Optimal Control of Voltage in Distribution Systems by Voltage Reference Management

Recently, renewable energy technologies such as wind turbine generators and photovoltaic (PV) systems have been introduced as distributed generations (DGs). Connections of a large amount of distributed generations may cause voltage deviation beyond the statutory range in distribution systems. A reactive power control of DGs can be a solution of this problem, and it also has a possibility to reduce distribution loss. In this paper, we propose a control methodology of voltage profile in a distribution system using reactive power control of inverters interfaced with DGs and tap changing transformers. In the proposed method, a one-day schedule of voltage references for the control devices are determined by an optimization technique based on predicted values of load demand and PV power generation. Reactive power control of interfaced inverters is implemented within the inverter capacity without reducing active power output. The proposed method accomplishes voltage regulation within the acceptable range and reduction of distribution loss. The effectiveness of the proposed method is confirmed by simulations. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Next-Day Peak Electricity Price Forecasting Using NN Based on Rough Sets Theory

Electricity price forecasting is an essential task for market participants in deregulated electricity market. This paper proposes an approach for next-day peak electricity price forecasting, since it is important for risk management and bidding strategy. In the proposed method, neural network (NN) is employed as the forecasting method, and its learning data is selected by using rough sets. Moreover, the creating method of learning data based on temperature fluctuation is also proposed for generation of new learning data in order to efficiently learn. This method is examined by using the data of Pennsylvania-New Jersey-Maryland (PJM) electricity market and The independent electricity system operator (IESO) market. From the simulation results, it is observed that the proposed method is useful for next-day peak electricity price forecasting. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Fuzzy unit commitment solution—A novel twofold simulated annealing approach

The authors propose a twofold simulated annealing (twofold-SA) method for the optimization of fuzzy unit commitment formulation in this paper. In the proposed method, simulated annealing (SA) and fuzzy logic are combined to obtain SA acceptance probabilities from fuzzy membership degrees. Fuzzy load is calculated from error statistics and an initial solution is generated by a priority list method. The initial solution is decomposed into hourly-schedules and each hourly-schedule is modified by decomposed-SA using a bit flipping operator. Fuzzy membership degrees are the selection attributes of the decomposed-SA. A new solution consists of these hourly-schedules of entire scheduling period after repair, as unit-wise constraints may not be fulfilled at the time of an individual hourly-schedule modification. This helps to detect and modify promising schedules of appropriate hours. In coupling-SA, this new solution is accepted for the next iteration if its cost is less than that of current solution. However, a higher cost new solution is accepted with the temperature dependent total cost membership function. Computation time of the proposed method is also improved by the imprecise tolerance of the fuzzy model. Besides, excess units with the system dependent probability distribution help to handle constraints efficiently and imprecise economic load dispatch (ELD) calculations are modified to save the execution time. The proposed method is tested using standard reported data sets. Numerical results show an improvement in solution cost and time compared to the results obtained from other existing methods.     

氢浆制备特性及其潜在应用

概述氢浆的制备特性及其在空间技术中用作推进剂的可能性     

An Altgernating Method Based on the VNA Solution for Analysis of Damaged Solid Containing Arbitrarily Distributed Elliptical Microcracks

This paper presents the development of an alternating method for the interaction analysis of arbitrary distributed numerous elliptical microcracks. The complete analytical solutions (VNA solutions) for a single elliptical crack in an infinite solid, subject to arbitrary crack-face tractions, are implemented in the present alternating method, together with the coordinate transformations for stress tensors. First, the present method is verified by solving the problems of two interacting cracks for which accurate numerical solutions have been obtained previously. Next, the present method demonstrates obtaining efficient and accurate solutions for the problems of many interacting elliptical cracks, which cannot be solved in a practical sense by the ordinary numerical methods such as the finite element method. Furthermore, damaged solids containing periodically distributed elliptical microcracks are analyzed by the present alternating method. The effective elastic moduli are evaluated for varying microcrack density. Detailed structures of the interactions in the damaged solids are visualized and clarified. This revised version was published online in August 2006 with corrections to the Cover Date.     

Three dimensional analysis of Fabry-Perot resonator

It is well known that the Fabry-Perot resonator generates an electric field concentrated in its central region. Therefore, we propose the use of the Fabry-Perot resonator as a microwave heating applicator. For microwave heating, it is necessary to analyze closely the electromagnetic field of the Fabry-Perot resonator. In this paper, we analyze the Fabry-Perot resonator by a numerical method, which is derived from the Bergeron formulation for the equivalent circuit of the three-dimensional electromagnetic field. Numerical results show that the electromagnetic field of the Fabry-Perot resonator is effective for microwave heating.     

Effectiveness of a backward mixing screw element for glass fiber dispersion in a twin‐screw extruder

In the kneading of glass–fiber‐reinforced plastics by twin‐screw extrusion, the use of a backward‐mixing screw (BMS) element for melt mixing has been found to be effective in dispersing glass–fiber bundles. In this study, we use computational fluid dynamics (CFD) to investigate the mechanism for the effectiveness of BMS for glass fiber dispersion. CFD of BMS melt mixing revealed that there is high uniformity of transport in the direction of extrusion and efficient transportation occurs from low‐stress to high‐stress regions. These findings demonstrate that BMS melt mixing is highly effective at imparting stress to the overall resin passing through. In addition, there is a correlation between the incidence of nondispersion of glass–fiber bundles measured experimentally and the stress history minimum value. On the basis of the above factors, we propose a method for predicting the operating conditions in which the nondispersion of glass–fiber bundles is controlled. The operating conditions for controlling glass–fiber nondispersion can be determined for various different mixing elements and the possible production rate can be predicted. Predictions for the operating conditions were applied to BMS and a forward kneading disk element (FKD). The effectiveness of BMS for controlling glass fiber nondispersion is characterized for a broad range of operating conditions. POLYM. ENG. SCI., 54:2005–2012, 2014. © 2013 Society of Plastics Engineers     

Production of new antioxidant compound from mycosporine-like amino acid,porphyra-334 by heat treatment

In this study, we evaluated the antioxidant activity (diphenylpicrylhydrazyl (DPPH)-radical scavenging activity) of the low-molecular weight fraction of water-soluble Susabinori (Porphyra yezoensis) extract (LMF). DPPH-radical scavenging activity of LMF (65.5 μmol-Trolox eq/ml) was enhanced at temperatures over 100 °C (1.4, 2.0 and 2.4 times higher at 100, 110 and 120 °C, respectively). As a result of HPLC separations, a newly produced peak was observed in heat treated LMF, while a marked decrease of another peak was found after heat treatment of LMF. The decreased peak was identified as porphyra-334, which is a typical mycosporine-like amino acid, by ESI/MSⁿ analysis (protonated molecule [M + H]⁺ at m/z 347.1 and its fragment ion 303.1). The newly produced compound with [M + H]⁺ at m/z 329.1 was found to be a dehydrated compound of porhyra-334. The compound exhibited an extremely high-IC₅₀ value of 10.1 μg/ml in DPPH-radical scavenging activity, compared to porphyra-334 (>1000 μg/ml).     

Optimisation strategy for an operational planning of a large photovoltaic system with enhanced electrical vehicles

Photovoltaic (PV) systems are rapidly gaining acceptance as some of the best alternative energy sources. In the installation area of a large PV system, appropriate operation is required, for example, of a method considering power output fluctuation and solar radiation forecast errors. In addition, it is better to minimise the size of the battery and its capital cost. This paper proposes an optimisation approach to determine the operational planning of the power output for a large PV system. This approach includes the method of determination of the charge/discharge amount for the battery of an electric vehicle as a demand response. The method aims to obtain a more beneficial deal with the sale of electrical power. The optimisation method applies the genetic algorithm to smoothen the fluctuating power output due to the PV system, and also to determine the initial state of charge of the battery. The validity of the proposed method is confirmed by the simulation results.     

Regulation of WTG dynamic response to parameter variations of analytic wind stochasticity

Although variable‐speed operation can reduce the impact of transient wind gusts and subsequent component fatigue, this is still an unknown factor that must now be quantified. Reduction in drive‐train stresses caused by fatigue loads in high wind turbulence is fundamental to realizing both output power leveling and long service life for a wind turbine generator (WTG). This paper presents an evolutionary controller comprising a linear quadratic Gaussian (LQG) and neurocontroller acting in tandem to effect optimal performance under high turbulence intensities, for a variable‐speed, fixed‐pitch WTG. The control objectives are maximum energy conversion and reduction in mechanical stresses on the system components. The proposed paradigm utilizes generator torque in controlling the rotor speed in relation to the highly turbulent wind speed, thereby ensuring the extracted aerodynamic power is maintained at a constant value, while shaft moments are regulated. The performance of the proposed controller is compared with that of the LQG and it is found that the former is more efficient in maintaining rated power, minimizing shaft torque variations, and shows robustness to parameter variations. Copyright © 2007 John Wiley & Sons, Ltd.