Predictive control for voltage collapse avoidance using a modified discrete multi-valued PSO algorithm

Voltage stability is one of the most challenging concerns that power utilities are confronted with, and this paper proposes a voltage control scheme based on Model Predictive Control (MPC) to overcome this kind of instability. Voltage instability has a close relation with the adequacy of reactive power and the response of Under Load Tap Changers (ULTCs) to the voltage drop after the occurrence of a contingency. Therefore, the proposed method utilizes reactive power injection and tap changing to avoid voltage collapse. Considering discrete nature of the changes in the tap ratio and also in the reactive power injected by capacitor banks, the search area for the optimizer of MPC will be an integer area; consequently, a modified discrete multi-valued Particle Swarm Optimization (PSO) is considered to perform this optimization. Simulation results of applying the proposed control scheme to a 4-bus system confirm its capability to prevent voltage collapse.     

Supporting multiple perspectives in feature-based configuration

Feature diagrams have become commonplace in software product line engineering as a means to document variability early in the life cycle. Over the years, their application has also been extended to assist stakeholders in the configuration of software products. However, existing feature-based configuration techniques offer little support for tailoring configuration views to the profiles of the various stakeholders. In this paper, we propose a lightweight, yet formal and flexible, mechanism to leverage multidimensional separation of concerns in feature-based configuration. We propose a technique to specify concerns in feature diagrams and to generate automatically concern-specific configuration views. Three alternative visualisations are proposed. Our contributions are motivated and illustrated through excerpts from a real web-based meeting management application which was also used for a preliminary evaluation. We also report on the progress made in the development of a tool supporting multi-view feature-based configuration.     

Parallel Lagrange interpolation on <Emphasis Type="Italic">k</Emphasis>-ary <Emphasis Type="Italic">n</Emphasis>-cubes with maximum channel utilization

This paper proposes an efficient parallel algorithm for computing Lagrange interpolation on k-ary n-cube networks. This is done using the fact that a k-ary n-cube can be decomposed into n link-disjoint Hamiltonian cycles. Using these n link-disjoint cycles, we interpolate Lagrange polynomial using full bandwidth of the employed network. Communication in the main phase of the algorithm is based on an all-to-all broadcast algorithm on the n link-disjoint Hamiltonian cycles exploiting all network channels, and thus, resulting in high-efficiency in using network resources. A performance evaluation of the proposed algorithm reveals an optimum speedup for a typical range of system parameters used in current state-of-the-art implementations.

A selective modified bentonite–porphyrin carbon paste electrode for determination of Mn(II) by using anodic stripping voltammetry

A novel voltammetric sensor based on chemically modified bentonite–porphyrin carbon paste electrode (MBPCE) has been introduced for the determination of trace amount of Mn(II) in wheat flour, wheat rice and vegetables. In this method Mn(II) gives well-defined voltammetric peak at the pH range of 3.5–7.5. For the preliminary screening purpose, the catalyst was prepared by modification of bentonite with porphyrin and characterized by thermogravimetric method (TG) and UV–vis spectroscopy. The detection limit (three times signal-to-noise) with 4 min accumulation is 1.07 × 10⁻⁷ mol L⁻¹ Mn(II). The peak currents increases linearly with Mn(II) concentration over the range of 6.0 × 10⁻⁷ to 5.0 × 10⁻⁴ mol L⁻¹ (r² = 0.9959). Statistical treatment of the results gave a relative standard deviation lower than 2.30%. The chemical and instrumental parameters have been optimized and the results showed that 1000-fold excess of the additive ions had not interferences on the determination of Mn(II).     

Magnetic and photocatalytic properties of CoFe2O4/Ni nanocomposites

Journal of Electroceramics - In this research, hard/soft CoFe2O4/Ni magnetic nanocomposite samples with different concentrations of Ni were successfully produced by a two-step mechanical alloying...     

Low voltage second-order alpha function synapse

This paper introduces a Transimpedance Amplifier (TIA) design capable of producing an incremental input resistance in the ohmic range, for input signals in the microampere range, such as are encountered in the design of instrumentation for electrochemical ampero-metric sensors, optical-sensing and current-mode circuits. This low input-resistance is achieved using an input stage incorporating negative feedback. In a Cadence simulation of an exemplary design using a 180 nm CMOS process and operating with?±?1.8 V supply rails, the input resistance is 1.05 ohms and the power dissipation is 93.6 µW. The bandwidth, for a gain of 100 dBohm, exceeded 9 MHz. For a 1µA, 1 MHz sinusoidal input signal the Total Harmonic Distortion, with this gain, is less than 1%. The input referred noise current with zero photodiode capacitance is 2.09 pA/√Hz and with a photodiode capacitance of 2pF is 8.52 pA/√Hz. Graphical data is presented to show the effect of a photodiode capacitance varying from 0.5 to 2 pF, when the TIA is used in optical sensing. In summary, the required very low input resistance, at a low input current level (µA) is achieved and furthermore a Table is included comparing the characteristics and a widely used Figure of Merit (FOM) for the proposed TIA and similar published low-power TIAs. It is apparent from the Table that the FOM of the proposed TIA is better than the FOMs of the other TIAs mentioned.

     

Internal electrical fault detection techniques in DFIG-based wind turbines: a review

The keys factor in making wind power one of the main power sources to meet the world’s growing energy demands is the reliability improvement of wind turbines (WTs). However, the eventuality of fault occurrence on WT com ponents cannot be avoided, especially for doubly-fed induction generator (DFIG) based WTs, which are operating in severe environments. The maintenance need increases due to unexpected faults, which in turn leads to higher operating cost and poor reliability. Extensive investigation into DFIG internal fault detection techniques has been carried out in the last decade. This paper presents a detailed review of these techniques. It discusses the methods that can be used to detect internal electrical faults in a DFIG stator, rotor, or both. A novel sorting technique is presented which takes into consideration different parameters such as fault location, detection technique, and DFIG modelling. The main mathematical representation used to detect these faults is presented to allow an easier and faster under standing of each method. In addition, a comparison is carried out in every section to illustrate the main differences, advantages, and disadvantages of every method and/or model. Some real monitoring systems available in the market are presented. Finally, recommendations for the challenges, future work, and main gaps in the field of internal faults in a DFIG are presented. This review is organized in a tutorial manner, to be an effective guide for future research for enhancing the reliability of DFIG-based WTs.