Mohammed Saaidia

DC-to-DC converters are Today widely used in power conversion systems that demand a continuous source and a continuous output, and the most prominent of these systems is the photovoltaic panels system, where dc-dc converters have become an integral part of this application, thanks to the advantages offered by dc-dc converters in this application as stability The system and performance improvement, as well as raising or lowering the output voltage compared to the input voltage, and increase the efficiency of the system and achieve the maximum power point that can be produced from photovoltaic panels by application of MPPT in addition to the ability to provide a fixed output voltage in the case of a variable input voltage due to natural factors such as a change in radiation and temperature. In this study, we describe Buck, Boost, Buck-Boost, CUK, and Zeta Converters, which are the most significant non-isolated DC-DC Converters that are frequently utilized in solar energy systems. This paper also provides an overview of recent studies for each converter; we also present a comparison between these converters, highlighting the most notable benefits and drawbacks of each converter.

Algeria is worldly known as one of the big producers and exporters of fossil fuels. However, by developing an adequate energy policy, it could become one of the great sun power (SP) producers and exporters. Indeed with almost 2 million Km2 sunny superficies, the country detains enormous SP energetic potential funds. However, eventual powerful SP plants deployment could be done far to the southern Algerian desert which makes HVDC technology the best candidate for the transmission of produced electricity towards the north region where are the main industrial poles and the most populated regions for local consumption and eventually farther to the north for electricity exportation to the European countries. The prospection work presented hereafter is a study on the feasibility of using HVDC technology for such possible projects according to country’s existing means and topographical and environmental characteristics.

As gas pipeline systems become larger and more complex, the importance of optimally operating and planning these facilities has increased. The capital costs and operating expenses of pipeline systems are so large that even small improvements in the use of the system can involve large sums of money. Purpose. This article proposes a method to improve the reliability and optimization of power supply systems for compressor stations. The novelty of the proposed work is the development of a new mathematical model that allows the choice of the most appropriate maintenance policies in the best way to significantly reduce costs as well as to optimize useful key performance indicators – failure rate, average time between breakdowns, the average repair time for equipment in compressor station electrical supply systems. Applying graph theory to represent this mathematical model from the schematic diagram of the different energy sources with respect to the five compressor stations is adequate. Methods. The problem that arises for the future operator or operator is, among others, how to balance two main aspects: a technical aspect and an economic aspect. The proposed methodology introduces a research algorithm to calculate the optimal values of the operating parameters of the power supply systems of compressor stations by combining technical and economic aspects in order to reduce costs and increase performance indicators. The proposed algorithm can be implemented in FORTRAN code. Results. The algorithm developed is an efficient tool for calculating maintenance costs and allows by means of programming to define the most appropriate maintenance policy. On the other hand, this technique could be used as an essential economic evaluation indicator for other equipment in order to choose among all the technically possible solutions the one which allow obtaining the best economic result. Practical value. The proposed algorithm has been examined in this third variant of the supply system with two turbogenerators. The result of the optimization shows a clear preference for selecting station C for the pipeline as this presents the minimum cost which is the definition of the algorithm optimizer. Then, it is important to adopt the most recommended maintenance policies and practices in order to ensure the availability of the power supply systems and to avoid unplanned outages with the resulting loss of production.

Face recognition is the most studied topic in the pattern recognition research field. This is probably due to the multiple useful applications which can be developed for important domains. Such deployed research efforts produced a huge number of methods, techniques and algorithms with different characteristics according to their simplicity, efficiency, robustness and speed. Present work investigates the performances of a simplified technique using a hierarchical classification scheme based on a constructed multi parts dictionary. The elementary features of the constructed dictionary were obtained using the well-known cross-correlation operator applied to the original images and their transformed images known as integral images and Discrete Cosine Transform. Hierarchical classification scheme is used to overcome the fact that this operator has high consumption time cost. The proposed strategy was implemented and tested on the images of the well known ORL and YALE database sets. Practical results demonstrate largely recognizable efficiency and speed characteristics.

HVDC technology is gradually becoming a standard in electrical energy transportation. Even more, in some situations it's the best practical solution. The research work presented hereafter is dedicated to the exploration of the basic notions, the induced gains, the basic components, the basic and extended structures, the applications and the real implemented projects of such technology. An overview on the research works concerned by the challenges of this expandable technology and an economic study of the deployment of real electrical transportation lines and their feasibility in Algeria are presented.

The research work presented hereafter is based on real recorded database of a PV plant in production and in connection with a classical electrical grid. Datasets were processed through two types of techniques. The first one, a classical type, is based on a predictive predefined model, however, the second one is an artificial intelligent method based on neural networks. Based on the carried out experiences, we proposed a new strategy to implement the proposed techniques. The results obtained through the two methods were compared which demonstrate the superiority of the AI method in terms of precision, generalization and robustness. Obtained results for each method were recorded, analyzed and compared.

Face recognition became a daily discipline in human life. At the work, with our PDP and smart phones, for our daily help, our security and many other utilities, face recognition has crossed the laboratory doors and colonized the human quotidian. However, the effectiveness of the developed applications still encounters many challenges. The presented work in this paper tries to deal with these challenges by proposing an advanced characterization way to enrich the feature vectors used by the classifiers to verify or identify faces. This process was done by compiling three types of feature vectors. Each type encapsulates a specific type of face information. At first, we compile a feature vector related to the geometric information of the face using Zernike moments; then spectral components using DCT are extracted to form the second type of feature vectors and finally, the last feature vector type is formed by compiling the texture and luminance information using LBP. The three vector types are then combined to form an enriched feature vector which was post-processed through a feature selection method then presented to the input of a neural network classifier. The validation experiments were realized on the XM2VTS and ORL database and recognition rates of 93.3% and 92.5% were respectively recorded for XM2VTS and ORL database.

The field oriented control (FOC) can provide the same performance as is achieved from a separately excited DC motor. However, this technique requires the mechanic sensor to determine correctly the orientation of the rotor flux vector and is very sensitive to the variation of motor parameters, results in an undesirable coupling between the flux and the torque of the machine, and loss of dynamic performance. To solve these problems, this paper proposes a global stability and robust nonlinear controller applied to the ideal induction motor speeding system, and dynamic decoupling of rotor speed and flux. A comparative study between the performances of the proposed controller and field oriented control is carried out. In presence of rotor resistance variations and load torque disturbance the simulation results show the proposed control method behaves preferable dynamic decoupling performance and robust performance.

Facial expression recognition became an important research subject for its diverse applications in human machine interaction. However, many challenges still to be overcome. By the presented work in this paper, we try to provide a new facial expression recognition technique based on combined vectors of multi-feature characterisation of the face. Thus, the face within an image is firstly localised using a simplified method, then it will be characterised in three different ways; by obtaining its Zernike moments feature vectors, known to compact geometric characteristics of the image, then by compiling AR model, supposed to be a representation of its spectral source model and at last, a statistical distribution analysis of the luminance information is performed through the LBP method. Obtained feature vectors were used to train neural network classifiers (NNC) in different manner. To demonstrate the effectiveness of the proposed technique, we record and compare recognition rates for NNC trained with each type of feature vector firstly, then for NNC trained with directly combined feature vectors and finally for NNC trained with composite feature vectors which underwent a fuzzy linear projection operation. Experiments were performed on the JAFFE and Yale database. Recorded results along with comparisons to other methods have affirmed the potency of the proposed approach attaining promising results compared to those reported in the literature.