Research Articles
Control
Sepehr Shakibi; Amir Mohammad Farahani; Mohsen Hamzeh
Abstract
Photovoltaic (PV) systems are a backbone of the infrastructure of renewable energy with its usage growing significantly. Early fault detection of these systems being essential to enhance their reliability and efficiency. Despite the development of fault diagnosis methods of PV system promoted by ...
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Photovoltaic (PV) systems are a backbone of the infrastructure of renewable energy with its usage growing significantly. Early fault detection of these systems being essential to enhance their reliability and efficiency. Despite the development of fault diagnosis methods of PV system promoted by machine learning models such as ensemble learning, support vector machine and neural networks, challenges in achieving high accuracy and generalization persist. In this paper, propose a deep learning method based on a ResNet architecture for reliable and efficient fault detection, including the following categories: Normal Operation, Short-Circuit, Degradation, Open Circuit, and Shadowing. Also devise a new learning rate schedule(LRS), which considerably improves the training dynamics and enables a 63% improvement in model performance. The suggested method has excellent performance achieves 99.8% accuracy throughout the training, validation and testing phases. The results obtained showcase the potential of ResNet-based architectures, in addition to prowess in adaptive learning rate strategies, at enhancing the reliability of photovoltaic systems through scalable and precise fault diagnosis.
Research Articles
Industrial Electronics
Mohsen Rahmani Haredasht; S. Masoud Barakati; Mohammad Bagheri Hashkavayi
Abstract
The modular multi-level converter (MMC) is widely recognized as one of the most promising structures for high-voltage and high-power applications due to its fully modular design, lack of bulky output filters, and independence from multiple DC sources. However, the large number of flying capacitors within ...
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The modular multi-level converter (MMC) is widely recognized as one of the most promising structures for high-voltage and high-power applications due to its fully modular design, lack of bulky output filters, and independence from multiple DC sources. However, the large number of flying capacitors within the MMC architecture presents significant challenges to system reliability, particularly in maintaining accurate voltage balance. Traditional methods to ensure voltage balancing require numerous voltage sensors, which increase both the cost and complexity of the system, potentially undermining its reliability. This paper introduces an innovative capacitor voltage balancing method aimed at enhancing the reliability of the MMC. By using a single voltage sensor for both half-bridge submodules (HB-SM), the proposed method simplifies the system architecture while ensuring precise voltage estimation through a simple sensor configuration. Furthermore, the unique placement of the voltage sensor facilitates a capacitance monitoring technique that accurately assesses the health of the capacitors, thereby further enhancing the converter's reliability. The proposed method's efficiency has been confirmed by simulation and experimental investigations in various scenarios.
Research Articles
Power systems
AmirHossein Babaali; Mohammad Taghi Ameli
Abstract
Short-term voltage stability (STVS) varies with operating conditions of power networks, making its accurate assessment a critical challenge. This paper investigates a multi-class, data-driven approach to STVS evaluation. A dynamic index is employed to categorize voltage magnitude variations into three ...
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Short-term voltage stability (STVS) varies with operating conditions of power networks, making its accurate assessment a critical challenge. This paper investigates a multi-class, data-driven approach to STVS evaluation. A dynamic index is employed to categorize voltage magnitude variations into three classes: stable, alert, and unstable. A significant obstacle in data-driven methods is missing measurement data, typically caused by sensor failures or communication delays. To address this issue, we propose two complementary solutions. First, a Bidirectional Gated Recurrent Unit (Bi-GRU) network with an attention mechanism is designed to recover data loss due to sensor failures. This method leverages both temporal trends and historical system information to reconstruct missing values with high accuracy. Second, a variable-length sliding window (VLSW) algorithm combined with a Bi-GRU is introduced to mitigate data loss arising from communication delays. The VLSW algorithm enhances data diversity and enables fast recovery. Simulation results on IEEE 39-bus and IEEE 118-bus test systems demonstrate that the proposed framework effectively identifies multi-class STVS under missing data conditions and remains robust against long-range data losses. Finally, validation on a real-world local network further confirms the practicality and robustness of the proposed approach.
Research Articles
Control
Ramezan Havangi; Fatemeh Karimi
Abstract
Accurate estimation of State of Charge (SOC) is essential for the efficiency, safety, and durability of battery-powered devices, playing a vital role in Battery Management Systems (BMS). This paper introduces an innovative method combining the Adaptive Robust Square Root Unscented Kalman Filter (ARSRUKF) ...
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Accurate estimation of State of Charge (SOC) is essential for the efficiency, safety, and durability of battery-powered devices, playing a vital role in Battery Management Systems (BMS). This paper introduces an innovative method combining the Adaptive Robust Square Root Unscented Kalman Filter (ARSRUKF) with Recursive Least Squares (RLS) to enhance SOC estimation accuracy and robustness. By maintaining semi-positive definite covariance matrices, the proposed method ensures numerical stability, avoiding issues commonly encountered in traditional techniques. A key feature of the ARSRUKF is its direct computation of the square roots of covariance matrices, preserving their symmetry and positive definiteness while increasing computational efficiency. Unlike conventional filters, such as the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), the ARSRUKF operates effectively without prior knowledge of noise statistics, accommodating non-Gaussian noise or uncertain noise characteristics in real-world scenarios.To further improve performance, an Adaptive Neuro-Fuzzy Inference System (ANFIS) dynamically tunes noise covariances in real-time, adapting to changes in operating conditions like temperature variations, battery aging, and load shifts. Extensive experimental results highlight the superior performance of the ARSRUKF compared to the EKF and UKF, particularly in conditions with unknown or varying noise statistics. This approach demonstrates significant advancements in SOC estimation accuracy, stability, and consistency. The proposed method has broad potential applications in electric vehicles, renewable energy storage, and portable electronics, offering a robust and efficient solution for advanced battery systems.
Research Articles
Communication
Mahdiye Mohammadi; Hadi Zayyani; Mehdi Bekrani
Abstract
Target localization in wireless sensor networks (WSNs) is essential for various applications. This study investigates received signal strength (RSS)-based localization in the presence of malicious anchor nodes that intentionally alter signal power levels to mislead the fusion center (FC) and degrade ...
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Target localization in wireless sensor networks (WSNs) is essential for various applications. This study investigates received signal strength (RSS)-based localization in the presence of malicious anchor nodes that intentionally alter signal power levels to mislead the fusion center (FC) and degrade positioning accuracy. To address this challenge, we adopt a Maximum a Posteriori (MAP) estimator, which estimates the target location even when the path loss exponent is unknown. We show that the MAP estimation method can estimate the WSN unknown parameters, including the path loss exponent, the distance between the target node and anchor nodes, and the received signal strength. Simulation results demonstrate that the MAP method achieves lower localization errors than other competing approaches when the Signal-to-Noise Ratio (SNR) exceeds 10 dB, although it entails higher computational complexity in terms of simulation run time. The proposed approach is particularly efficient in applications in transportation, military operations, security, smart industries, and mapping.
Research Articles
Control
Violet Farhad; Seyed Mehdi Mirhosseini-Alizamini
Abstract
This paper introduces a new application of variable gain sliding mode control (VGST) to the air supply system of a proton exchange membrane fuel cell (PEMFC), which is crucial for its performance and longevity. The air supply system comprises a centrifugal compressor, a DC-DC converter, and a fuel cell ...
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This paper introduces a new application of variable gain sliding mode control (VGST) to the air supply system of a proton exchange membrane fuel cell (PEMFC), which is crucial for its performance and longevity. The air supply system comprises a centrifugal compressor, a DC-DC converter, and a fuel cell stack, forming a complex and nonlinear system with multiple inputs and outputs. The VGST method adjusts the control gain based on the system state and the sliding level and employs a cascade structure to regulate the excess oxygen ratio and the compressor airflow. The main goals of VGST are to control the PEMFC output voltage and power under various load conditions and uncertainties and to optimize the excess oxygen ratio (λ_(O_2 )) to avoid oxygen depletion and membrane damage. The stability and robustness of the proposed controller are verified by Lyapunov theory and its performance is superior compared to other controllers such as variable gain closed-loop control and constant gain sliding mode control (single loop and cascade). The controller is validated by simulation and experimental data and demonstrates that it can enhance the efficiency and reliability of the PEMFC system. The variable gain controller of the cascade structure was also tested under noisy and uncertain conditions to further confirm its desired performance and showed that it could cope well with adverse situations and achieve the control objectives.
Research Articles
Optimization
Maryam Alipour; Samaneh Soradi-Zeid
Abstract
This paper deals with a general form of fractional optimal control problems involving variable-order fractional integro differential equation using orthonormal Laguerre wavelets expansions. By effectively employing these functions, product variable-order operational matrices have been ...
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This paper deals with a general form of fractional optimal control problems involving variable-order fractional integro differential equation using orthonormal Laguerre wavelets expansions. By effectively employing these functions, product variable-order operational matrices have been obtained. By using these fractional operational matrices and collocation points, the study transforms the original continuous-time optimal control problems of variable-order fractional integro-differential equations into a system of linear or non-linear algebraic equations. Attempts have been made to use the collocation method with a joint application of Lagrange multiplier technique, to obtain the approximate cost function based on determining the state and control functions. The main components for applying these wavelets is to have viable solutions due to their orthogonality. In addition, the convergence analysis is presented with respect to the operational matrices of this scheme. Simulation results indicate that the proposed method works well and provides satisfactory results with regard to accuracy and computational effort.
Research Articles
Control
Mohammad Moodi; Mohammad Reza Ramezani-al
Abstract
Accurate state-of-charge (SOC) estimation is essential for the safe and efficient operation of lithium-ion batteries in electric vehicles and energy storage systems. This paper proposes a fusion-based SOC estimation method that integrates two extended Kalman filters (EKFs), each paired with a distinct ...
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Accurate state-of-charge (SOC) estimation is essential for the safe and efficient operation of lithium-ion batteries in electric vehicles and energy storage systems. This paper proposes a fusion-based SOC estimation method that integrates two extended Kalman filters (EKFs), each paired with a distinct open-circuit voltage (OCV)–SOC model. The fusion strategy, grounded in Bayesian probability and residual error analysis, dynamically assigns weights to each model’s output, ensuring that the most appropriate model contributes predominantly to the final SOC estimate at any given moment. The proposed framework utilized a second-order equivalent circuit model (ECM) and estimates parameters online via a variable forgetting factor recursive least squares (VFFRLS) algorithm. Simulation results under LA92 and UDDS driving cycles demonstrate that the method achieves superior accuracy and robustness, reducing the maximum estimation error by up to 26% and RMSE by over 10% compared to conventional EKF approaches. These findings highlight the method’s effectiveness and adaptability for real-time battery management applications.
Research Articles
Industrial Electronics
Mustafa Okati; Mohammad Osmani-Bojd
Abstract
This study introduces a non-isolated semi-quadratic DC/DC buck-boost converter designed to enhance performance. Derived from a conventional CUK converter, the proposed topology operates in two distinct modes: one providing a semi-quadratic voltage gain of D(2−D)/(1−D)2 and the other offering ...
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This study introduces a non-isolated semi-quadratic DC/DC buck-boost converter designed to enhance performance. Derived from a conventional CUK converter, the proposed topology operates in two distinct modes: one providing a semi-quadratic voltage gain of D(2−D)/(1−D)2 and the other offering a gain of D/(1−D). In addition, the proposed structure features constant input and output currents due to the presence of inductive filters at the input and output ports, which reduces the current stress on the capacitors at the output port and lowers the voltage ripple. Under steady-state conditions, the continuous conduction mode efficiency of the converter and small-signal modeling were analyzed by considering the effects of parasitic resistance. The results demonstrated lower total switching device power and a reduced component count than other buck-boost converters in dual-mode operation. The proposed converter was simulated using PLECS software. The experimental results were consistent with theoretical predictions due to their high efficiency and applications, particularly in photovoltaic systems and fuel cells.
Research Articles
Power systems
Seyed Fariborz Zarei; Saeed Hasanzadeh
Abstract
This paper presents a comprehensive approach to the design of Resolution Bandwidth (RBW) filters specifically for Electromagnetic Interference (EMI) applications. We propose a mathematical modeling method that accurately captures the characteristics of standard RBW filters, which are essential for precise ...
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This paper presents a comprehensive approach to the design of Resolution Bandwidth (RBW) filters specifically for Electromagnetic Interference (EMI) applications. We propose a mathematical modeling method that accurately captures the characteristics of standard RBW filters, which are essential for precise EMI noise measurements. The proposed approach utilizes paired complementary second-order filters with symmetrical cutoff frequencies to ensure compliance with CISPR-16 standards. The methodology underscores the importance of aligning theoretical models with real-world filter behavior, ensuring that the resulting models are both accurate and reliable. By establishing a robust framework for RBW filter design, the method enables optimized EMI system performance and the implementation of appropriate filtering solutions. Validation is carried out through simulations using a 150 kHz signal with a dynamically ramped amplitude increase, demonstrating high accuracy and strong performance under both transient and steady-state conditions. Despite the challenging test scenarios, the results confirm that the proposed filter model remains accurate and effective even under worst-case EMI conditions.
