing system resilience and reducing downtime. By leveraging LSTM's ability to capture temporal patterns alongside fuzzy logic's capacity for handling uncertainties, the method proactively identifies and mitigates potential equipment failures. Traditional maintenance strategies predominantly rely on reactive mechanisms, resulting in higher costs and increased system vulnerabilities. Simulation results indicate that the proposed algorithm achieves a 10% to 40% improvement in fault detection across varying failure levels, demonstrating significant advantages over conventional techniques. https://joape.uma.ac.ir/article_3602_6b234b838685b9871cf0fc4be28f3b2f.pdf

University of Mohaghegh Ardabili Journal of Operation and Automation in Power Engineering 2322-4576 12 Special Issue 2024 06 01 Stability Enhancement of Hybrid Wind-Diesel Systems via SVC and PSO-Optimized Voltage and Frequency Control 16 23 3626 10.22098/joape.2025.15384.2178 EN Wilson-Wilmar Candia-Quispe Faculty of Engineering, Universidad Tecnologica de los Andes , Andahuaylas, Peru. Iris-Liliana Vásquez-Alburqueque Academic Department of Philosophy and Art, National University of Trujillo, Trujillo. Peru Nestor Cuba Carbajal Departamento Académico of Turismo y Hotelería, Universidad de San Martin de Porres, Lima , Perú Marco Antonio Ananos Bedrinana Faculty of Agricultural Sciences Professional School of Forestry and Environmental Engineering, National Autonomous University of Chota, Chota, Peru Diana Luján Pérez Department of graduate School, National University of San Cristobal de Huamanga, Ayacucho, Perú. Yersi-Luis Huamán-Romaní Department of Basic Sciences, National Amazonian University of Madre de Dios, Puerto Maldonado, Peru. Juan-Jesús Garrido-Arismendis Department of Food Industries Engineering, national frontier university, Sullana, Peru. Journal Article 2024 07 02 Nowadays, distributed generation (DG) systems with renewable energies are increasingly used to supply a portion of fluctuating electric loads. However, the intermittent nature of these systems leads to variable frequency and voltage, posing challenges for consistent energy supply. This paper addresses the challenges of variable frequency and voltage in DG systems powered by renewable energy sources. It models a hybrid wind-diesel system in MATLAB to optimize voltage and frequency control using a Particle Swarm Optimization (PSO) algorithm with Sine-Cosine Acceleration Coefficients (SCAC). Simulation results show that the optimized Proportional-Integral (PI) controller reduces frequency deviations to 0.21 Hz under a 1% active power disturbance and minimizes voltage deviations with support from a Static Var Compensator (SVC). The system stabilizes rapidly, achieving a settling time of 0.4 seconds, demonstrating the advantages of the SCAC-PSO approach over traditional methods. https://joape.uma.ac.ir/article_3626_ca3b0316fb96c0e4caed527de274858f.pdf

University of Mohaghegh Ardabili Journal of Operation and Automation in Power Engineering 2322-4576 12 Special Issue 2024 06 01 An Optimal Interaction Model of Reconfigurable Smart Distribution System and Parking Lot Operators 24 32 3627 10.22098/joape.2024.15467.2186 EN Muayad Nadhim Farhan Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran. Sajjad Gholshannavaz Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran. Tohid Ghanizadehbolandi Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran. Journal Article 2024 07 17 By leveraging the capabilities of Internet of Things (IoT) technology in conjunction with the smart grid concept and cloud-based data sharing, distribution system operators (DSOs) and parking lot operators (PLOs) can coordinate collaboratively to optimize techno-economic interactions. The integration of smart devices for data acquisition, monitoring, and control, along with cloud-based platforms for data storage, analysis, and collaboration, facilitates more efficient energy management, cost-effectiveness, and overall performance improvements. Building on these technological advancements, this study examines the daily operational planning of a smart distribution system in collaboration with PLOs, utilizing the Equilibrium Optimizer (EO) algorithm. Considering the potential of parking lots, the DSO aims to optimize both economic objectives and load leveling goals simultaneously, benefiting from structural reconfiguration for additional technical and financial gains. The model effectively incorporates constraints related to the expected and reliable operation of parking lots, as well as the security and radiality of the distribution system. By analyzing various objective functions and perspectives, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to determine the optimal state across all scenarios, achieving 60.3$, 5306.6 kW, and 716.8 kW for the first, second, and third objective functions, respectively. Numerical studies and simulation validations are conducted to evaluate the proposed model's performance, with results discussed in detail. https://joape.uma.ac.ir/article_3627_a11d1c09a7c9a6979663996494c54593.pdf

University of Mohaghegh Ardabili Journal of Operation and Automation in Power Engineering 2322-4576 12 Special Issue 2024 06 01 Adaptive Islanding Detection in Microgrids Using Deep Learning and Fuzzy Logic for Enhanced Stability and Accuracy 33 42 3633 10.22098/joape.2025.16153.2247 EN Ulugbek Kubayev Kimyo International University in Tashkent, Uzbekistan. Saodat Toshalieva Termez State University, Termez, Uzbekistan 0000-0002-0271-6534 Ilyos Ayubov Samarkand Institute of Economics and Service, Samarkand, Uzbekistan Murodov Farxodjon Samarkand State University Named after Sharof Rashidov, University Boulevard, Samarkand, Uzbekistan Qodirov Farrux Ergash Ugli Shahrisabz State Pedagogical Institute, Shakhrisabz, Kashkadarya, Uzbekistan Zaynalov Jakhongir Rasulovich Samarkand Institute of Economics and Service, Samarkand, Uzbekistan Tlegenov Baxitbay Nietbaevich Department of Software Engineering and the Digital Economy, Nukus Innovation Institute, Uzbekistan Abduxamid Abdumalikovich Bektemirov Samarkand State University Named after Sharof Rashidov, University Boulevard, Samarkand, Uzbekistan Aliyeva Susanna Seyranovna Samarkand Institute of Economics and Service, Samarkand, Uzbekistan Rustam Haydarovich Kushatov Samarkand State University Named after Sharof Rashidov, University Boulevard, Samarkand, Uzbekistan Madina Khurramova International School of Finance and Technology, Tashkent Region, Kibrai District, University Street, Tashkent, Uzbekistan Rano Davlatova Haydarovna Navoi State Pedagogical Institute, Uzbekistan Tulovov Erkinjon Tashkent State University of Economics, Tashkent, Uzbekistan Journal Article 2024 11 08 The growing complexity of microgrid operations, driven by the integration of renewable energy sources and distributed generation, has heightened the need for more advanced islanding detection methods. Traditional techniques, such as passive and active methods, often struggle with accuracy in these dynamic environments. Passive methods can result in high false detection rates as they rely on system parameters like voltage and frequency, which are sensitive to fluctuations. Active methods, while generally more accurate, can introduce disturbances into the system and are often less effective in low-power scenarios. These limitations pose significant challenges to maintaining the stability and integrity of microgrids, underscoring the need for innovative approaches. To address these challenges, this paper presents a novel approach that combines deep learning with fuzzy logic for adaptive control in microgrids. Deep learning facilitates precise real-time data analysis, enabling the system to accurately detect islanding events as they occur. Meanwhile, fuzzy logic provides adaptable decision-making, allowing the system to respond effectively to changing conditions. This integration significantly enhances detection accuracy and reduces error rates compared to traditional techniques, ensuring reliable performance throughout the day. By offering a more robust and flexible solution, the proposed method not only improves fault detection but also enhances overall system stability, making it a valuable contribution to microgrid management. This approach addresses the critical need for more effective islanding detection in increasingly complex microgrid environments, paving the way for more resilient and reliable energy systems. https://joape.uma.ac.ir/article_3633_0dc99d93768ba7ee9d67f98699450fe7.pdf

University of Mohaghegh Ardabili Journal of Operation and Automation in Power Engineering 2322-4576 12 Special Issue 2024 12 01 Machine Learning-based Fault Detection and Classification in microgrid 43 52 3767 10.22098/joape.2025.16912.2315 EN Azizov Tuxtamish Azamovich Vice-Rector for Scientific Affairs and Innovation, International School of Finance Technology and Science, Uzbekistan. Tulovov Erkinjon Tashkent State University of Economics, Tashkent, Uzbekistan Mansur Khalmirzaev Department of "Digital Economy", Samarkand State University Named after Sharof Rashidov, University Boulevard, 15, Samarkand, 703004, Uzbekistan. Otabek Mukhitdinov Kimyo International University in Tashkent , Shota Rustaveli Street 156, 100121, Тashkent, Uzbekistan. Nizamov Akhtam Numanovich Department of Network Economics, Samarkand State University Named after Sharof Rashidov, University Boulevard, 15, Samarkand, 703004, Uzbekistan. I.B. Sapaev Department Physics and Chemistry, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University, Tashkent, Uzbekistan. Scientific University of Tashkent for Applied Sciences, Street Gavhar 1, Tashkent 100149, Uzbekistan. Toshmirza Rakhmonov Department of Digital Economy, Samarkand State University Named after Sharof Rashidov, University Boulevard, 15, Samarkand, 703004, Uzbekistan. Yunusova Minovvarkhon Sabirovna Department of General Sciences and Culture, Tashkent State University of Law, Uzbekistan. Bobokulov Bakhromkul Mamatkulovich Department of Network Economics, Samarkand State University Named after Sharof Rashidov, University Boulevard, 15, Samarkand, 703004, Uzbekistan. Bobojonov Otabek Khakimboy Ugli Urganch State University, Uzbekistan. Ulugbek Tulakov Termez State University, Termez, Uzbekistan. Rаvshаn Kholikov Department of Fundamental Economic Science of the International School of Finance Technology and Science, Uzbekistan. Journal Article 2025 03 04 Fault Detection and Classification plays a vital role in maintaining the reliability and stability of microgrids, especially as they incorporate renewable energy sources and become more decentralized. Microgrids face a wide variety of faults, such as short circuits, line-to-ground faults, and other disturbances, which can negatively affect system performance. Traditional fault detection methods have primarily focused on False Data Injection and cyber-attacks, emphasizing vulnerabilities in communication infrastructure. However, this study addresses current faults within the electrical network, focusing on system stability and real-time fault detection in the absence of communication-related errors. In this work, machine learning techniques are employed to enhance fault classification accuracy. Partial Least Squares is used for feature selection to extract relevant statistical features from real-time current data collected from various microgrid components. By optimizing these features and applying them to machine learning models, the approach overcomes the limitations of conventional fault detection methods. The results show a significant improvement in fault classification performance, with up to 10% higher accuracy compared to traditional methods. Additionally, the use of data from neighboring microgrid components boosts the model's robustness, adaptability, and performance under varying operational conditions, contributing to a more resilient microgrid. This research introduces an innovative approach to fault detection in microgrids by combining machine learning and feature optimization, offering a more accurate, reliable, and efficient solution to ensure continuous energy supply and improve system stability under different fault scenarios. https://joape.uma.ac.ir/article_3767_95f403820775c927e5ccfe8a0fdfb282.pdf

University of Mohaghegh Ardabili Journal of Operation and Automation in Power Engineering 2322-4576 12 Special Issue 2024 12 01 Designing an Energy Management Control System in Hybrid Vehicles Using an Optimized Fuzzy Method 53 63 3942 10.22098/joape.2025.17112.2334 EN Sherzod Khalilov International School of Finance Technology and Science, Tashkent, Uzbekistan. Sitorabonu Abdiganiyeva International School of Finance Technology and Science, Tashkent, Uzbekistan. Abdullah Abed Hussein Department of Sciences, Al-Manara College for Medical Sciences, Maysan, Iraq. Jamal K. Abbas Al-Nisour University College, Nisour Seq. Karkh, Baghdad, Iraq. Ali Ashoor Issa College of Technical Engineering, National University of Science and Technology, Dhi Qar, Iraq. Hussam Abdali Abdulridui Al-Hadi University College, Baghdad, 10011, Iraq. Hassan Khalid Abozibid Al-Zahrawi University College, Karbala, Iraq. Sadiq Naama Henedy Mazaya University College, Nasiriyah, Iraq. Sardor Ganiyev International School of Finance Technology and Science, Tashkent, Uzbekistan. Shoh-Jakhon Khamdamov Mamun University, Khiva City, 220900, Uzbekistan. Samadova Nargiza Rasulovna Tashkent State University of Economics, Islam Karimov, Tashkent, Uzbekistan. Journal Article 2025 04 04 Improving fuel efficiency and enhancing the dynamic performance of hybrid electric vehicles are critical challenges in modern powertrain control design. This paper proposes a novel optimized fuzzy logic-based energy management strategy specifically developed for a Class B HEV. The main objective is to reduce fuel consumption and emissions while ensuring effective power distribution among key drivetrain components. The study introduces a two-stage methodology: first, an optimal sizing of the powertrain components—internal combustion engine, electric motor, and battery—is achieved using a genetic algorithm, ensuring the most efficient configuration for vehicle performance. Second, three different energy management strategies are implemented and compared: a conventional rule-based control, a standard fuzzy logic controller, and the proposed optimized fuzzy controller. Simulation results demonstrate that the optimized fuzzy strategy significantly improves fuel economy and emission performance compared to the other methods. Specifically, it achieves up to 20% better fuel efficiency than the rule-based controller while maintaining smooth power transitions. The study also highlights the impact of component sizing on control effectiveness, reinforcing the advantage of co-optimization of both sizing and control logic. The findings suggest that integrating intelligent optimization techniques such as GA with fuzzy control logic provides a superior approach to energy management in HEVs. This makes the proposed method a promising solution for next-generation hybrid vehicle applications aiming for both environmental sustainability and high performance. https://joape.uma.ac.ir/article_3942_769961b71f50b537059b6f4c9302be91.pdf

University of Mohaghegh Ardabili Journal of Operation and Automation in Power Engineering 2322-4576 12 Special Issue 2024 12 01 Enhancing Frequency Stability in Islanded Microgrids via Model Predictive Control 64 75 3941 10.22098/joape.2025.17113.2335 EN Anvar Ziyadullayevich Avlokulov Tashkent state University of Economics, Tashkent, Uzbekistan. Suhrob Ovlayev Tashkent state University of Economics, Tashkent, Uzbekistan. Hussein Basim Furaijl University of Al-Ameed, Karbala, Iraq. Muntadher Abed Hussein Department of Sciences, Al-Manara College, Maysan, Iraq. Hassan Khalid Abozibid Al-Zahrawi University College, Karbala, Iraq. Zainab Ali Nasir Al-Nisour University College, Nisour Seq. Karkh, Baghdad, Iraq. Tahir Toma Farhan Mazaya University College, Nasiriyah, Iraq. Sarmad Jaafar Naser Collage of Nursing, National University of Science and Technology, Dhi Qar, 64001, Iraq. Oybarchin Avlayeva Tashkent state University of Economics, Tashkent, Uzbekistan. Oybek Yulchiyev Tashkent state University of Economics, Tashkent, Uzbekistan. Dilorom Normuminova Tashkent state University of Economics, Tashkent, Uzbekistan. Journal Article 2025 04 04 This paper proposes a Model Predictive Control-based strategy for secondary load frequency control to enhance the dynamic performance of such systems. The proposed controller generates optimal control signals for dispatchable units to minimize frequency deviations induced by load and generation variability. A comprehensive microgrid model is developed, incorporating photovoltaic arrays, wind turbines, fuel cells, battery and flywheel energy storage systems, diesel generators, and electrolyzers. The dynamic behavior of each component is formulated using small-signal transfer functions, and the MPC is designed based on a constrained quadratic optimization problem that predicts and mitigates frequency deviations. Simulation results in MATLAB/Simulink demonstrate the superiority of the proposed MPC approach compared to conventional and intelligent controllers, including Ziegler–Nichols tuned PI, Fuzzy-PI, CPSO-PID, and CPSO-FOPID. The proposed controller achieved a maximum frequency deviation of 0.0052 pu, a settling time of 5.1 seconds, and an ITAE of 0.00024—outperforming all benchmarks in both steady-state and transient scenarios. Robustness under system parameter variations and load disturbances was also validated through five distinct case studies. The controller exhibits improved reliability, reduced stress on primary controllers, and better resilience to uncertainties. Future work will focus on implementing adaptive MPC algorithms, integrating machine learning-based disturbance predictors, and validating the control scheme using real-time hardware-in-the-loop platforms for enhanced applicability in hybrid AC/DC microgrids. https://joape.uma.ac.ir/article_3941_60226b46b8b2648bf1f8318a3a67c36d.pdf

University of Mohaghegh Ardabili Journal of Operation and Automation in Power Engineering 2322-4576 12 Special Issue 2024 12 01 Improving the Resilience of a Smart Microgrid Based on Energy Interactions Between Smart Homes 76 88 4036 10.22098/joape.2025.16269.2259 EN Saif Mohanad Maher Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran. Tohid Ghanizadeh Bolandi Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran. Sajjad Golshannavaz Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran. Journal Article 2024 11 28 The development of smart energy microgrids (MGs) and the influence of various types of distributed energy resources (DERs) on the demand side have increased the importance of addressing technical issues within MGs. In addition, the expansion of smart homes can improve issues related to the resilience of MGs during a disaster event. Therefore, this paper presents an efficient model for the optimal planning of energy management in a smart MG, taking into account the optimal energy interactions between smart homes and aiming to improve the MG’s resilience in a disrupted state, specifically with the integration of smart homes. In the suggested framework, interactions between smart homes and the microgrid operator (MGO) are considered. Through this model, the MGO obtains oversight of home energy management (HEM) systems, which enables it to improve resilience against MG disruptions. By facilitating energy management and interaction among smart homes, supported by DERs, the MGO can effectively bolster system resilience. In the mathematical modeling, smart homes include responsive loads (RLs), non-RLs, a bath-heating system (BHS), air-conditioning (AC), plug-in hybrid electric vehicles (PHEVs), energy storage systems (ESSs), and photovoltaic (PV) systems. Simulation analysis shows that operating the HEM system effectively, together with interlinked smart home energy exchanges, boosts the resilience of microgrids by 63.59%, using the General Algebraic Modeling System (GAMS). The results obtained are promising. https://joape.uma.ac.ir/article_4036_21e9ac23eca923a9046d999bd2d91d9a.pdf

University of Mohaghegh Ardabili Journal of Operation and Automation in Power Engineering 2322-4576 12 Special Issue 2024 12 01 Advanced Sliding Mode Control Tuned with a PSO Algorithm for Wind Power Systems: Performance and Efficiency Enhancement 89 99 4172 10.22098/joape.2025.17195.2350 EN Hayder Ali Hasan AL-Furat AL-Awsat Technical University, Najaf, Iraq. Journal Article 2025 04 16 Wind energy systems based on Doubly-Fed Induction Generators are increasingly deployed due to their efficiency and cost-effectiveness. However, ensuring high power quality, system stability, and robustness under wind variability remains a challenge. This paper addresses these issues by proposing an improved Integral Sliding Mode controller, whose gains are optimized offline using the Particle Swarm Optimization algorithm. The control scheme regulates generator speed, DC-link voltage, and stator currents through a combined proportional-integral-sliding control law, reducing chattering while enhancing dynamic performance. The proposed ISM controller was implemented on both the rotor-side and grid-side converters of a DFIG-based wind energy conversion system. Simulation results under varying and extreme wind conditions confirm the superiority of the ISM controller over conventional PI control. Notably, the ISM reduced generator speed tracking error by over 75%, achieving a Root Mean Square Error of 1.06 rad/s. It also lowered stator current Total Harmonic Distortion to below 0.85%, improved turbine efficiency to 93.6%, and minimized electromagnetic torque ripple by more than 60%. In extreme wind conditions, the controller maintained stability and compliance with grid standards, with only minor degradation in performance. Overall, the proposed ISM controller demonstrates strong potential for improving power quality, reliability, and efficiency in modern wind power systems. Future work will explore adaptive gain tuning and experimental validation to further enhance real-time applicability and practical deployment in field conditions. https://joape.uma.ac.ir/article_4172_939ebcfc9b2445515bbabed5779dd441.pdf