2026· IEEE Transactions on Automation Science and Engineering· Vol 23, pp. 14093-14102· 0 citations· 38 references
Abstract
To satisfy the stringent real-time requirements of harmonic detection, this paper proposes a predefined-time zeroing neural network (PTZNN) model, marking the first application of the zeroing neural network (ZNN) model in modern power systems analysis. Compared with other detection methods, the PTZNN model integrates a novel predefined-time activation function (NPTAF), ensuring that the detection error converges to zero within a predefined time, thereby achieving predefined-time convergence. In addition, the PTZNN model eliminates the reliance on analytical derivatives via the proposed NPTAF, enabling high-accuracy harmonic detection even in the presence of numerical differentiation deviations. Comparative simulations confirm the effectiveness and superiority of the PTZNN model for harmonic detection under both steady-state and transient distorted signals, which cover typical operating scenarios in modern power systems. Finally, a real-time experimental platform based on RT-LAB and OP5600 is established to validate the effectiveness and engineering feasibility of the proposed PTZNN model for harmonic detection. Note to Practitioners—This study is motivated by the urgent need for rapid and precise harmonic detection in modern power systems. Conventional harmonic detection methods often face challenges in balancing processing speed with tracking accuracy, especially when signals are subjected to abrupt disturbances. The PTZNN model is presented to address these challenges, offering a significant practical advantage where practitioners can set a specific predefined time to ensure the harmonic detection error converges to zero by this deadline. This capability is critical for time-sensitive power quality compensation and protection. Additionally, the model remains effective even when facing numerical differentiation deviations, ensuring reliable performance on standard industrial processors that may lack high-precision computing power. The proposed framework is suitable for integration into power quality analyzers and the control loops of active power filters to provide timely data for power systems monitoring and compensation. It is particularly effective for tracking both steady-state and transient harmonics. However, it should be noted that the predefined convergence time must be balanced against the hardware sampling frequency. Setting an unrealistically short time may increase the computational load on the processor.
To address the requirements of current tracking control and power quality improvement for active power filters (APF), this paper proposes a nested terminal sliding mode control scheme based on hippocampal neural network to overcome the performance limitations of existing APF control methods. First, the circuit structure of the APF is elaborated, and the mathematical model including lumped system uncertainties is derived. Then, a nested terminal sliding mode surface is designed to ensure that the tracking error converges to zero in finite time, which achieves performance improvement compared with traditional linear sliding mode control that can only realize asymptotic convergence. Afterward, a hippocampal‐inspired neural network that mimics the human hippocampal information processing mechanism is introduced for the first time to learn the unknown nonlinear terms in the sliding mode controller, effectively weakening the adverse effects of system uncertainties on control performance. A novel feature selection mechanism is proposed to extract and process key information within the network, greatly reducing the network computational overhead. A dual‐loop structure is designed in the neural network to improve the processing efficiency of time‐varying harmonic signals, and the online adaptive update law of network parameters is derived based on Lyapunov theorem to guarantee system stability. Finally, simulation and hardware experimental results verify the effectiveness of the proposed algorithm. This method reduces the total harmonic distortion (THD) of the grid source current to 1.29% in simulation and 3.03% in experiment. Compared with mainstream methods, it exhibits excellent current tracking ability, strong robustness, and superior grid harmonic suppression performance.
Pengpeng Lyu, Qiangsheng Bu, Guangjian Li et al.· International Journal of Ada...· 0 citations
The whole-system impedance model has proven a powerful tool for assessing the small-signal stability of multi-inverter power systems; however, its application is limited to a small range around a steady-state operating point due to the inherent assumptions of time invariance and linearisation. In this paper, a dedicated physics-informed neural network (PINN) for small-signal stability analysis in high-dimensional multi-inverter power systems is developed. The PINN is trained with step-response data produced from limited sets of system electromagnetic transient (EMT) simulations, and the trained model can predict the poles and residues of the whole-system impedance/admittance model, i.e., the transfer functions, across the full operating space. Such a PINN offers unique insights into system stability that surpass what conventional analytical methods or EMT simulations can achieve. By characterising how the impedance model evolves with power flow variations, it predicts the dynamic behaviour of the time-varying system and reveals oscillation risks that may emerge while identifying their root causes. It also provides direct visualisation of the possible range of oscillatory modes under a given power flow condition, enabling an optimal generation distribution while maintaining safe operation of the system. The proposed PINN is fully validated on a 2-IBR system and a 4-IBR system, with its application details presented.
Hanxi Chen, Xiangyu Meng, Jianhong Wang et al.· 0 citations
Switching harmonics generated by power electronic converters have become a critical power quality issue in more-electric aircraft (MEA) dual-generator power systems, and accurate harmonic prediction is essential for effective harmonic cancellation. However, conventional analytical models often suffer from limited prediction accuracy, while data-driven methods are constrained by the scarcity of labeled data under certain operating conditions. This paper presents a method for harmonic prediction and cancellation in the dual-generator power systems of more-electric aircraft. To address the accumulation of switching harmonics on the DC bus, a comparative study is conducted among simplified analytical models, feedforward neural networks, and transfer learning-based models. A harmonic modeling framework and phase shift optimization strategy are established for harmonic cancellation. To overcome data limitations on the high-pressure shaft, transfer learning is employed to transfer knowledge learned from the low-pressure side to the high-pressure side. Results show that data-driven methods outperform traditional models, while transfer learning-based models further improve prediction accuracy and generalization under limited data conditions.
LCL-type grid-connected inverters face problems including complex modeling, sampled current distortion from harmonics and negative-sequence components, and reduced control accuracy of conventional deadbeat predictive current control (DPCC) due to its heavy reliance on precise system parameters. To solve these issues, this paper proposes a model-free DPCC (MF-DPCC) using an adaptive-gain extended state observer (AGESO). Firstly, an ultra-local model is established to avoid dependence on accurate mathematical models. Secondly, an AGESO is designed to overcome conventional ESO drawbacks (initial differential peaking, inflexible bandwidth tuning, and tracking-noise immunity trade-off) by adopting adaptive gains to real-time estimate the ultra-local model’s lumped disturbance and state variables. Finally, a double second-order generalized integrator (DSOGI) purifies sampled currents and extracts fundamental positive-sequence components, reducing harmonic disturbance on the AGESO, allowing for higher bandwidth operation without excessive noise amplification, and indirectly enhancing resonance suppression by including LCL resonance-induced disturbance in the lumped term.
This paper presents the design and experimental validation of a micro-phasor measurement unit (uPMU) system developed for real-time protection and power quality analysis in low-voltage distribution networks. The proposed architecture integrates a Raspberry Pi-based Central Protection Unit, an AD7606 synchronous analog-to-digital converter for high-speed multi-channel sampling, and a DAC8568 digital-to-analog converter for test and simulation purposes. The system supports hardware time synchronization via the Precision Time Protocol (PTP) and enables both real and simulated grid event measurements. Experimental results confirm the feasibility of achieving up to 200~kSPS sampling per channel, providing the temporal resolution required for sub-cycle fault detection. The long-term objective is to apply artificial intelligence (AI) techniques to predict the next waveform samples and detect deviations that indicate faults or abnormal grid conditions within microseconds.
József Bencsik, Zsolt Čonka· Advances in Science and Tech...· 0 citations
Power quality degradation caused by nonlinear loads and extensive use of power electronic converters has become a critical concern in modern distribution systems, leading to excessive harmonic distortion, reactive power demand, and reduced system reliability. Shunt Active Power Filters (SAPFs) are widely recognized as effective solutions for mitigating these disturbances; however, their performance strongly depends on accurate harmonic estimation and optimal controller tuning under dynamic operating conditions. This paper proposes a Genetic Algorithm optimized Recursive Least Squares (GA-RLS) based SAPF for enhanced harmonic and reactive power compensation in a three-phase system. The RLS algorithm is employed for fast and precise extraction of fundamental and harmonic current components, while a Genetic Algorithm optimally tunes the RLS parameters to achieve faster convergence, reduced estimation error, and improved dynamic response. The proposed control scheme is implemented and evaluated using MATLAB/Simulink under nonlinear load conditions. Performance analysis is carried out through time-domain waveforms, FFT spectra, and Total Harmonic Distortion (THD) indices, and the results are compared with a conventional PQ-theory-based SAPF. Simulation results demonstrate significant improvement in power quality, with voltage THD reduced to 1.55% and current THD reduced to 6.49%, confirming the effectiveness and robustness of the proposed GA-RLS-SAPF strategy.
Rohit Gedam, Abhimanyu Kumar· 2026 International Conferenc...· 0 citations