Rough Waters, Dark Shadows and Cross-Cutting Winds:the impact of European Union enlargement on Turkey’s EU accession negotiations

For a multiplicity of socio-economic, geo-political, strategic and identity-based reasons, Turkey’s progress towards EU membership is often treated as a sui generis case. Yet although Turkey’s accession negotiations with the European Union (EU) are essentially a bilateral – and often stormy – affair, they take place within a wider and dynamic process of enlargement in which not only can the gloomy – sometimes dark – shadows of past and prospective enlargements be clearly detected, but so too can the often chill winds from ongoing, parallel negotiations with other candidates. How the EU negotiates accession and what it expects from candidates has continued to evolve since the EU began drawing up its framework for negotiations with Turkey ten years ago. This paper charts this evolution by first identifying changes in the light of Croatia’s negotiating experience, the ‘lessons learnt’ by the EU in meeting the challenges of Bulgarian and Romanian accession, the EU’s handling of Iceland’s membership bid and accession negotiations, and the revised approach to negotiating accession evident in the more recent frameworks for accession negotiations with Montenegro and Serbia. The paper then explores the extent to which these changes have impacted on the approach the EU has adopted in framing and progressing accession negotiations with Turkey. In doing so, it questions both the consistency with which the EU’s negotiates accession and the extent to which Turkey’s progress towards EU membership is conditioned by the broader dynamics of EU enlargement as opposed to simply the dynamics within EU-Turkey relations and domestic Turkish reform efforts.

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

Adaptive backstepping droop controller design for multi-terminal high-voltage direct current systems

Wind power is one of the most developed renewable energy resources worldwide. To integrate offshore wind farms to onshore grids, the high-voltage direct current (HVDC) transmission cables interfaced with voltage source converters (VSCs) are considered to be a better solution than conventional approaches. Proper DC voltage indicates successive power transfer. To connect more than one onshore grid, the DC voltage droop control is one of the most popular methods to share the control burden between different terminals. However, the challenges are that small droop gains will cause voltage deviations, while higher droop gain settings will cause large oscillations. This study aims to enhance the performance of the traditional droop controller by considering the DC cable dynamics. Based on the backstepping control concept, DC cables are modelled with a series of capacitors and inductors. The final droop control law is deduced step-by-step from the original remote side. At each step the control error from the previous step is considered. Simulation results show that both the voltage deviations and oscillations can be effectively reduced using the proposed method. Further, power sharing between different terminals can be effectively simplified such that it correlates linearly with the droop gains, thus enabling simple yet accurate system operation and control.