Partially rated DC interlinking converters are recognized for their high-gain power regulation capabilities, which effectively synergize active power across DC microgrids (DCMGs). Integrating energy storage units (ESUs) to address the intermittent nature of renewables in DCMGs has become an enhanced requirement for these converters. This article proposes a partially rated multiport interlinking converter (PMIC) that incorporates a distributed ESU. The PMIC controls a floating voltage and a bidirectional shunt current on the DC line, ensuring full galvanic isolation for the ESU while operating at a low DC-link voltage. It regulates multidirectional power flow and balances power during peak and off-peak periods. A decentralized droop-based power flow control strategy is proposed for the PMIC, which distributes renewable energy generation, load consumption, and ESU utilization proportionally across the system. The control strategy includes two tailored continuously differentiable activation functions, Sigmoid and Hyperbolic Tangent, to facilitate autonomous global power-sharing and seamless ESU engagement. Simulation and experimental case studies confirm the PMIC's capability to smooth renewable energy fluctuations and enhance the power and voltage profiles of DCMGs.

Multiport dc-dc converters have been intensively concerned and studied in power electronics-based systems. However, the instability issue and its suppression method are rarely discussed. This article investigates the stability of the triple active bridge (TAB) converter with two constant power loads by impedance-based method, where the output impedance of the TAB converter is derived by the extra element theorem. Then, a virtual impedance-based active damping (AD) control is proposed, where the virtual impedance has four different positions at the output capacitor of the dc port. The effect of the proposed AD control on output impedance and system dynamic response is analyzed. Meanwhile, the range of the virtual impedance is specified. Simulation is performed in PLECS and experimental tests are conducted on a scale-down TAB prototype to verify the effectiveness of the proposed AD control.

The traditional fault control strategy of converter-interfaced renewable energy sources (CIRESs) may bring about a lower sensitivity level or misoperation of fault component-based directional elements. To overcome this problem, a new control scheme is proposed to adjust sequence impedance angles of CIRESs by computing suitable current references of the CIRES controller. Meanwhile, these current references are maximized by an iterative algorithm to make full use of the short-circuit capacity of CIRESs. The proposed control scheme is applicable to various faulty conditions such as different fault types, power factors, weak grids, and larger fault resistances. Compared with the new directional elements that need to update protection algorithms, the proposed control strategies can make CIRESs compatible with the existing directional elements whilst the necessary fault ride-through (FRT) requirements can still be satisfied. Furthermore, all the controller parameters are not required to be revised based on the detected fault type, even with only local measured data collected. The associated PSCAD simulations, real-time digital simulator (RTDS) testing and the downscale hardware experiment verify the proposed method.

Ship behavior is the semantic expression of corresponding trajectory in spatial-temporal space. The intelligent identification of ship behavior is critical for safety supervision in the waterborne transport. In particular, the complicated behavior reflects the long-term intentions of a ship, but it is challenging to recognize it automatically for computers without a proper understanding. For this purpose, this study provides a method to model the behavior for computers from the perspective of knowledge modeling that is explainable. Based on our previous work, a semantic model for ship behavior representation is given considering the multi-scale features of ship behavior in cognitive space. Firstly, the multi-scale features of ship behavior are analyzed in spatial-temporal dimension and semantic dimension individually. Then, a method for multi-scale behaviors modeling from the perspective of semantics is determined, which divides the behavior scale into four sub-scales in cognitive space, considering spatial and temporal dimensions: action, activity, process, and event. Furthermore, an ontology model is introduced to construct the multi-scale semantic model for ship behavior, where behaviors with different semantic scales are expressed using the functions of ontology from a microscopic perspective to a macroscopic perspective consecutively. To validate the model, a case study is conducted in which ship behavior with different scales occurred in port water areas. Typical behaviors, which include leveraging the axioms expression and semantic web rule language (SWRL) of the ontology, are then deduced using a reasoner, such as Pellet. The results show that the model is reasonable and feasible to represent multi-scale ship behavior in various scenarios and provides the potential to construct a smart supervision network for maritime authorities.

Objective: Robotic endoscopes have the potential to dramatically improve endoscopy procedures, however current attempts remain limited due to mobility and sensing challenges and have yet to offer the full capabilities of traditional tools. Endoscopic intervention (e.g., biopsy) for robotic systems remains an understudied problem and must be addressed prior to clinical adoption. This paper presents an autonomous intervention technique onboard a Robotic Endoscope Platform (REP) using endoscopy forceps, an auto-feeding mechanism, and positional feedback. Methods: A workspace model is established for estimating tool position while a Structure from Motion (SfM) approach is used for target-polyp position estimation with the onboard camera and positional sensor. Utilizing this data, a visual system for controlling the REP position and forceps extension is developed and tested within multiple anatomical environments. Results: The workspace model demonstrates accuracy of 5.5% while the target-polyp estimates are within 5 mm of absolute error. This successful experiment requires only 15 seconds once the polyp has been located, with a success rate of 43% using a 1 cm polyp, 67% for a 2 cm polyp, and 81% for a 3 cm polyp. Conclusion: Workspace modeling and visual sensing techniques allow for autonomous endoscopic intervention and demonstrate the potential for similar strategies to be used onboard mobile robotic endoscopic devices. Significance: To the authors' knowledge this is the first attempt at automating the task of colonoscopy intervention onboard a mobile robot. While the REP is not sized for actual procedures, these techniques are translatable to devices suitable for in vivo application.

For optimal operation of power-intensive plants, production scheduling and electricity procurement have to be considered simultaneously. In addition, uncertainty needs to be taken into account. For this purpose, an integrated stochastic mixed-integer linear programming model is developed that considers the two most critical sources of uncertainty: spot electricity price, and product demand. Conditional value-at-risk is incorporated into the model as a measure of risk. Furthermore, scenario reduction and multicut Benders decomposition are implemented to solve large-scale real-world problems. The proposed model is applied to an illustrative example as well as an industrial air separation case. The results show the benefit from stochastic optimization and the effect of taking a risk-averse rather than a risk-neutral approach. An interesting insight from the analysis is that in risk-neutral optimization, accounting for electricity price uncertainty does not yield significant added value; however, in risk-averse optimization, modeling price uncertainty is crucial for obtaining good solutions.