To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Pembangunan model penentuan keperluan perumahan kajian kes: Johor Bahru, Malaysia

Perumahan merupakan satu komponen penting dalam pembangunan ekonomi di mana ia telah menjadi dasar kerajaan untuk menyediakan rumah bagi setiap rakyat. Rancangan Malaysia terdahulu telah cuba merancang bagi merealisasikan dasar ini. Walaupun anggaran keperluan perumahan dibuat di bawah Rancangan Malaysia, namun anggaran tersebut tidak membayangkan keperluan sebenar pembeli dan penyewa rumah di Malaysia. Negara-negara maju telah menggunakan pelbagai model dalam menentukan keperluan perumahan. Namun begitu, model-model tersebut tidak sesuai digunakan di Malaysia kerana data yang terhad. Kajian ini memfokuskan kepada dua objektif iaitu, mengenal pasti model dan faktor yang signifikan bagi menentukan keperluan perumahan, dan kedua menghasilkan model penentuan keperluan perumahan di Malaysia. Skop kajian ini tertumpu kepada pembeli dan penyewa rumah di Daerah Johor Bahru yang dipilih melalui kaedah pesampelan kelompok pelbagai peringkat. Data diperolehi melalui borang kaji selidik dan dianalisis menggunakan pendekatan kuantitatif. Analisis statistik deskriptif digunakan bagi menghuraikan taburan kekerapan, peratus, min, dan sisihan piawai manakala statistik inferensi iaitu ujian Korelasi Pearson dan Regresi Pelbagai digunakan untuk pembentukan model. Dengan menggunakan kaedah Enter, satu model yang signifikan dapat dihasilkan (F4,178 = 353.699 p < 0.05. Adjusted R square = .886) yang signifikan terhadap dua faktor utama iaitu demografi dan kemampuan. Model yang dihasilkan bagi kajian ini adalah General Linear Model. Model ini dapat digunakan bagi menentukan keperluan perumahan di Johor Bahru. Ia juga berfungsi sebagai alat penting dalam perancangan sektor perumahan pada masa hadapan di Malaysia

Observation of charge ordering signal in monovalent doped Nd0.75Na0.25-xKxMn1O3 (0 ≤ x ≤ 0.10) manganites

K doping in the compound of Nd0.75Na0.25-xKxMn1O3 (x = 0, 0.05 and 0.10) manganites have been investigated to study its effect on crystalline phase and surface morphology as well as electrical transport and magnetic properties. The structure properties of the Nd0.75Na0.25- xKxMnO3 manganite have been characterized using X-ray diffraction measurement and it proved that the crystalline phase of samples were essentially single phased and indexed as orthorhombic structure with space group of Pnma. The morphological study from scanning electron microscope showed there was an improvement on the grains boundaries and sizes as well as the compactness with K doping suggestively due to the difference of ionic radius. On the other hand, DC electrical resistivity measurement showed all samples exhibit insulating behavior. However, analysis of dlnρ/dT-1 vs. T revealed the clearly peaks could be observed at temperature 210K for x = 0 and the peaks were shifted to the lower temperature around 190 K and 165 K for x = 0.05 and x = 0.1 respectively, indicate the existence of charge ordering (CO) state in the compound. Meanwhile, the investigation on magnetic behavior showed all samples exhibit transition from paramagnetic phase to anti-ferromagnetic phase with decreasing temperature and the TN was observed to shift to lower temperature suggestively due to weakening of CO stat

Investigation on Mlp Artificial Neural Network Using FPGA For Autonomous Cart Follower System

Dengan kos alat pengesan yang semakin rendah, masa depan sistem pedati pengikut autonomi akan dilengkapi dengan lebih banyak alat pengesan. Ini menjadi cabaran rekabentuk dalam mengendalikan data besar dan kerumitan perkukuhan. Kebanyakan sistem yang sedia ada menggunakan papan mikropengawal yang mempunyai prestasi yang terhad dan pengembangan tidak mungkin tanpa penggantian yang lebih baru. Projek ini mencadangkan perlaksanaan alternatif sistem pedati pengikut autonomi dengan model rangkaian neural MLP menggunakan FPGA. Sistem pedati pengikut autonomi yang mengguakan papan mikropengawal telah diubah suai untuk menggunakan papan FPGA dan dilaksanakan melalui Sistem pada Chip (SOC). System rangkaian neural dilatih dalam simulasi dengan vektor latihan yang dikumpul daripada sistem pedati pengikut autonomi yang sedia ada. System rangkaian neural kemudian dilaksanakan sebagai perkukuhan dalam SOC itu. Dalam pemerhatian, jejak perkukuhan model rangkaian neural kekal saiz kecil tanpa mengira saiz rangkaian neural. Hasil kajian menunjukkan bahawa dengan penggunaan sumber tambahan sebanyak 40%, penambahbaikan sistem secara keseluruhan sebanyak 27 kali dicapai dengan penggunaan blok pecutan perkakasan di SOC, berbanding dengan SOC tanpa penggunaan blok pecutan perkakasan. ________________________________________________________________________________________________________________________ The future of the autonomous cart follower system will equipped with lots of sensory data, due to the ever lower cost of sensory device. This provides design challenge on handling large data and firmware complexity. Most of the existing systems are implemented via usage of microcontroller board, which has limited performance and expansion is not possible without replacement of newer board. The project proposes an alternative approach of running the autonomous cart follower systems on neural network model using Field Programmable Gates Array (FPGA). A microcontroller based autonomous cart follower systems is modified to use the FPGA board and implemented via the System on Chip (SOC) approach. The neural network is trained offline in simulation tools with training vector collected from running the existing autonomous cart follower systems. The trained neural network model then implemented as software code in the SOC. By observation the firmware footprint of the neural network model remains small size regardless of the neural network size. The result shows that with 40% more additional resource utilization, the overall system improvement of 27 times is achieved with the usage of hardware acceleration block in SOC compared to SOC without hardware acceleration

Strategy to reduce transient current of inverter-side on an average value model high voltage direct current using adaptive neuro-fuzzy inference system controller

Growing-up of high voltage direct current (HVDC) penetration into modern power systems (PS) makes difficulty on the PS operation. The HVDC produces high and slow transient current (TC) at start-time, especially for higher up-ramp rate (Urr&gt;20 pu/s). Its condition makes the HVDC cannot be linked and synchronized into the PS rapidly. A strategy to reduce the TC is proposed by an adaptive network based fuzzy inference system (ANFIS) control on inverter HVDC-link to cope up this problem. The ANFIS control is tuned with the help of conventional control in various train-data by using offline mode. Response of ANFIS scheme is improved by suppressing TC at the values of 3.75% for the both phases (A and C), and 3.95% for phase B, for the Urr=30 pu/s. While the conventional control achieved at 9.1% for the both phases (A and B), and 9.2% for the phase C. The ANFIS control gives shorter settling time (0.553 s) than the conventional control (0.584 s) for all phases. The proposed control is more effective than the conventional control at all the scenario

NEURAL NETWORKS FOR DECISION SUPPORT: PROBLEMS AND OPPORTUNITIES

Neural networks offer an approach to computing which - unlike conventional programming - does not necessitate a complete algorithmic specification. Furthermore, neural networks provide inductive means for gathering, storing, and using, experiential knowledge. Incidentally, these have also been some of the fundamental motivations for the development of decision support systems in general. Thus, the interest in neural networks for decision support is immediate and obvious. In this paper, we analyze the potential contribution of neural networks for decision support, on one hand, and point out at some inherent constraints that might inhibit their use, on the other. For the sake of completeness and organization, the analysis is carried out in the context of a general-purpose DSS framework that examines all the key factors that come into play in the design of any decision support system.Information Systems Working Papers Serie

Induction Motors Bearing Failures Detection and Diagnosis Using a RBF ANN Park Pattern Based Method

International audienceThis paper deals with the problem of bearing failure detection and diagnosis in induction motors. The proposed approach is a sensor-based technique using the mains current and the rotor speed measurement. The proposed approach is based on the stator current Park patterns. Induction motor stator currents are measured, recorded and used for Park patterns computation. A Radial Basis Function (RBF) Artificial Neural Network (ANN) is then used to automate the fault detection and diagnosis process. Experimental tests with artificial bearing damages results show that the proposed method can be used for accurate bearing failures detection and diagnosis in induction motors