The objective of this study is to understand the consolidated undrained stress–strain behavior of a “tunable” clay–polymer composite composed of kaolin and nonionic polyacrylamide under different confining stress, ionic concentration and pH conditions that promote changes in polymer molecule conformation (coiled, partially extended or extended). Based on the experimental results, the shear strength of the composite is controlled by the formation of higher-order face-to-face aggregated flocs under predicted partially extended conformation conditions and localized dilation under a low confining stress (100 kPa). Under high confining stress (200 kPa), the composite has a more contractive tendency compared to pure clay due to breakage of aggregates and flattened conformation of the polymer. The composite at partially extended conformation shows the thixotropic rearrangement of particles when overconsolidated. Both the coiled and extended conformations of polymer molecules promote apparent overconsolidation due to an increase in interparticle bonding and in contact area by forming large, aggregated flocs. However, the composite shear strength at coiled or extended conformation is mostly influenced by the conformation of polymer molecules. The clay–polymer composite is more responsive to change in pH and ionic concentration than pure clay and demonstrates the “tunability” of the composite. This experimental study sheds light on the link between polymer conformational changes and the mechanical behavior of clay–polymer composites.
The DC bus neutral point of the three-level neutral point clamped (3L-NPC) converter is always connected to the neutral wire of the power system due to the requirement of zero-sequence output current, based on which the additional common resonance loop via the neutral wire will be introduced. The analysis of this introduced common-mode resonance is presented in this article, and the neutral point voltage fluctuation model is established considering the coupling of multi-frequency. The proposed neutral point voltage fluctuation model reveals that the output current of the 3L-NPC causes the fluctuation excitation sources of different frequencies, and the resonance will occur when the DC capacitors and the line inductance form a resonance loop at exactly this frequency. An additional extra active filter circuit is proposed in the article to suppress the fluctuation excitation sources, and the common-mode resonance won't be motivated as a matter of course. The control strategy and the stability are analyzed based on the structure of the proposed active filter circuit. Finally, simulation results verify the accuracy of the derived neutral point voltage fluctuation model and the effectiveness of the proposed active filter circuit.
Accurate load prediction is critical for optimizing energy dispatch and ensuring reliable operation within fuel cell microgrids. Addressing the limitations of conventional forecasting methods when applied to the complex, multi-faceted load profiles inherent in these systems, this paper introduces a novel multi-dimensional load forecasting approach based on deep belief networks (DBNs). This DBN model effectively extracts high-order nonlinear characteristics from load data and integrates diverse information sources, enabling precise forecasting of electricity, hydrogen, and heat demands. Furthermore, genetic algorithms and mutual information are utilized to optimize feature selection. To comprehensively evaluate the model's performance, particularly considering the unique attributes of fuel cell microgrids, we propose three specific indices: a Comprehensive Error Improvement Index (CEII), a Response Speed Adaptability Index (S), and a Fluctuation Characteristic Matching Index (F). Experimental results demonstrate that the proposed DBN-based method significantly outperforms traditional techniques in terms of forecasting accuracy, response speed, and the ability to match load fluctuation characteristics. This research offers crucial technical support for the optimized operation of fuel cell microgrids
