Global maximum power point tracking method for photovoltaic systems using Takagi-Sugeno fuzzy models and ANFIS approach

Introduction. A new global maximum power point tracking (GMPPT) control strategy for a solar photovoltaic (PV) system, based on the combination of Takagi-Sugeno (T-S) fuzzy models and an ANFIS, is presented. The novelty of this paper lies in the integration of T-S fuzzy models and the ANFIS approach to develop an efficient GMPPT controller for a PV system operating under partial shading conditions. Purpose. The new GMPPT control strategy aims to extract maximum power from the PV system under varying weather conditions or partial shading. Methods. An ANFIS algorithm is used to determine the maximum voltage, which corresponds to the actual maximum power point, based on PV voltage and current. Next, the nonlinear model of the PV system is employed to design the T-S fuzzy controller. A reference model is then derived based on the maximum voltage. Finally, a tracking controller is developed using the reference model and the T-S fuzzy controller. The stability of the overall system is evaluated using Lyapunov’s method and is represented through linear matrix inequalities expressions. The results clearly demonstrate the advantages of the proposed GMPPT-based fuzzy control strategy, showcasing its high performance in effectively reducing oscillations in various steady states of the PV system, ensuring minimal overshoot and a faster response time. In addition, a comparative analysis of the proposed GMPPT controller against conventional algorithms, such as Incremental Conductance, Perturb & Observe and Particle Swarm Optimization, shows that it offers a fast dynamic response in finding the maximum power with significantly less oscillation around the maximum power point. References 20, tables 3, figures 14

A multiple constraints framework for collaborative learning flow orchestration

Paper presented at ICWL 2016, 15th International Conference, Rome, Italy, October 26–29, 2016.Collaborative Learning Flow Patterns (e.g., Jigsaw) offer sound pedagogical strategies to foster fruitful social interactions among learners. The pedagogy behind the patterns involves a set of intrinsic constraints that need to/nbe considered when orchestrating the learning flow. These constraints relate to the organization of the flow (e.g., Jigsaw pattern - a global problem is divided into sub-problems and a constraint is that there need to be at least one expert group working on each sub-problem) and group formation policies (e.g., groups solving the global problem need to have at least one member coming from a different previous expert group). Besides, characteristics of specific learning situations such as learners’ profile and technological tools used provide additional parameters that can be considered as context-related extrinsic constraints relevant to the orchestration (e.g., heterogeneous groups depending on experience or interests). This paper proposes a constraint framework that considers different constraints for orchestration services enabling adaptive computation of orchestration aspects. Substantiation of the framework with a case study demonstrated the feasibility, usefulness and the expressiveness of the framework.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness/n(TIN2014-53199-C3-3-R; MDM-2015-0502)

Group formation for collaboration in exploratory learning using group technology techniques

Exploratory Learning Environments (ELEs) allow learners to approach a problem in different ways; they are particularly suitable for ill-defined problems where knowledge is less structured and open-ended exploration is allowed. Moreover, multiple solutions which are equally valid are possible and a common and efficient way to convey this is by promoting and supporting students’ collaboration. Successful collaboration, however, depends on forming groups in which the activity is relevant for all members of the group. In this paper we present a computational model for group formation for open-ended exploration in ELEs by modelling the various strategies that learners adopt to solve the same task. This is underpinned by Group Technology techniques that use as criteria the learners’ strategies and the similarity among them to form groups that match pedagogy considerations. The proposed mechanism is tested in an exploratory learning environment for mathematical generalisation