4 resultados para Predictive Control Strategy


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Increasingly in power systems, there is a trend towards the sharing of reserves and integration of markets over wide areas in order to enable increased penetration of renewable sources in interconnected power systems. In this paper, a number of simple PI and gain based Model Predictive Control algorithms are proposed for Automatic Generation Control in AC areas connected to Multi-Terminal Direct Current grids. The paper discusses how this approach improves the sharing of secondary reserves and could assist in achieving EU energy targets for 2030 and beyond.

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Wind generation in highly interconnected power networks creates local and centralised stability issues based on their proximity to conventional synchronous generators and load centres. This paper examines the large disturbance stability issues (i.e. rotor angle and voltage stability) in power networks with geographically distributed wind resources in the context of a number of dispatch scenarios based on profiles of historical wind generation for a real power network. Stability issues have been analysed using novel stability indices developed from dynamic characteristics of wind generation. The results of this study show that localised stability issues worsen when significant penetration of both conventional and wind generation is present due to their non-complementary characteristics. In contrast, network stability improves when either high penetration of wind and synchronous generation is present in the network. Therefore, network regions can be clustered into two distinct stability groups (i.e. superior stability and inferior stability regions). Network stability improves when a voltage control strategy is implemented at wind farms, however both stability clusters remain unchanged irrespective of change in the control strategy. Moreover, this study has shown that the enhanced fault ride-through (FRT) strategy for wind farms can improve both voltage and rotor angle stability locally, but only a marginal improvement is evident in neighbouring regions.

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This paper presents a multiple robots formation manoeuvring and its collision avoidance strategy. The direction priority sequential selection algorithm is employed to achieve the raw path, and a new algorithm is then proposed to calculate the turning compliant waypoints supporting the multi-robot formation manoeuvre. The collision avoidance strategy based on the formation control is presented to translate the collision avoidance problem into the stability problem of the formation. The extension-decomposition-aggregation scheme is next applied to solve the formation control problem and subsequently achieve the collision avoidance during the formation manoeuvre. Simulation study finally shows that the collision avoidance problem can be conveniently solved if the stability of the constructed formation including unidentified objects can be satisfied.

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In this updated analysis of the EXPERT-C trial we show that, in magnetic resonance imaging-defined, high-risk, locally advanced rectal cancer, adding cetuximab to a treatment strategy with neoadjuvant CAPOX followed by chemoradiotherapy, surgery, and adjuvant CAPOX is not associated with a statistically significant improvement in progression-free survival (PFS) and overall survival (OS) in both KRAS/BRAF wild-type and unselected patients. In a retrospective biomarker analysis, TP53 was not prognostic but emerged as an independent predictive biomarker for cetuximab benefit. After a median follow-up of 65.0 months, TP53 wild-type patients (n = 69) who received cetuximab had a statistically significant better PFS (89.3% vs 65.0% at 5 years; hazard ratio [HR] = 0.23; 95% confidence interval [CI] = 0.07 to 0.78; two-sided P = .02 by Cox regression) and OS (92.7% vs 67.5% at 5 years; HR = 0.16; 95% CI = 0.04 to 0.70; two-sided P = .02 by Cox regression) than TP53 wild-type patients who were treated in the control arm. An interaction between TP53 status and cetuximab effect was found (P <.05) and remained statistically significant after adjusting for statistically significant prognostic factors and KRAS.