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.

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.