A practical approach for profit-based unit commitment with emission limitations

This paper proposes a practical approach for profit-based unit commitment (PBUC) with emission limitations. Under deregulation, unit commitment has evolved from a minimum-cost optimisation problem to a profit-based optimisation problem. However, as a consequence of growing environmental concern, the impact of fossil-fuelled power plants must be considered, giving rise to emission limitations. The simultaneous address of the profit with the emission is taken into account in our practical approach by a multiobjective optimisation (MO) problem. Hence, trade-off Curves between profit and emission are obtained for different energy price profiles, in a way to aid decision-makers concerning emission allowance trading. Moreover, a new parameter is presented, ratio of change, and the corresponding gradient angle, enabling the proper selection of a compromise commitment for the units. A case study based on the standard IEEE 30-bus system is presented to illustrate the proficiency Of Our practical approach for the new competitive and environmentally constrained electricity supply industry.

FMRI 3D registration based on Fourier space subsets using neural networks

In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.

Linear and sliding-mode control design for matrix converter-based unified power flow controllers

This paper presents the design and compares the performance of linear, decoupled and direct power controllers (DPC) for three-phase matrix converters operating as unified power flow controllers (UPFC). A simplified steady-state model of the matrix converter-based UPFC fitted with a modified Venturini high-frequency pulse width modulator is first used to design the linear controllers for the transmission line active (P) and reactive (Q) powers. In order to minimize the resulting cross coupling between P and Q power controllers, decoupled linear controllers (DLC) are synthesized using inverse dynamics linearization. DPC are then developed using sliding-mode control techniques, in order to guarantee both robustness and decoupled control. The designed P and Q power controllers are compared using simulations and experimental results. Linear controllers show acceptable steady-state behaviour but still exhibit coupling between P and Q powers in transient operation. DLC are free from cross coupling but are parameter sensitive. Results obtained by DPC show decoupled power control with zero error tracking and faster responses with no overshoot and no steady-state error. All the designed controllers were implemented using the same digital signal processing hardware.

Multilevel high-voltage pulse generation based on a new modular solid-state switch

This paper describes a modular solid-state switching cell derived from the Marx generator concept to be used in topologies for generating multilevel unipolar and bipolar high-voltage (HV) pulses into resistive loads. The switching modular cell comprises two ON/OFF semiconductors, a diode, and a capacitor. This cell can be stacked, being the capacitors charged in series and their voltages balanced in parallel. To balance each capacitor voltage without needing any parameter measurement, a vector decision diode algorithm is used in each cell to drive the two switches. Simulation and experimental results, for generating multilevel unipolar and bipolar HV pulses into resistive loads are presented.

Enhanced PCA-based localization using depth maps with missing data

In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.