Dry air is one of the most popular insulation medium as SF₆ substitutes, and gas insulated switchgears which applied dry air have been manufactured since 2000 in Japan. Since dry air has only one-third insulation strength compared with SF₆ gas, dry air insulated switchgears need some improved techniques to downsize the equipment. This paper introduces a recent advance of dry air insulation techniques and their application to switchgears.

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This paper describes a water flow forecasting system for dams using neural networks and regression models. Water flow forecasting task is very important for reliable and economic operation. Many conventional methods have been used. They take much time to develop an accurate forecasting system, because it is difficult to adjust parameters.
Water flow forecasting system for dams, which have much flood data, can be developed by neural networks. On the contrary, that system for dams, which have few flood data, must be developed by regression models. The system has been used for three years at Tadami/Agano river basin to assure forecasting performance by the proposed method.

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Although load's dynamic behavior is well known to affect power system stability significantly, load's dynamics itself is not well known. As a result, power system stability assessment is very far from sufficient accuracy even today in 21^st century. Structure of existing power system load is assumed to be parallel composite of induction motor (IM) and constant impedance (CZ) behind a series reactance. Three parameters: IM ratio, inertia, and loading are identified by measured data using least square error method. The series reactance is identified by system aggregation method considering temporary system structure, and about 17% at peak load demand in studied 9 pure load areas. Effects by temporary included local generators can be corrected. Using 594 measured data of voltage sag accompanying network faults, the three parameters: IM ratio, inertia, and loading are identified as 55%, 0.6 second, and 70% respectively at summer peak demand when the voltage stability will become the worth. The parameters are slightly affected by season, demand, and area. All three parameters very slowly decrease by year.

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This paper deals with the introduction effects on the basis of the comparative study of residential PEFC (polymer electrolyte fuel cell) co-generation systems and conventional systems that consist of a conventional gas boiler or a condensing gas boiler or a CO₂ heat pump and the thermal power plant, by using the computer simulation. The target systems for estimation conform to real systems in the market and the energy demand data acquired from the past field tests was applied. As a result, it becomes clear that the residential PEFC co-generation systems have high performance in the energy saving and the CO₂ reduction, from a comparison study with conventional systems and CO₂ heat pump system. Concretely to say, the average energy saving rate that the residential PEFC co-generation system provides is 13.9% and the average CO₂ reduction rate is 16.5% using quantity of reduction of CO₂ as estimate function. Otherwise, the average energy saving rate that the CO₂ heat pump system provides is 13.7% and the average CO₂ reduction rate is 10.0%. Furthermore, we have proved the effectiveness a radiator in the residential PEFC co-generation system.

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In the power sector, the national government has set the goal that the introduction of PV reaches 53 million kW by 2030. However, large-scale introduction of PV will cause several problems in power systems such as surplus electricity. We need large capacity of pumped storages or batteries for the surplus electricity, but the construction costs of these plants are very high. On the other hand, in the transport sector, Electric Vehicle (EV) is being developed as an environmentally friendly vehicle. To promote the diffusion of EV, it is necessary to build infrastructures that can charge EV in a short time; a battery switch station is one of the solutions to this problem. At a station, the automated switch platform will replace the depleted battery with a fully-charged battery. The depleted battery is placed in a storage room and recharged to be available to other drivers. In this study, we propose the use of station's battery as a countermeasure for surplus electricity of PV and evaluate the economic value of the proposed system. We assumed that 53 million kW of PV is introduced in the nationwide power system and considered two countermeasures for surplus electricity: (1) Pumped storage; (2) Battery of station. The difference in total annual cost between Pumped case and Battery case results in 792.6 billion yen. Hence, if a utility leases the batteries from stations fewer than 792.6 billion yen, the utility will have the cost advantage in Battery case.

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Various artificial neural network (ANN) structures have been utilized to determine the maximum power points of PV system. The most common methods are radial basis function neural network (RBF), adaptive neuro-fuzzy inference system neural network (ANFIS) and three layered feed-forward neural network (TFFN). These ANN methods are recognized with simple computational techniques and high pattern recognition capabilities to deal with non-linear characteristic and intermittent output of PV system. However, there still might be strong and weak points for these methods during the optimization process. Since the characteristic of crystalline Silicon PV modules technology is almost similar, it is possible to select a single prominent ANN structure for identification the optimum points of this type solar cell technology. The paper discusses the most suitable ANN structure for estimation the MPP crystalline Silicon PV modules through their optimum operating voltages. To reach this objective, the ANN models have been trained and verified for multi-crystalline Silicon based edge defined film-fed growth (EFG) and wafer solar cell technologies, mono-crystalline Silicon and thin-film Silicon solar cell technologies. Then, the performance of ANN models is compared with hill-climbing (HC) based MPPT technique in terms of tracking the MPP voltage and the energy index.

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Partial discharge (PD) measurements are universally accepted as an effective and reliable tool for on-line condition monitoring of high voltage equipments. A relatively new application is conducting on-line high frequency PD measurements to detect falling trees on covered-conductor (CC) overhead distribution lines. In this paper, theoretical model of CC overhead distribution line is presented and the frequency-dependent distributed transmission line (TL) parameters, wave propagation characteristics (attenuation, phase constant, and propagation velocity), and characteristic impedance are calculated. The derived wave propagation characteristics are compared with those obtained from the time domain reflectometry (TDR) measurements to validate the theoretical model of the CC line. An accurate determination of the TL characteristics for CC lines can be used as a design aid to develop PD measuring system for condition monitoring; thus deciding the optimal locations of the sensors in getting reliable PD detection results to detect falling trees on CC overhead distribution lines.

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To improve the accuracy of measurement method for EDLC's equivalent circuit constants of internal resistance R and capacitance C, an energy based method is developed in this paper. The most important feature of the method is that we focus on the voltage V_c not at circuit terminal but at capacitance element and, using V_c and differential capacitance, estimate the discharge energy of U. This enables us to determine the energy loss at resistance I²Rt and then the effective values of R and C from I²Rt and U respectively. Through the theoretical discussions and test results, we confirm the effectiveness of the proposed method.

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Recently, a lightning surge is recognized as one of the biggest risks to the society. The lightning might give some influences not only on the lightning point but also on systems in nearby the point. In addition, if the surges induced by lightning invade into low voltage distribution lines, there might be a possibility of giving some severe damages to the systems. Therefore, it is necessary to examine these induced phenomena. FDTD (Finite Difference Time Domain) method which treats objects as three-dimensional solid circuits is applied to analyze these induced phenomena. VSTL is the surge analysis simulation program using FDTD method. In this paper, the lightning induced phenomena on the low voltage distribution line in the house are analyzed by using VSTL.

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