University of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614420261201Enhancing State Estimation Accuracy in Distribution Networks: An Optimized Algorithm for Strategic Meter Placement241248395410.22098/joape.2025.15292.2163ENAliFarahani ShahvarooghiDepartment of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran.EhsanHeydarian-ForushaniDepartment of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran.MehdiZeraatiPower Systems Operation and Planning Research Department, Niroo Research Institute (NRI), Shahrak Ghods, Tehran, Iran.SaeedHasanzadehDepartment of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran.Journal Article20240618Accurate state estimation is crucial for the effective control and management of power grids, as it provides a comprehensive understanding of voltage magnitude and phase angle at network buses. Incorrect estimations may lead to damaging decisions and network collapse. This paper addresses the significance of precise state estimation in distribution networks and proposes an efficient algorithm for optimal measurement device allocation, aiming to minimize estimation errors. The algorithm considers both investment and technical constraints, utilizing an optimal alternative current (AC) power flow model that eliminates the need for exact values of active and reactive load demands. The proposed method identifies optimal locations for installing a specific number of phasor measurement units (PMUs) across the network. The application of the algorithm to 33-bus and 69-bus test systems demonstrates its effectiveness in enhancing state estimation accuracy. Results reveal that optimizing the number and location of measurement devices significantly improves outcomes. A comparative analysis with the conventional weighted least squares (WLS) algorithm underscores the applicability of the proposed model, particularly in distribution networks with limited measurement devices. The proposed method formulates optimal meter placement problems in distribution networks based on an optimal power flow model, which has a superior performance in both accuracy and convergence without needing to exact nodal demands for state estimation. This research contributes to the advancement of state estimation procedures, offering a practical approach to enhance accuracy and reliability in power grid management.https://joape.uma.ac.ir/article_3954_7252291ba23d2509cb2f39795276ab2b.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614420261201Design and Analysis of BLDC Motor with Novel Hybrid Approach for Cogging Torque Reduction249256378810.22098/joape.2025.15392.2182ENTanujJhankalDepartment of Electrical, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India.Amit N.PatelDepartment of Electrical, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India.Journal Article20240709Radial flux brushless DC motors with surface-mounted permanent magnets offer several advantages, but they are also characterized by a significant drawback: high cogging torque. Mitigating cogging torque is a critical challenge in the design of brushless direct current motors, particularly in applications such as electric vehicles. This article presents three approaches to reduce cogging torque in radial flux permanent magnet brushless motors: teeth edge inset width variation, magnet tip depth variation, and a hybrid approach combining both techniques. The teeth edge inset width variation method involves reducing the inset width of the stator teeth, while the magnet tip depth variation approach addresses the depth of the magnet's edge inset on the rotor core surface. The hybrid approach integrates changes to both the stator teeth and rotor magnet poles. Additionally, the study investigates how these approaches affect the average torque and flux density distribution. Finite element analysis was conducted to simulate and analyze a 1000 W, 510 rpm radial flux brushless DC motor. The results show that the proposed methods effectively reduce cogging torque, demonstrating their potential to enhance the performance of these motors in practical applications.https://joape.uma.ac.ir/article_3788_3c17076014bbecb9e2854a6ba170413c.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614420261201Analysis of Flashover Voltage of Porcelain and Glass Insulators under Different Temperatures with Various Levels of Pollution and Humidity257266424810.22098/joape.2025.15614.2201ENRezaZahedi KhatirFaculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.MohammadMirzaieFaculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.HassanMahdaviFaculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.Journal Article20240811Insulators of overhead transmission lines are continuously exposed to various environmental conditions. Factors such as pollution, humidity, temperature, and electrical stress negatively affect their performance. Environmental stresses can reduce surface resistance, increase leakage current, and ultimately lead to the flashover voltage of insulators. As a result, overhead transmission lines and some electrical equipment become disconnected from the power network. This can cause interruptions in energy transmission and reduce electrical grid reliability. In this paper, the flashover voltage of porcelain and glass insulators with artificial/natural pollution, as well as under clean conditions (non-pollution) and different levels of humidity and temperature, is measured, and the results are evaluated and analyzed. The experimental findings show that there is a mathematical correlation between the flashover voltage of the polluted insulators and the various levels of humidity and temperature. The coefficients of the model are determined in such a way that the results of the presented model are in good agreement. Besides, the experimental results indicate that the increase in temperature has a significant effect on the behavior of the insulators examined under different pollution and humidity conditions. The results show that the flashover voltage of the insulators decreases between 4% and 41% under constant pollution and humidity with different temperatures. It was also revealed that it is possible to estimate the flashover voltage of the insulators under different humidities using the correction factor of the flashover voltage.https://joape.uma.ac.ir/article_4248_d9dc55c13e95fa1fbc347de2800a758d.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614420261201Coordinated Distributed Security Constrained Unit Commitment with Frequency Deviation Control for High Renewable Penetration Low Inertia Power Grids267274425910.22098/joape.2025.15739.2210ENShahlaMohammad Hosseini MirzaeiFaculty of Engineering and Technology, Shahrekord University, Shahrekord, Iran.AbdorrezaRabieeFaculty of Engineering and Technology, Shahrekord University, Shahrekord, Iran.SamadTaghipour BoroujeniFaculty of Engineering and Technology, Shahrekord University, Shahrekord, Iran.Journal Article20240830 Among different renewable resources, wind power and solar photovoltaics (PVs) have the most desirable technical and economic prospects. However, the significant penetration of such low inertia power plants in power grids causes a decrease in the system's total inertia and thereby leads to a decrease in system frequency deviation (SFD) more than the acceptable range. This paper presents a distributed (D-SCUC) problem in which system frequency deviation is considered as a constraint to prevent system frequency deviation more than the pre-determined level. With the proposed method, the system partitions into several areas wherein a SCUC problem is separately solved, and the analytical target cascading (ATC) method is used to coordinate these sub-systems. To avoid the masking effect, a modified penalty function is used. A simple 6-bus network and the modified IEEE RTS 24-bus test system are used as the case study. The results show the effectiveness of the D-SCUC technique, especially in large power systems, and therefore, the system operator's concern about the system frequency is relieved.https://joape.uma.ac.ir/article_4259_a67645ae2c0243e631a523aedb845d19.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614420261201Partial Shading Detection of Solar Panels Using Ensemble Bagged Trees Algorithm275285419610.22098/joape.2025.15344.2172ENPoorvaSharmaDepartment of Electrical Engineering, National Institute of Technology, Raipur, CG, India.AnamikaYadavDepartment of Electrical Engineering, National Institute of Technology, Raipur, CG, India.Journal Article20240625The adoption of photovoltaic (PV) energy is growing rapidly, leading to the installation of numerous solar power plants to meet rising electricity demand. Among the various operational challenges, partial shading significantly reduces the power output of PV panels. During routine maintenance, panels are typically cleaned to remove debris such as dust, dirt, or bird droppings—common causes of shading. However, shading severity varies across panels, especially in large PV installations, making uniform cleaning inefficient. To address this, the paper proposes a machine learning-based methodology for detecting and quantifying partial shading at the panel level. By analyzing power output, irradiance, and ambient temperature data, the proposed approach identifies the panels most affected by shading and classifies the severity into low, medium, and high categories. This enables smart maintenance prioritization and early fault prediction, preventing issues such as hotspots and module degradation while reducing operational costs. In a comparative study of eleven Machine Learning models, the ensemble bagged trees algorithm achieved the best performance 90% accuracy in identifying the most affected panels and 93.5% accuracy in classifying shading levels. The proposed solution is well-suited for real-time deployment in solar parks, offering an effective tool for predictive maintenance and optimized plant operations.https://joape.uma.ac.ir/article_4196_66c19c7d69035ef148db265adb98ea94.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614420261201Virtual Synchronous Generator-Based Interlinking Converter for Enhanced Power Sharing and Quality in Islanded Hybrid AC/DC Microgrids286296417610.22098/joape.2025.16064.2241ENMojtabaEldoromiFaculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, IranAli AkbarMoti BirjandiFaculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, IranNimaMahdian DehkordiFaculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, IranJournal Article20241023This paper presents a novel power management scheme for hybrid AC/DC microgrids (HMGs), focusing on improving power sharing, voltage control and system stability using Virtual Synchronous Generator (VSG)-based interlinking converters (ILCs). The proposed approach integrates a multi-layered control framework that employs adaptive droop control to coordinate the AC and DC subsystems in real time, responding to load variations and bidirectional power flow. The system is equipped with two ILCs: the first (ILC1) facilitates interlinking and energy exchange between the AC and DC sub-grids, while the second (ILC2), integrated with a bidirectional DC/DC converter, manages the DC-link voltage and enables efficient power transfer. A Battery Energy Storage System (BESS) is placed between ILC2 and the DC-link to stabilize power fluctuations. The VSG method, leveraging virtual inertia, is employed to counteract the negative impacts of fluctuating renewable energy sources, such as wind power and photovoltaic (PV), thereby enhancing the system's dynamic behavior and stability. This strategy mimics synchronous generator inertia, ensuring reliable frequency and voltage regulation. Simulation studies in MATLAB/Simulink validate the effectiveness of the proposed scheme, demonstrating significant improvements in power sharing efficiency and power quality.https://joape.uma.ac.ir/article_4176_785c5b0b8ee6a27d007d8e159642f561.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614420261201Improving Power Quality in a Microgrid through Control of Active and Reactive Power Output from Inverter-Based Sources297307417710.22098/joape.2025.15894.2223ENMoeinGanjianDepartment of Electrical and Biomedical Engineering, Mazandaran University of Science and Technology, Babol, Iran.AliGhasemi-MarzbaliDepartment of Electrical and Biomedical Engineering, Mazandaran University of Science and Technology, Babol, Iran.Journal Article20240922The increasing integration of inverter-based sources in microgrids demands advanced control strategies to maintain power quality by mitigating harmonics and distortion. This study proposes an enhanced control system for grid-connected inverters, integrating a proportional-integral (PI) controller in the synchronous rotating frame with a repetitive control (RC) compensator. A particle swarm optimization (PSO) algorithm is employed to optimize the controller parameters, ensuring effective harmonic suppression. The proposed PI+RC controller is evaluated through simulation studies under various scenarios, including linear and nonlinear loads as well as grid voltage distortion. Results demonstrate a significant reduction in total harmonic distortion (THD), with the proposed controller achieving a reduction from 37.5% to 5.6% under nonlinear load conditions and from 49.5% to 5% when both nonlinear loads and voltage distortion are present. Additionally, the proposed method effectively stabilizes the active and reactive power outputs, surpassing conventional PI controllers.https://joape.uma.ac.ir/article_4177_7284ab61eedeb93566b1fc37efb63422.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614420261201Deep Learning- Model Predictive Control for Load Frequency Control of Microgrids with Electric Vehicles308317417510.22098/joape.2025.16193.2250ENFarhadAmiriDepartment of Electrical Engineering, Tafresh University, Tafresh 39518-79611, Iran.SajadSadrDepartment of Electrical Engineering, Tafresh University, Tafresh 39518-79611, Iran.Journal Article20241114In an islanded microgrid, ensuring frequency stability is essential for reliable system operation. Distributed generation (DG) and electric vehicles (EVs) make frequency stability challenging in an islanded microgrid because they increase generation and load variability and reduce system inertia. Load frequency control (LFC) is mainly used to enhance the frequency response of these types of microgrids. In addition, uncertainty in parameters and perturbations strongly impact the application of LFC. To address these challenges, this paper presents an LFC method for islanded microgrids using model predictive control (MPC) based on deep learning. The deep learning technique is used to enhance MPC controller performance against uncertainties and disturbances. The proposed method is validated through experiments, especially in the presence of disturbances and parameter instability. It is then compared with other methods, including linear active disturbance rejection control (LADRC), fractional-order PID (FOPID), and several others. The results show that the MPC method based on deep learning outperforms these approaches in terms of disturbance rejection, frequency response improvement, and system inertia enhancement.https://joape.uma.ac.ir/article_4175_37681025ebd9518e8b9483817ff26f0b.pdf