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A Comparative Study on Neural Network Based Controllers Used in Grid-Interactive Solar System
摘要： This paper proposes a comparative assessment of NN based current controllers followed by hardware validation towards power quality improvement in grid-interactive VSI controlled solar system. The steady state errors, transient disturbances and high current harmonic effects encountered in conventional linear PI and PR controllers are nullified by employing intelligent adaptive current controller. Three adaptive current control strategies viz. ADALINE-LMS, ALLMS, and VLAS-LMS are identified by using artificial neural network topology having been controlled by different weight-regulating algorithms which helps in minimizing current harmonics generated by the widespread use of VSI, non-linear loads, faults and uncertain polluted grid. A comprehensive comparative study is driven from the proposed adaptive controller’s stability and convergence criterion, current magnitudes calculated at different power zones, % overshoot, settling time and power quality and analyzed under numerous operating conditions. From the comparative assessment performed in between conventional and intelligent current controllers, it is confirmed that the intelligent control technique performs best under the non-linear loads and transient conditions whereas all the controllers perform equally good under the linear loads. Proposed methods are simulated in MATLAB/Simulink and their effectiveness is compared in terms of time responses, stability and low-order current harmonics compensation capability. The robustness of the intelligent current controller is established through the experimental performances using dSPACE RTI 1202.
关键词： grid-interactive solar,power quality,stability,dSPACE,neural network (NN),current controllers (cc),time response
更新于2025-09-23 15:21:01
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Machine Learning for 100Gb/s/λ Passive Optical Network
摘要： To respond the growing bandwidth demand by emerging applications such as fixed-mobile convergence for 5G and beyond 5G, 100Gb/s/λ access network becomes the next research focus of passive optical network (PON) roadmap. Intensity modulation and direct detection (IMDD) technology is still considered as a promising candidate for 100Gb/s/λ PON attributed to its low cost, low power consumption and small footprint. In this paper, we achieve 100Gb/s/λ IMDD PON by using 20G-class optical and electrical devices due to its commercial linear and nonlinear availability. To mitigate the system distortions, neural network (NN) based equalizer is used and the performance is compared with feedforward equalizer (FFE) and Volterra nonlinear equalizer (VNE). We introduce the rules to train and test the data when using NN-based equalizer to guarantee a fair comparison with FFE and VNE. Random data has to be used for training, but for test, both random data and psudo-random bit sequence (PRBS) are applicable. We found NN-based equalizer has the same performance with FFE and VNE in the case of linear distortion only, but outperforms them in strong nonlinearity case. In the experiment, to improve the loss budget, we increase the launch power to 18 dBm, achieving a 30-dB loss budget for 33Gbaud/s PAM8 signal at the system frequency response of 16.2 GHz, attributed to the strong nonlinear equalization capability of NN.
关键词： neural network (NN),machine learning,intensity modulation and direct detection (IMDD),digital signal processing (DSP),Passive optical network (PON)
更新于2025-09-04 15:30:14