In recent years, rapid introduction of photovoltaic power systems (PVs) causes voltage rise in distribution systems. In addition, residential PVs cause voltage unbalance in distribution systems. For these reasons, it has become difficult to control voltage in distribution systems to within the allowable range. Therefore, it is necessary to consider the voltage unbalance suppression method corresponding to the measured data in unbalanced distribution systems. Thus, we measured voltage on the distribution lines which are expected to large voltage imbalance and analyzed the reality of voltage unbalance. Also, Static Var Compensator (SVC) is suitable for voltage unbalance suppression, although it is expensive. Therefore, we make the distribution system model using the measured voltage in distribution systems to verify the voltage unbalance suppression effect.
In order to develop a watching system for an aged single person by grasping his or her usage of electricity, we hatched out a method to infer definitely whether a resident directly operated electric appliances and it resulted in a significant increase in electricity consumption or not from total load current of the household, which was averaged at an appropriate interval. Then we evaluated the method by using the total load current measured at 11 households of an actual aged single persons for 1 year (totally 3,724 days) and records on their outing. The results of the evaluation showed an effectiveness of the method.
This paper presents a confidence interval estimating method for load forecasting with consideration of error causes. In recent years, it has been planned to introduce the new balancing rules by Ministry of Economy, Trade and Industry (METI). It will be required to submit the planned values of power generation amount and load amount. Thus, it is necessary to evaluate the confidence interval for load forecasting in terms of power producer and supplier. The proposed method uses the decision tree to analyze error causes and the beta distribution to estimate proper confidence interval. The former has functions to classify data into terminal nodes and extract rules from each terminal node with consideration of error causes. The latter expresses a variety of distribution shape by parameters estimated maximum likelihood for obtained error distribution. The effectiveness of the proposed method is demonstrated using actual data of considerable local electric power utility. The proposed confidence interval estimating method succeeded in reducing 5.0%pt of Prediction Interval Coverage Probability (PICP) and 0.9%pt of Normalized Mean Prediction Interval Length (NMPIL) for the conventional method without consideration of error causes. Therefore the proposed method can estimate more accurately than conventional method.
The paper proposes a method to quantify the effect of critical peak pricing (CPP) Demand-Response (DR) for residential customers. The method uses electricity consumption measured at all circuits of a distribution board. Operation of appliances and rooms connected to each circuit and corresponding electricity consumption are first detected. By using the data, the baseline of electricity demand during DR period is estimated. The combination of the operation of circuits is defined as to be transition state modeled as Markov Chain. Then, electricity consumption is quantified by summing up electricity consumptions of all circuits corresponding to the estimated state. Due to the method, the DR effect can be distinguished into those due to the change in operation schedule of appliances and rooms and the change in the service-level provided by appliances and in rooms. This paper demonstrates the method by applying it to a DR experiment result.
In this paper, a new technique of daily peak load forecasting is proposed. At first, we propose Non-linear correction T method as improvement technique of T method that is one of the multivariate analysis techniques, called MT system, suggested in Quality Control. Moreover, we examine the daily peak load forecasting technique, which is based on data in the last year on a forecasting day, by T method and Non-linear correction T method. Furthermore, we examine the technique, which is based on the data of most recent several weeks of a forecasting day, by the T method and Non-linear correction T method. In addition, the effectiveness of the proposed method by comparing the multiple regression analysis is discussed.
After the liberalization of electricity market in Japan, New entry electricity suppliers start to supply electricity to residential sectors. Because they don't have as many power generation resources as conventional electric companies, they need to know whether their customer's electricity demand pattern match with their generation resources characteristics for avoid procuring shortage electricity from electricity trade market. In addition, they need to estimate electricity demand accusatory in mid to long term for business planning. In this paper, as a basic study for estimation of aggregated households' electricity demand pattern characteristics, we survey 1,720 households about their family structure and monthly electricity usage in one or two years, and evaluate monthly electricity demand characteristics to classify seasonal electricity demand ratio pattern against annual average electricity demand. As a result, we classify 1,720 households in 15 groups and correlate the household ratio of each group and household's attributes such as house type and number of family members. Then, we propose a method to estimate the monthly electricity demand of aggregated households based on the ratio of each group.
The recent growth in penetration of photovoltaic generation units (PVs) has brought new operation ideas to Power Producer-Suppliers (PPSs) and/or Megawatt-Solar Power Plant (MSPP) owners which achieve more profitable electric power management for them. Their expectable profit will improve if the PPSs operate their controllable generators (CGs) appropriately, and sell the generated electricity properly to contracted customers and/or Power Exchanges together with the output of MSPPs. Furthermore, the profitable cooperation between the PPSs and the MSPP owners decrease the difficulty in supply and demand balancing operation of main power grids. Hence we can expect that the profitable cooperation will become one of the most efficient PV-compatible operations with few adverse effects for the main power grid's operation. However, when the PPSs cannot procure sufficient electricity by only their CGs and the output of MSPPs, there is a risk to purchase the very expensive electricity from the main power grids to compensate the shortage. This paper presents a problem framework and its solution to make the optimal power management plan for the PPS considering the electricity procurement from the MSPPs. The aim of this research is to design a profitable and stable power management plan for the PPS with the intension of providing both better selling profit to the PPS and the MSPP owners, and reducing difficulty in supply-demand balancing operation, which is caused by the prediction uncertainty of PV output, to the main power grids. In addition, in order to verify the usefulness of the authors' proposal, numerical simulations are carried out and their results are discussed.
This paper describes about the process to obtain the position of a rotating and swaying cargo of a jib crane by using an Inertia Measurement Unit (IMU) which is possible to measure the attitude of a moving object. A number of researches on transporting and anti-swaying control of a crane are reported up to now. However, those related to a jib crane as against a overhead crane are few because the cargo of a jib crane is easy to rotate by the external effect such as acceleration and deceleration operations and disturbance. Therefore, we used an IMU as a sensor to measure the attitude of a cargo, and then we proposed one approach to obtain the position of a cargo by the relation between the coordinate system of a cargo mounted an IMU (ZYX Euler's angle) and the one of a rope (ZYZ Euler's angle) even if a cargo was rotating and swaying by wind. As a result, we could confirm the effectiveness for the position detection in the experiments, and also get good performances for transporting and anti-sway control of a jib crane.
We built a virtual desktop system for operating multiple virtual desktops on one physical server. We have already built a virtual desktop system for approximately 34,000 users. Previously, we propose a way of preventing the significant rise in the load on resources such as a disk I/O, a CPU, a network, a memory when we manage users in a virtual desktop system. And we evaluated by simulation of a proposed method for managing the load imposed on resources in a virtual desktop system showed that it effectively prevents significant CPU load increases. In this paper, we evaluate by simulation of a previously proposed method effectively prevents significant CPU load even if other resources (a disk I/O, a network, a memory) load increases. Use of this method would enable virtual desktops to be managed more effectively.
It is general to use a fisheye camera for obtaining a full view image, but the images obtained by a fisheye camera are not rotation invariant, that is, the distortion of scenes on the image varies with their place of image. On the other hand, the content of a spherical image is invariant with the camera rotation and scenes do not vary. In this paper, we propose to detect corners on spherical images. We map a full view image to a discrete spherical image for obtaining a rotation invariant spherical image, and modify the conventional FAST (Features from Accelerated Segment Test) corner detector for directly applying it to the discrete spherical image.
Verification of image manipulation for various alterations is an indispensable technology to protect original images. Conventional forgery detection methods using fingerprint are useful, since the original image is not required in detection process. However, manipulated local region can't be specified. Further, type of manipulation is not identified. In this paper, a novel forgery identification method based on reversible histogram shift is proposed. Our method can detect partial and overall manipulation regions and identify manipulation type. Simulation results are shown to demonstrate the effectiveness of our method using various types of manipulated database images.
We evaluate the proposed method that estimates acceptable room temperatures, by French residential data. By distributing HEMS sets to 200 French volunteer families, we have collected data of smart plugs and thermometers from April of 2014 to March of 2015. By applying the proposed method to the French volunteer residential data of winter, we obtain 0.4 degree Celsius reduction on average by comparing the room temperature means to the minimum acceptable temperatures estimated by the proposed method. This result indicates that our proposed method may be useful for inhabitants in France, in addition to those in Japan.
We propose a method for shared file cache function for cloned files used by virtual machines, called SCC. The file clone function copies the files faster than conventional (read and write) method. Moreover, the function reduces disk spaces. The function is used to deploy virtual machines in virtual desktop infrastructure because of fast copying a lot of virtual machine disk files. SCC uses the file cache of a shared file as a shared cache among cloned files. The cached data on the shared file are returned to application programs on accessing the shared file via cloned files. Therefore, SCC improves the I/O performance of the shared file due to avoiding disk accesses. In this paper, we implement SCC and evaluate the I/O performance. From the evaluation, we have found SCC improves I/O throughput about 38 times in the case of random read and shared cache hit.
The third-party verification test aims to detect potential defects by test specialty division, and judge result based on specific criteria. Usually third-party verification was used for stand-alone products and embedded system products, but few instances of the third-party verification for system products have been reported. Furthermore, verification reports simply list assessment results for test items and for faults being detected, but the locations of their occurrence and possible causes were not pointed out. This paper proposes a method of the third-party verification for system products. The method estimates fault locations and possible causes. The method performs isolation of fault location and produces an estimation of fault cause using interface tracing between subsystems and the operating environment log. This third-party verification method was applied to a remote operating system for household appliances. Its effectiveness for the estimation of fault location and causes was confirmed.