Iranian Journal of Electrical and Electronic Engineering

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Power transformers are key components in electrical power supplies and their failure could cause severe consequences on continuity of service and also generates substantial costs. Identifying problems at an early stage, before catastrophic failure occurs, is a great benefit for reliable operation of power transformers. Frequency Response Analysis (FRA) is a new, well-known and powerful diagnostic test technique for transformers which could find mechanical as well as electrical faults such as detection and positioning of winding short circuit, winding movement, loss of clamping pressure, aging of insulation, etc. Yet there are several practical limitations to affect the accuracy and ease using this test as a regular condition monitoring technique in the field that many of them originated from noise and measuring errors. This paper purposes a transformer automated self diagnosis system can be installed on every power supply as a part of SCADA to extract FRA graphs from transformers and offers high repeatability which is a great benefit for FRA test. This is the first time that KALMAN Filter will be use in order to eliminate narrow-band and wide-band noises from FRA graphs that ends up not only smoothed measurement but also rate of changes that is so valuable in decision making and scheduling for transformers maintenance. So we will have an intelligent system which is able to predict the future of transformer using experience of not only own self but also all the transformers in an integrated network.

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This paper presents a novel solution method for joint energy and Spinning Reserve (SR) dispatch problem. In systems in which the Lost Opportunity Cost (LOC) should be paid to generators, if the LOC is not considered in the dispatch problem, the results may differ from the truly optimum solution. Since the LOC is a non-differentiable function, including it in the formulation makes the problem solving process to be time-consuming and improper for real time applications. Here, the joint energy and SR dispatch problem considering the LOC in the objective function is reformulated as a Linear Programming (LP) problem which its solving process is computationally efficient. Also, with reliance on the performance of LP problem solving process, an iterative algorithm is proposed to overcome the self-referential difficulty arising from dependence of the LOC on the final solution. The IEEE 30-bus test system is used to examine the proposed solution method.

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In this paper, an approach is proposed for accurate locating of single phase faults in transmission lines using voltage signals measured at one-end. In this method, harmonic components of the voltage signals are extracted through Discrete Fourier Transform (DFT) and are normalized by a transformation. The proposed fault locator, which is designed based on Random Forests (RF) algorithm, is trained based on these normalized harmonic components. RF algorithm has the capability of learning patterns with a large number of features. The proposed approach only requires voltage signals measured at one-end hence, there are not problems of transmitting and synchronization of two-end data. In addition, current measurement is not required and the proposed approach is sheltered against current transformer errors and its saturation. No need for very high sampling frequency is another advantage of the proposed approach. Numerous tests carried out on a sample system indicate that accuracy of the proposed fault locator is secure against changing fault location, fault inception angle, fault resistance, and magnitude and direction of pre-fault load current. An average of 0.11% is obtained for the fault locating test errors.

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Visible Light Communication, a key optical wireless technology, offers reliable, high-bandwidth, and secure communication, making it a promising soloution for a variety of applications. Despite its many advantages, optical wireless communication faces challenges in medical environments due to fluctuating signal strength caused by patient movement. Smart transmitter structures can improve system performance by adjusting system parameters to the fluctuating channel conditions. The purpose of this research is to examine how adaptive modulation performs in a medical body sensor network system that uses visible light communication. The analysis focuses on various medical situations and investigates machine learning algorithms. The study compares adaptive modulation based on supervised learning with that based on reinforcement learning. The findings indicate that both approaches greatly improve spectral efficiency, emphasizing the significance of implementing link adaptation in visible light communication-based medical body sensor networks. The use of the Q-learning algorithm in adaptive modulation enables real-time training and enables the system to adjust to the changing environment without any prior knowledge about the environment. A remarkable improvement is observed for photodetectors on the shoulder and wrist since they experience more DC gain.

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Photovoltaic (PV) energy is increasingly recognized as an environmentally friendly source of renewable energy. Integrating PV systems into power grids involves power electronic inverters, adding complexity and evolving traditional grids into smarter systems. Ensuring the reliability of decentralized PV generation is crucial, particularly as PV systems are often exposed to extreme weather conditions. This study investigates the impact of temperature and solar radiation on the performance of a PV array, focusing on key characteristics such as open-circuit voltage (V_OC), short-circuit current (I_SC), and maximum power (P_MAX). Using PSCAD/EMTDC simulations, the study analyses these characteristics under varying temperatures (5°C to 45°C) and radiation levels (200 W/m² to 1200 W/m²). Results indicate that V_OC increases with higher irradiance but decreases with higher temperatures. I_SC increases with both higher radiation and temperature, while P_MAX is optimized at high irradiance and low temperatures. The impulse withstand voltage (V_imp), a critical factor for PV system reliability, is assessed according to the PD CLC/TS 50539-12 standard. Findings reveal that at low temperatures and high radiation, the V_imp requirement is highest, emphasizing the need for robust voltage protection in PV systems. These insights underscore the importance of considering local climate conditions and implementing effective thermal management to enhance the performance and reliability of PV systems.

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This study introduces a pioneering method to enhance the efficiency and effectiveness of three-phase five-level reduced switch cascaded H-bridge multilevel inverters (CHB MLI) by employing the Henry Gas Solubility Optimization (HGSO) algorithm. Targeting the selective harmonic elimination (SHE) technique, the research emphasizes the optimization of switching angles to significantly reduce total harmonic distortion (THD) and align the fundamental output voltage closely with the reference voltage. Central to this exploration are three distinct objective functions (OFs), meticulously designed to assess the HGSO algorithm’s performance across various modulation indices. Simulation results, facilitated by PSIM software, illustrate the impactful role these objective functions play in the optimization process. OF1 demonstrated a superior ability in generating low OF values and maintaining a consistent match between reference and fundamental voltages across the modulation index spectrum. Regarding the reduction of THD, it is crucial to emphasize that all OFs can identify the most effective switching angle to minimize THD and eliminate the fifth harmonic to a level below 0.1%. The findings highlight the potential of HGSO in solving complex optimization challenges within power electronics, offering a novel pathway for advancing modulation strategies in CHB MLIs and contributing to the development of more efficient, reliable, and compact power conversion systems.