Search Results (220)

Nonlinear narrowband low-frequency noise generated during tractors’ operation significantly affects operators’ comfort and working efficiency. Traditional linear active noise control algorithms often struggle to effectively suppress such complex acoustic disturbances. To address this challenge, this paper proposes a momentum-enhanced Volterra filter-based active noise control (ANC) algorithm that improves both the modeling capability of nonlinear acoustic paths and the convergence performance of the system. The proposed approach integrates the nonlinear representation power of the Volterra filter with a momentum optimization mechanism to enhance convergence speed while maintaining robust steady-state accuracy. Simulations are conducted under second- and third-order nonlinear primary paths, followed by performance validation using multi-tone signals and real in-cabin tractor noise recordings. The results demonstrate that the proposed algorithm achieves superior performance, reducing the NMSE to approximately −35 dB and attenuating residual noise energy by 3–5 dB in the 200–800 Hz range, compared to FXLMS and VFXLMS algorithms. The findings highlight the algorithm’s potential for practical implementation in nonlinear and narrowband active noise control scenarios within complex mechanical environments. Full article

The quality of a grid-injected current in LCL-type grid-connected inverters (GCI) degrades under ultra-weak grid conditions, posing serious challenges to the stability of the GCI system. For this purpose, the sliding mode control (SMC) approach has been utilized to integrate DC energy seamlessly into the grid. The control performance of a GCI equipped with an LCL filter is greatly reduced when it is operating in a power grid with varying impedance and fluctuating grid voltages, which may result in poor current quality and possible instability in the system. A non-singular double integral terminal sliding mode (DIT-SMC) control is presented in this paper for a three-phase GCI with an LCL filter. The proposed method is presented in the α, β frame of reference without adopting an active or passive damping approach, reducing the computational burden. MATLAB/Simulink Version R2023b is leveraged to simulate the mathematical model of the proposed control system. The capability of the DIT-SMC method is validated through the OPAL-RT hardware-in-loop (HIL) platform. The effectiveness of the proposed method is first compared with SMC and integral terminal SMC, and then the DIT-SMC method is rigorously analyzed under resonance frequency events, grid impedance variation, and grid voltage distortions. It is demonstrated by the experimental results that the proposed control is highly effective in delivering a high-quality current into the grid, in spite of the simultaneous occurrence of power grid impedance variations in 6 mH and large voltage distortions. Full article

Precisely predicting photovoltaic (PV) output is crucial for reliable grid integration; so far, most models rely on site-specific sensor data or treat large meteorological datasets as black boxes. This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters and reveals their physical relevance to PV generation. Starting from 27 local and plant-level variables recorded at 15 min resolution for a 1 MW array in Çanakkale region, Türkiye (1 August 2022–3 August 2024), we apply a three-stage feature-selection pipeline: (i) variance filtering, (ii) hierarchical correlation clustering with Ward linkage, and (iii) a meta-heuristic optimizer that maximizes a neural-network R² while penalizing poor or redundant inputs. The resulting subset, dominated by apparent temperature and diffuse, direct, global-tilted, and terrestrial irradiance, reduces dimensionality without significantly degrading accuracy. Feature importance is then quantified through two complementary aspects: (a) tree-based permutation scores extracted from a set of ensemble models and (b) information gain computed over random feature combinations. Both views converge on shortwave, direct, and global-tilted irradiance as the primary drivers of active power. Using only the selected features, the best model attains an average R² ≅ 0.91 on unseen data. By utilizing transparent feature-reduction techniques and explainable importance metrics, the proposed approach delivers compact, more generalized, and reliable PV forecasts that generalize to sites lacking embedded sensor networks, and it provides actionable insights for plant siting, sensor prioritization, and grid-operation strategies. Full article

This paper proposes a coordinated control strategy for grid-forming inverters (GFMs) to address two critical challenges in evolving power systems. These are the active harmonic mitigation under nonlinear loading conditions and dynamic overcurrent control during grid disturbances. The proposed framework integrates a shunt active filter (SAF) mechanism within the GFM control structure to achieve a real-time suppression of harmonic distortions from the inverter and grid currents. In parallel, a virtual impedance-based dynamic current limiting strategy is incorporated to constrain fault current magnitudes, ensuring the protection of power electronic components and maintaining system stability. The SAF operates in a current-injection mode aligned with harmonic components, derived via instantaneous reference frame transformations and selective harmonic extraction. The virtual impedance control (VIC) dynamically modulates the inverter’s output impedance profile based on grid conditions, enabling adaptive response during fault transients to limit overcurrent stress. A detailed analysis is performed for the coordinated control of the grid-forming inverter. Supported by simulations and analytical methods, the approach ensures system stability while addressing overcurrent limitations and active harmonic filtering under nonlinear load conditions. This establishes a viable solution for the next-generation inverter-dominated power systems where reliability, power quality, and fault resilience are paramount. Full article

To address the challenge of a conventional line-commutated converter (LCC), unable to operate properly in connection with a very weak AC system, the technology of the capacitor-commutated converter (CCC) was widely utilized in 1990s. The topology of the CCC is constructed as a conventional LCC modified with a series capacitor between the converter transformer and the thyristor valves in each phase. Additional phase voltage can be generated on the capacitor to assist the process of the commutation. However, the CCC technology may experience continuous commutation failure due to the uncontrolled charging of the series capacitor. Based on the submodule-cascaded static synchronous compensator (STATCOM), this paper proposes a novel topology called the submodule-cascaded STATCOM-based CCC (SCCC). The SCCC technology enables the function of reactive power compensation and active filtering. It can also improve the transient characteristics of the AC faults via dynamic reactive power injection during the transient process, which helps to reduce the risk of continuous commutation failure in the CCC. Full article

The present work proposes a novel fractional-order multifunction filter topology in current-mode (CM), which is designed based on the Modified Current Feedback Operational Amplifier (MCFOA). The proposed design simultaneously generates fractional-order low-pass (FO-LPF), high-pass (FO-HPF), and band-pass (FO-BPF) outputs while utilizing an optimized set of essential active and passive elements, thereby ensuring simplicity, cost efficiency, and compatibility with integrated circuits (ICs). The fractional-order feature allows precise control over the transition slope between the passband and the stopband, enhancing design flexibility. PSpice simulations validated the filter’s theoretical performance, confirming a 1 kHz cut-off frequency, making it suitable for VLF applications such as military communication and submarine navigation. Monte Carlo analyses demonstrate robustness against parameter variations, while a low THD, a wide dynamic range, and low power consumption highlight its efficiency for high-precision, low-power applications. This work offers a practical and adaptable approach to fractional-order circuit design, with significant potential in communication, control, and biomedical systems. Full article

The Dual Active Bridge converter (DABC), known for its bidirectional power transfer capability and high efficiency, plays a crucial role in various applications, particularly in electric vehicles (EVs), where it facilitates energy storage, battery charging, and grid integration. The Dual Active Bridge Converter (DABC), when paired with a high-performance CLLC filter, is well-regarded for its ability to transfer power bidirectionally with high efficiency, making it valuable across a range of energy applications. While these features make the DABC highly efficient, they also complicate controller design due to nonlinear behavior, fast switching, and sensitivity to component variations. We have used a Fractional-order PID (FOPID) controller to benefit from the simple structure of classical PID controllers with lower complexity and improved flexibility because of additional filtering gains adopted in this method. However, for a FOPID controller to operate effectively under real-time conditions, its parameters must adapt continuously to changes in the system. To achieve this adaptability, a Multi-Agent Reinforcement Learning (MARL) approach is adopted, where each gain of the controller is tuned individually using the Deep Deterministic Policy Gradient (DDPG) algorithm. This structure enhances the controller’s ability to respond to external disturbances with greater robustness and adaptability. Meanwhile, finding the best initial gains in the RL structure can decrease the overall efficiency and tracking performance of the controller. To overcome this issue, Grey Wolf Optimization (GWO) algorithm is proposed to identify the most suitable initial gains for each agent, providing faster adaptation and consistent performance during the training process. The complete approach is tested using a Hardware-in-the-Loop (HIL) platform, where results confirm accurate voltage control and resilient dynamic behavior under practical conditions. In addition, the controller’s performance was validated under a battery management scenario where the DAB converter interacts with a nonlinear lithium-ion battery. The controller successfully regulated the State of Charge (SOC) through automated charging and discharging transitions, demonstrating its real-time adaptability for BMS-integrated EV systems. Consequently, the proposed MARL-FOPID controller reported better disturbance-rejection performance in different working cases compared to other conventional methods. Full article

This paper presents a fully integrated bandpass filter (BPF) with high tunability based on a novel differential active inductor (DAI), designed for sensor interface circuits operating in the ultra-high frequency (UHF) band. The design of the proposed DAI is based on a symmetrical configuration, utilizing a differential amplifier for the feedforward transconductance and a common-source (CS) transistor for the feedback transconductance. By integrating a cascode scheme with a feedback resistor, the quality factor of the active inductor is significantly improved, leading to enhanced mid-band gain for the bandpass filter. To facilitate independent tuning of the BPF‘s center frequency and mid-band gain, an active resistor adjustment and bias voltage control are employed, providing precise control over the filter’s operational parameters. Post-layout simulations and process corner results are conducted with 0.13 µm CMOS technology at 1.2 V supply voltage. The proposed second order BPF achieves a broad tuning range of 280 MHz to 2.426 GHz, with a passband gain between 8.9 dB and 16.54 dB. The design demonstrates a maximum noise figure of 16.54 dB at 280 MHz, an input-referred 1 dB compression point of −3.78 dBm, and a third-order input intercept point (IIP3) of −0.897 dBm. Additionally, the BPF occupies an active area of only 68.2×30 µm², including impedance-matching part, and consumes a DC power of 14–20 mW. The compact size and low power consumption of the design make it highly suitable for integration into modern wireless sensor interfaces where performance and area efficiency are critical. Full article

The Shunt Active Filter (SAF) is an effective solution for mitigating electrical perturbations in power networks. SAFs usually consist of a voltage source inverter (VSI) with lossy transistors and bulky inductors. In this context, this article proposes analytical models to evaluate the losses and efficiency of a SAF. The models include conduction and switching losses in the transistors and diodes and are valid for both IGBT and SiC MOSFET transistors. The methodology consists of analysing the current waveform to separate the portion flowing through the transistor or diode. IGBT and SiC MOSFET are compared in two cases: firstly, the classic SAF operation with harmonic and reactive power compensation and, secondly, in the case of power injection by a photovoltaic panel or batteries, in addition to the classic SAF operation. The results are validated with real manufacturer data. A step-by-step comparison shows a good accuracy of the model. Therefore, the developed methodology is useful for a SAF designer to select relevant components for the converter and to estimate the efficiency of the system accurately and quickly. Full article

Active magnetic bearings (AMBs) are a class of electromechanical equipment that effectively integrate Magnetic Bearing technology with PMSM technology, particularly for applications involving high-power and high-speed permanent magnet motors. However, as the rotor operates in a suspended state, the motor’s trajectory changes continuously. The installation of a speed sensor poses a risk of collisions with the shaft, which inevitably leads to rotor damage due to imbalance, shaft wear, or other mechanical effects. Consequently, for the rotor control system of PMSM, it is crucial to adopt a sensorless speed estimation method to achieve high-performance speed and position closed-loop control. This study uses the rotor system of a 75 kW AMB high-speed motor as a case study to provide a detailed analysis of the principles of high-frequency square wave signal injection (HFSWSII) and current signal injection for speed estimation. The high-frequency current response signal is derived, and a speed observer is designed based on signal extraction and processing methods. Subsequently, a speed estimation model for PMSM is constructed based on HFSWSII, and the issue of “filter bandwidth limitations and lagging effects in signal processing” within the observer is analyzed. A scheme based on the high-frequency pulse array current injection method is then proposed to enhance the observer’s performance. Finally, to assess the system’s anti-interference capability as well as the motor’s static and dynamic tracking performance, its dynamic behavior is tested under conditions of increasing and decreasing speed and load. Simulation and experimental results demonstrate that the PMSM control system based on HFSWSII achieves accurate speed estimation and shows excellent static and dynamic performance. Full article

Edge-computing applications demand ultra-low-power architectures for both feature extraction and classification tasks. In this manuscript, a Keyword Spotting (KWS) system tailored for energy-constrained portable environments is proposed. A 16-channel analog filter bank is employed for audio feature extraction, followed by a digital Gated Recurrent Unit (GRU) classifier. The filter bank is behaviorally modeled, making use of second-order band-pass transfer functions, simulating the analog front-end (AFE) processing. To enable efficient deployment, the GRU classifier is trained using a Learned Step Size (LSQ) and Look-Up Table (LUT)-aware quantization method. The resulting quantized model, with 4-bit weights and 8-bit activation functions (W4A8), achieves 91.35% accuracy across 12 classes, including 10 keywords from the Google Speech Command Dataset v2 (GSCDv2), with less than 1% degradation compared to its full-precision counterpart. The model is estimated to require only 34.8 kB of memory and 62,400 multiply–accumulate (MAC) operations per inference in real-time settings. Furthermore, the robustness of the AFE against noise and analog impairments is evaluated by injecting Gaussian noise and perturbing the filter parameters (center frequency and quality factor) in the test data, respectively. The obtained results confirm a strong classification performance even under degraded circuit-level conditions, supporting the suitability of the proposed system for ultra-low-power, noise-resilient edge applications. Full article

Neural signal recording demands compact, low-power, high-performance amplifiers, to enable large-scale, multi-channel electrode arrays. This work presents a bioamplifier optimized for action potential detection, designed using TSMC 28 nm HPC CMOS technology. The amplifier integrates an active low-pass filter, eliminating bulky DC-blocking capacitors and significantly reducing the size and power consumption. It achieved a high input impedance of 105.5 GΩ, ensuring minimal signal attenuation. Simulation and measurement results demonstrated a mid-band gain of 58 dB, a −3 dB bandwidth of 7 kHz, and an input-referred noise of 11.1 μ${\mathrm{V}}_{\mathrm{rms}}$, corresponding to a noise efficiency factor (NEF) of 8.4. The design occupies a compact area of 2500 μ$m^2$, making it smaller than previous implementations for similar applications. Additionally, it operates with an ultra-low power consumption of 3.4 μW from a 1.2 V supply, yielding a power efficiency factor (PEF) of 85 and an area efficiency factor of 0.21. These features make the proposed amplifier well suited for multi-site in-skull neural recording systems, addressing critical constraints regarding miniaturization and power efficiency. Full article

Energy storage systems (ESSs) and active power filters (APFs) are key power electronic technologies for FACTS (Flexible AC Transmission Lines). Battery energy storage has a structure similar to a shunt active power filter, i.e., a storage element and a voltage source inverter (VSI) connected to the grid using a PWM filter and/or transformer. This similarity allows for the design of an ESS with the ability to operate as a shunt APF. One of the key milestones in ESS or APF development is the DC-link design. The proper choice of the capacitance of the DC-link capacitors and their equivalent resistance ensures the proper operation of the whole power electronic system. In this article, it is proposed to estimate the required minimum DC-link capacitance using a spectral analysis of the DC-link current for different operating modes, battery charge mode and harmonic compensation mode, for a nonlinear load. It was found that the AC component of the DC-link current is shared between the DC-link capacitors and the rest of the DC stage, including the battery. This relation is described analytically. The main advantage of the proposed approach is its universality, as it only requires calculating the harmonic spectrum using the switching functions. This approach is demonstrated for DC-link capacitor estimation in two-level and three-level NPC inverter topologies. Moreover, an analysis of the AC current component distribution between the DC-link capacitors and the other elements of the DC-link stage was carried out. This part of the analysis is especially important for battery energy storage systems. The obtained results were verified using a simulation model. Full article

This paper investigates the challenges and solutions associated with integrating a hydrogen-generating nuclear-renewable integrated energy system (NR-IES) under a transactive energy framework. The proposed system directs excess nuclear power to hydrogen production during periods of low grid demand while utilizing renewables to maintain grid stability. Using digital real-time simulation (DRTS) in the Typhoon HIL 404 model, the dynamic interactions between nuclear power plants, electrolyzers, and power grids are analyzed to mitigate issues such as harmonic distortion, power quality degradation, and low power factor caused by large non-linear loads. A three-phase power conversion system is modeled using the Typhoon HIL 404 model and includes a generator, a variable load, an electrolyzer, and power filters. Active harmonic filters (AHFs) and hybrid active power filters (HAPFs) are implemented to address harmonic mitigation and reactive power compensation. The results reveal that the HAPF topology effectively balances cost efficiency and performance and significantly reduces active filter current requirements compared to AHF-only systems. During maximum electrolyzer operation at 4 MW, the grid frequency dropped below 59.3 Hz without filtering; however, the implementation of power filters successfully restored the frequency to 59.9 Hz, demonstrating its effectiveness in maintaining grid stability. Future work will focus on integrating a deep reinforcement learning (DRL) framework with real-time simulation and optimizing real-time power dispatch, thus enabling a scalable, efficient NR-IES for sustainable energy markets. Full article

The aim of this paper is to present an approach to condition monitoring of an actuated mechanical system operating in a steady-state regime. The state signals generated by the sensors placed on the mechanical system (a lathe headstock gearbox) operating in a steady-state regime contain a sum of periodic components, sometimes mixed with a small amount of noise. It is assumed that the state of a rotating part placed inside a mechanical system can be characterized by the shape of a periodic component within the state signal. This paper proposes a method to find the time domain description for the significant periodic components within these state signals, as patterns, based on the arithmetic averaging of signal samples selected at constant time regular intervals. This averaging has the same effect as a numerical filter with multiple narrow pass bands. The availability of this method for condition monitoring has been fully demonstrated experimentally. It has been applied to three different state signals: the active electrical power absorbed by an asynchronous AC electric motor driving a lathe headstock gearbox, the vibration of this gearbox, and the instantaneous angular speed of the output spindle. The paper presents some relevant patterns describing the behavior of different rotating parts within this gearbox, extracted from these state signals. Full article