An earthquake is a sudden and destructive natural disaster that often results in unpredictable damage to human life and property. Investigating the effects of earthquakes on buildings and enhancing the seismic performance of structures is a crucial approach for mitigating severe damage during such events. One effective tool in testing the resistance of structures against earthquakes is the use of shaking tables. In this paper, the stabilization and control of earthquake simulator using a fuzzy sliding mode controller (FSMC) and adaptive unscented Kalman filter (AUKF)and adaptive extended Kalman filter (AEKF) is presented. These filters employ a recursive technique to effectively adjust the noise covariance by utilizing an adaptation method known as the steepest descent. In the proposed approach, the shaking table states are estimated using an accelerometer, encoder, and camera. These estimated states are then utilized by the AEKF/AUKF to stabilize and control the closed-loop system. A fuzzy sliding mode controller is designed to track the reference input, and eliminate external disturbances and noise. In the control of sliding mode, the occurrence of chattering in the control input is unavoidable. To mitigate this undesired chattering phenomenon, a fuzzy inference mechanism has been employed. The image processing approach has been utilized to measure the displacement online using the camera. The advantages of using the camera include not requiring direct contact with the table, as well as offering a low price and good accuracy.
In this paper, a single-phase boost AC-AC converter with inherent commutation and step-changed frequency operation is proposed. Distinct from conventional AC-AC converters, the proposed design achieves output voltage regulation utilizing one high-frequency switch. The inherent commutation feature of the converter negates the necessity for additional snubber circuits or complex commutation strategies, thereby simplifying the control method for adjusting the output voltage amplitude and frequency. The continuous input current of the proposed converter eliminates the requirement for a bulky LC filter. A straightforward and adaptable switching strategy is implemented to produce output frequency variations. The approach of preventing the conduction of the body diode in power MOSFETs mitigates issues associated with poor reverse recovery, enabling high-speed switching. The operating principles of the converter are elucidated across various modes of operation, with key equations derived and analyzed. To substantiate the validity of the proposed design, simulation results obtained using PSCAD/EMTDC software at frequencies of 25Hz, 50Hz, and 100Hz are presented.
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.
Insulator pollution levels are critical for ensuring the operational stability and safety of power transmission systems. Traditional methods for detecting pollution are often invasive, inaccurate, and time-consuming. To address these issues, this study investigates the application of Artificial Intelligence (AI), specifically Gradient Boosting Machines (GBM), to classify insulator pollution levels based on Partial Discharge (PD) characteristics. We utilize a combination of time-domain and frequency-domain features extracted from PD signals to train a predictive model. The results indicate that the proposed model achieves a high classification accuracy, averaging between 92% and 95% across various contamination levels. Furthermore, the study analyzes the model's sensitivity to environmental factors, including humidity and Hydrophobicity Class (HC), revealing important insights that could influence classification performance. By employing this AI-driven approach, we aim to significantly enhance the efficiency of power grid maintenance, ultimately contributing to the long-term stability and reliability of transmission systems. The findings from this research underscore the potential of AI in revolutionizing pollution assessment methods and optimizing maintenance practices in power infrastructure.
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.
Objective: In this paper, a trans-conductance amplifier based on Common Mode Rejection Ratio (CMRR) enhancement block is presented. The proposed block is capable of eliminating common mode signals at input stage. This feature improves the gain and CMRR of the amplifier substantially. The Cascode structure is also eliminated in proposed architecture, which resulted in favorably reduced power consumption due to low supply voltage requirements. Materials and Methods: The presented OTA is simulated in 180nm CMOS technology at Cadence Spectre environment with 1.5 v supply voltage proving it appropriate for low-voltage applications. The bias current of the proposed circuit is very low value of 3.9 μA. Results: Gain and phase margin for this block are achieved to be 83.96 dB and 61.68 degree, respectively. These results achieved while the circuit drive a 5pF load at its output. The power consumption of the proposed amplifier is interestingly very low value of 5.9 μW. Conclusion: It is interestingly concluded that the achieved specifications makes the block very much suitable for low-power applications.
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.
The present paper proposes a novel numerical approach for approximating solutions to optimal control problems with parabolic constraints. Utilizing Laguerre polynomials as a novel basis set, a method was developed to address a class of this problem. The employment of these basis functions in conjunction with the collocation method facilitates the transformation of optimal control problems governed by parabolic constraints into a system of nonlinear algebraic equations. The present study proposes an efficient discretization and transformation of complex optimal control problems governed by parabolic equations into lower-dimensional algebraic systems by leveraging the unique properties of Laguerre polynomials.Convergence analysis has been demonstrated to ascertain the optimal value approximations of the proposed method. In order to provide a comprehensive illustration of the reliability and applicability of the proposed method, two illustrative examples are presented. The findings underscore the efficacy and precision of the implemented methodology. This work makes a significant contribution to the field by offering a robust framework for solving complex parabolic control problems, thereby demonstrating the potential of spectral methods in the context of optimal control theory.
Calculating the current of the rotor bars in a squirrel cage induction motor (SCIM) using stator data is difficult, but it is very useful. With the calculation of the rotor bars’ current, analysis and investigation of some different parameters of the motor can be done, precisely. One of the faults faced by SCIM is the broken rotor bar (BRB) fault. The breakage of one or more bars causes a change in the healthy bars' current and the motor's behavior and parameters. This paper introduces the method of calculating the rotor bars’ current in the SCIM in two healthy and defective states (breakage of 1 bar and 2 adjacent bars) using the multiple-coupled circuit (MCC) model. In addition to currents, some important parameters, such as speed, magnetic field, etc., will be calculated. For validation of results, a two-pole SCIM with a nominal specification of 1.1 kW, 220/380 V, and 50 Hz is subjected to experimental testing. The results are confirmed by practical tests and simulations using the Ansys Maxwell software.
A new compact Ultra-Wide Band (UWB) arch shaped wide-slot antenna has been implemented for Microwave Imaging (MI) of breast cancer. It includes a fork-shaped strip and an arched slot ground, has a compact size of 16×20mm with a height of 1mm. The arched slot in the ground plate enhances the impedance bandwidth and the gain of the antenna. It has a bandwidth of 3.7 GHz to 18 GHz, that covers WLAN (5.4 GHz), X band (8-12 GHz), and Ku band (12-18 GHz) and having gain of 2.7 dBi to 6.3 dBi in the frequency ranges. The fidelity factor was computed for both E-plane and H-plane scenarios, indicating range of 0.922 to 0.975 for the E-plane across all angles. It has a small size, simple design, less signal distortion, a high gain of 6.3 dBi, the fractional bandwidth percentage of 131%. and efficiency of 93.7% at 6 GHz. It has reliable performances in terms of the fidelity factor at all angles compared to the most recent works. A microwave imaging simulation for breast tumor detection is performed to detect changes in the backscattering signal in the presence or absence of a tumor with a high dielectric inclusion. S11 is quite high when measured in front of the breast model and a noticeable difference in S21 exists between the scenarios with and without a tumor in the breast model. A significant variation in the transmission parameter exists across the entire frequency range, the scenarios with and without the presence of the tumor.
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.
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.
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.
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.
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.
This paper presents an optimized microwave rectifier circuit that integrates various couplers to enhance RF-to-DC conversion efficiency. A comprehensive theoretical analysis and performance evaluation of different microwave couplers are conducted to determine their impact on power distribution and impedance matching. The study demonstrates that incorporating couplers into the rectifier circuit effectively reduces reflected power over a broad input power range. Among the evaluated configurations, the rectifier incorporating a branch-line coupler (BLC) exhibits superior RF-to-DC efficiency over a wide range of operating frequencies, input power levels, and output loads, ensuring broad impedance matching. To validate the proposed design, a rectifier circuit based on the BLC is implemented and fabricated at 2.45 GHz. The prototype consists of two identical sub-rectifying networks connected to the two output ports of the coupler, with the isolated port grounded. Experimental results indicate that the rectifier consistently achieves efficiency levels exceeding 50% for input power levels ranging from 0 to 12.5 dBm. Additionally, the design maintains high efficiency across a frequency range of 2.16 to 2.96 GHz. These findings underscore the potential of BLC-based rectifiers for high-efficiency microwave power transmission systems, offering enhanced energy harvesting capabilities and improved system performance.
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.
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.
This paper introduces a high-gain, non-isolated DC-DC converter featuring a single switch and a straightforward driving circuit. The proposed topology employs a super-lift Lou converter beside inductor-based and capacitor-based voltage multiplier cell to enhance the voltage lift technique. The voltage gain is capable of exceeding tenfold at low duty cycles. The input current is kept continuous to decrease the current stress of the input filter capacitor and the current stresses are less than the input current. Moreover, the voltage stresses remain below the output voltage, notably, the maximum value on the components is kept under half of the output voltage, marking a significant advancement for high-output voltage applications. Additionally, a common ground for the load and input source is established and the electromagnetic interference (EMI) noise issues are managed. The proposed topology is discussed in the ideal and non-ideal modes. Furthermore, the converter’s required relations are discussed in the continuous and discontinuous conduction modes (CCM & DCM). Then the suitable applications are discussed. Finally, this topology is highly recommended for high-intensity discharge (HID) lamps, supported by experimental results from a prototype with a 12 V input, 312 V output, 0.1 A output current and a 50 percent duty cycle.
As power systems rapidly expand and the demand for uninterrupted power supply to network loads increases, ensuring the safe and stable operation of these systems has become crucially important. However, conducting dynamic stability assessments with detailed dynamic models is nearly impossible in today’s complex power networks. The introduction of Phasor Measurement Units (PMUs) has paved the way for new stability evaluation techniques that rely on real-time measurement data. A common limitation of most measurement-based techniques is their vulnerability to noise in the data. While some newer methods offer improved noise resistance, they are often hindered by high computational demands and slow processing times, limiting their practical use. This paper developed a measurement-based method that uses power spectral density (PSD) and cross-spectral density (CSD) to achieve a more precise estimation of low-frequency oscillations in power systems. Simulation results on the IEEE 14-bus and 39-bus test systems, tested under both noisy and noise-free conditions, show that the proposed method yields more accurate frequency and oscillation shape estimates, even when measurement noise is present. Additionally, the Prony algorithm, a well-known measurement-based method, is also implemented, and its high sensitivity to noisy data is demonstrated.
