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Purpose The density method is one of the powerful topology optimization methods of magnetic devices. The density method has the advantage that it has a high degree of freedom of shape expression which results in a high-performance design. On the other hand, it has also the drawback that unsuitable shapes for actually manufacturing are likely to be generated, e.g. checkerboards or grayscale. The purpose of this paper is to develop a method that enables topology optimization suitable for fabrication while taking advantage of the density method. Design/methodology/approach This study proposes a novel topology optimization method that combines convolutional neural network (CNN) as an effective smoothing filter with the density method and apply the method to the shield design with magnetic nonlinearity. Findings This study demonstrated some numerical examples verifying that the proposed method enables to efficiently obtain a smooth and easy-to-manufacture shield shape with high shielding ability. A network architecture suitable as smoothing filter was also exemplified. Originality/value In the field of magnetic field analysis, very few studies have verified the usefulness of smoothing by using CNN in the topology optimization of magnetic devices. This paper develops a novel topology optimization method that skillfully combines CNN with the nonlinear magnetic field analysis and also clarifies a suitable network architecture that makes it possible to obtain a target device shape that is easy to manufacture while minimizing the objective function value.

Purpose This paper aims to further improve the efficiency of multi-objective optimization design of synchronous reluctance motors (SynRMs) using the level set (LS) method, which has the advantage of obtaining a practical shape. The solutions obtained by gradient methods tend to be local ones due to the multi-modality of the objective function, especially when multiple objective functions. A huge number of trial calculations are required to obtain a high-quality and broadly distributed Pareto front. Therefore, it is indispensable to effectively get out of the local solutions in the optimization process with the LS method. Design/methodology/approach The authors propose a novel method appropriately switching multiple objective functions with high independence of sensitivity information. The authors adopt highly independent mathematical expressions for the objective functions of the average torque and torque ripple. In addition, the authors repeatedly perform the optimization while appropriately selecting the sensitivity information of one objective function from multiple ones, which enables the authors to effectively break out of local solutions in the optimization process. Findings The proposed method was applied to the shape optimization of SynRM flux barriers and successfully searched a more extensive and advanced Pareto front in comparison with the conventional method. Originality/value The proposed method adopts search spaces with mathematical high independence for average torque and torque ripple. In the optimization process, when the solution search is judged to get stuck by several criteria, the search space is alternately switched to effectively get out of local solutions.

We proposed a new multi-objective optimization approach combining both gradient calculation in actual high-dimensional design space and that in a low-dimensional latent space that is an appropriately compressed design space with Auto-Encoder. The proposed approach enables us to quickly and precisely search for more global pareto solution group in multi-objective optimization by using the optimization results in the low-dimensional space as the initial value of the gradient calculation in the actual design space.

In Japan, low thermal insulation performance in existing houses is a considerable issue that may lead to health problems. Therefore, it is necessary to acquire technologies and knowledge regarding thermal insulation renovation. On the other hand, with the spread of photovoltaic panels, urban scale energy management and self-consumption have become important. The purpose of this study is to introduce a net-zero energy house which was designed, built, and analyzed by the team of Waseda University and Shibaura Institute of Technology in the ENEMANE HOUSE 2017 competition. The team proposed a renovation method which insert highly insulated walls into the existing structure, creating not only highly insulated areas but also non-insulated areas called “loggias.” By opening and closing the windows of the loggia according to the season, it functions as a passive system and enables residents’ behavioural adaptation to the environment. On the other hand, to aim for self-consumption and leveling of reverse power flow, a heat pump water heater and a storage battery were operated according to solar radiation prediction. Actual measurements confirmed the implementation and effect of this operational method. Moreover, the energy consumption was greatly reduced, while thermal comfort was maintained by thermal insulation and efficient equipment introduction.

Purpose The purpose of this paper is to solve efficiently the topology optimization (TO) in time domain problem with magnetic nonlinearity requiring a large-scale finite element mesh. As an actual application model, the proposed method is applied to induction heating apparatus. Design/methodology/approach To achieve TO with efficient computation time, a multistage topology is proposed. This method can derive the optimum structure by repeatedly reducing the design domain and regenerating the finite element mesh. Findings It was clarified that the structure derived from proposed method can be similar to the structure derived from the conventional method, and that the computation time can be made more efficient by parameter tuning of the frequency and volume constraint value. In addition, as a time domain induction heating apparatus problem of an actual application model, an optimum topology considering magnetic nonlinearity was derived from the proposed method. Originality/value Whereas the entire design domain must be filled with small triangles in the conventional TO method, the proposed method requires finer mesh division of only the stepwise-reduced design domain. Therefore, the mesh scale is reduced, and there is a possibility that the computation time for TO can be shortened.

In this paper, the nonconforming infinite edge element method with a reference line is developed for fast magnetic field analysis of infinitely long models such as railway system. The proposed method is effective for reducing calculation burden while retaining computational accuracy even in far region.

Electric retailers need to predict spot market prices in the Japan Electric Power Exchange for efficient daily power procurement. And it is necessary to predict demand and PV output when predicting the price. Therefore, this paper deals with the simple and accurate estimation methods of total PV power over a wide area. We propose two kinds of simple methods to calculate the total PV output in the Kanto and Tohoku areas in Japan based on the predicted values of solar radiation at multiple observation points that are easy to obtain from the Japan Meteorological Agency.

The introduction of photovoltaic power generation has been increasing rapidly. Recently, the novel PPA (Power Purchase Agreement) model has been proposed aiming at further introduction of solar power generation in Japan. In the model, an electric power retailer installs its own PV system on the premises or roof of a customer and sells the generated power to the customer during long contract periods as well as power utilities. In this paper, we calculated the profits of the electric power retailer and CO2 emissions, customers costs in the PPA model and propose the design method of a power supply plan.

This study explored the mechanical factors that determine accuracy of a baseball pitching. In particular, we focused on the mechanical parameters at ball release, referred to as release parameters. The aim was to understand which parameter has the most deterministic influence on pitch location by measuring the release parameters during actual pitching and developing a simulation that predicts the pitch location from given release parameters. By comparing the fluctuation of the simulated pitch location when varying each release parameter, it was found that the elevation pitching angle and speed significantly influenced the vertical pitch location, and the azimuth pitching angle significantly influenced the horizontal pitch location. Moreover, a regression model was obtained to predict the pitch location, and it became clear that the significant predictors for the vertical pitch location were the elevation pitching angle, the speed, and spin axis, and those for the horizontal pitch location were the azimuth pitching angle, the spin axis, and horizontal release point. Therefore, it was suggested that the parameter most affecting pitch location weas pitching angle. On the other hand, multiple regression analyses revealed that the relation between release parameters varied between pitchers. The result is expected to contribute to an understanding of the mechanisms underlying accurate ball control skill in baseball pitching.

Purpose Shape optimization using the level-set method is one of the most effective automatic design tools for electromagnetic machines. While level-set method has the advantage of being able to suppress unfeasible shape, it has a weakness of being unable to handle complex topology changes such as perforate at material region. With this method, it is only possible to define simple connected topology, and it is difficult to determine the optimal shape which has holes. Therefore, it is important to efficiently expand the searching area in the optimization process with level-set method. Design/methodology/approach In this paper, the authors introduce the newly defined hole sensitivity which is based on concept of topological derivatives, and combine it with level-set method to effectively create holes in the search process. Furthermore, they consider a variable bandwidth of gray scale, which indicates the transition width between air and magnetic body and combine it with the hole creation method described above. With these methods, the authors aim to expand the searching area in comparison with the conventional level-set method. Findings As a result of applying the proposed methods to a magnetic shielding problem, the multi-layered shielding which effectively reduces the magnetic flux in the target area, is successfully produced. Originality/value The proposed methods enable us to effectively create a hole and to expand the searching area in the topology optimization process unlike in the case of conventional level-set method.

PurposeThe purpose of this paper is the improvement of topology optimization. The scope of the paper is focused on the speedup of optimization. Design/methodology/approachTo achieve the speedup, the method of moving asymptotes (MMA) with constrained condition of level set function is applied instead of solving the Hamilton–Jacobi equation. FindingsThe acceleration of convergence of objective function is drastically improved by the implementation of MMA. Originality/valueNormally, the level set method is solved through the Hamilton–Jacobi equation. However, the possibility of introducing mathematical programming is clear by the constrained condition. Furthermore, the proposed method is suitable for efficiently solving the topology optimization problem in the magnetic field system.

The introduction of photovoltaic power systems is being significantly promoted. This paper proposes the implementation of a distributed energy management framework linking demand-side management systems and supply-side management system under the given time-of-use pricing program for efficient utilization of photovoltaic power outputs; each system implements a consistent management flow composed of forecasting, operation planning, and control steps. In our framework, demand-side systems distributed in the electric distribution network manage individual energy consumption to reduce the residential operating cost by utilizing the residential photovoltaic power system and controllable energy appliances so as not to inconvenience residents. On the other hand, the supply-side system utilizes photovoltaic power maximally while maintaining the quality of electric power. The effectiveness of the proposed framework is evaluated on the basis of an actual Japanese distribution network simulation model from both the supply-side and demand-side viewpoints.

Because a synchronous reluctance motor (SynRM) does not require permanent magnets, its industrial applications focus on lowering manufacturing costs. However, the SynRM has disadvantages. It experiences a lower average torque and oscillations derived from torque ripple compared to a permanent magnet-based motor. Therefore, the improvement of torque characteristics is investigated using topology optimization (TO) method which allows a greater variety of rotor structure than the shape optimization technique and does not require the concrete initial structure. Although the level set method is well known among TO methods, the smoothed-heaviside-function-based TO implemented by method of moving asymptotes is more suitable due to the feasibility of implementing the physical structure and the convergence speed. In this paper, the reasonable structure of rotor core in SynRM is investigated using TO method to improve the torque characteristic.

It is very important to design electrical machineries with high efficiency from the point of view of saving energy. Therefore, topology optimization (TO) is occasionally used as a design method for improving the performance of electrical machinery under the reasonable constraints. Because TO can achieve a design with much higher degree of freedom in terms of structure, there is a possibility for deriving the novel structure which would be quite different from the conventional structure. In this paper, topology optimization using sequential linear programming using move limit based on adaptive relaxation is applied to two models. The magnetic shielding, in which there are many local minima, is firstly employed as firstly benchmarking for the performance evaluation among several mathematical programming methods. Secondly, induction heating model is defined in 2-D axisymmetric field. In this model, the magnetic energy stored in the magnetic body is maximized under the constraint on the volume of magnetic body. Furthermore, the influence of the location of the design domain on the solutions is investigated.

Topology optimization (TO) has advantages over shape optimization; for example, the design space is wider and the degrees of the shape freedom is larger. The convergence characteristic in the conventional level set (LS) method is slow because of the limitation of the time step size that is used for solving the simplified Hamilton-Jacobi equation, which is normally defined as the width of one finite element in the design domain. To overcome this difficulty, convergence acceleration using the method of moving asymptotes is investigated. The performance of the proposed method is compared with the conventional LS method for a 3-D TO problem regarding the magnetic shielding and an interior permanent magnet motor in a 3-D nonlinear magnetostatic field.

Recently, the deep learning technology attracts much attention in various industrial fields. In our previous research, we developed an Encoder-Decoder precisely reproducing the optimization process of conventional optimization method, that is, the level-set method which is one of the gradient methods, by means of Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM). The developed method enables us to implement high speed search of solutions, which means the possibility of better and effective optimization starting with various initial shapes. This method can deal with only the initial shape as design parameter for optimization. Thus, it is necessary to re-train the Encoder-Decoder when the design conditions change, e.g., the displacement of permanent magnet. To overcome this drawback, we have developed a novel network structure to incorporate the design conditions into the training data. Finally, to confirm the validity of the proposed method, we evaluate its calculation time and computational accuracy by using a magnetic circuit design model.

Synchronous reluctance motor (SynRM) attracts attention because of high price of rare earth in recent years. In order to improve its torque characteristics, optimization of the rotor core structure using topology optimization has been reported. However mechanical performance such as rigidity may be deteriorated. In this paper, coupled topology optimization method based on magnetic and structural analysis of electrical machine is verified in the SynRM. As a result, optimized structure with high equipment rigidity was obtained while maintaining the torque characteristics.

The finite element analysis (FEA) of magnetic field generally requires a lot of calculation time. Especially, design optimization methods such as the level-set method with FEA result in large computational effort to find better solution. In this paper, we propose a novel method of precisely and quickly reproducing the conventional optimization steps by means of Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM). The developed method enables us to implement high speed search of solution, which means the possibility of effective optimization with various initial conditions for better solution. Finally, we evaluate calculation time and computational accuracy of the proposed method by using a magnetic circuit design model.

In the design optimization of electric machine, we ordinarily derive the objective physical quantities, e.g., the shape of the investigated model as design variables, by using numerical method such as the finite element method with the analysis conditions. In recent years, the representation learning using Deep Learning much attracts attention because it can acquire the features of data as a distributed representation and reproduce corresponding data. In this paper, utilizing machine learning technology, we propose an application of Conditional Variational Auto-Encoder (CVAE) to reproduce the more adequate shape of magnetic materials, i.e., design variables, corresponding to the intended magnetic energy, i.e., objective function values.

In Japan, a number of residences have introduced PV system after the Feed-in-tariff program started in 2009. After the program is over, households that consume PV power by themselves instead of selling it will increase. With this backdrop, in this paper, we perform the confidence interval estimation of the next-day apparent residential load, i.e., the difference amount between the actual residential load and PV output, with Just-In-Time modeling. Some numerical examples, which demonstrate the validity of the proposed method, are presented.

Reporter genes have contributed to advancements in molecular biology. Binding of an upstream regulatory protein to a downstream reporter promoter allows quantification of the activity of the upstream protein produced from the corresponding gene. In studies of synthetic biology, analyses of reporter gene activities ensure control of the cell with synthetic genetic circuits, as achieved using a combination of in silico and in vivo experiments. However, unexpected effects of downstream reporter genes on upstream regulatory genes may interfere with in vivo observations. This phenomenon is termed as retroactivity. Using in silico and in vivo experiments, we found that a different copy number of regulatory protein-binding sites in a downstream gene altered the upstream dynamics, suggesting retroactivity of reporters in this synthetic genetic oscillator. Furthermore, by separating the two sources of retroactivity (titration of the component and competition for degradation), we showed that, in the dual-feedback oscillator, the level of the fluorescent protein reporter competing for degradation with the circuits’ components is important for the stability of the oscillations. Altogether, our results indicate that the selection of reporter promoters using a combination of in silico and in vivo experiments is essential for the advanced design of genetic circuits.

This paper proposes a novel accuracy improvement technique for the double-layer charge (DLC) formulation in shielding problems. Although the DLC formulation has advantages over the direct methods from the viewpoint of computational cost, the calculation accuracy can be worse because of cancellation errors. In order to improve the accuracy, we apply a new technique based on the difference field concept. Numerical results, which verify the effectiveness of the proposed method, are presented.

SUMMARY In a distribution line, power system control and power equipment investment are planned based on a measured power system current. However, recently the mass introduction of photovoltaic (PV) make it difficult for us to precisely measure the demand curve that is a current consumed by electrical equipment because the reversal power flow from PV systems is superposed. Therefore, the prediction of demand curves of distribution line is indispensable for power system management. In addition, it is also necessary to estimate the reliability of the predicted values as well as predicted current itself. In this paper, we propose the estimation method of the prediction interval that is the index of reliability based on the past demand curve database. The feature of the proposed method based on Just-In-Time (JIT) modeling make it possible for us to accurately estimate the prediction interval by the normalized database of demand curve. In this paper, some numerical examples are presented, which demonstrate the effectiveness of the proposed method.

The axle counter as a train detector is one of the important railway signaling devices for accurate and safe train control systems. This paper deals with a design optimization of the axle counter by large-scale magnetic field computations. To efficiently reduce the computational burden in the optimization, we accurately estimate the signaling level by applying infinite edge elements to an extensive air domain, which results in a practical design approach within an acceptable CPU-time.

The infinite edge element method (IEEM) has been proposed as an effective method for efficiently handling open boundary region, i.e., the air region. In this paper, we present a newly defined infinite element radiated from a reference plane (RPL), and propose the combination method using various infinite element configurations, such as radiated configurations from a reference point, a reference line, and an RPL. By using the proposed hybrid method, we can drastically decrease the number of ill-shaped infinite elements and improve the computational accuracy and convergence characteristics in comparison with the conventional IEEM, especially in the case of flattened analysis models.

The nonlinear magnetic properties of an iron core should be considered when topology optimization (TO) is applied to a realistic problem. However, a scheme for introduction of the magnetic nonlinearity to material-density-based TO has not been rigorously established. In this paper, TO by using material density with magnetic nonlinearity is proposed. The numerical instability of the sequential linear programming method is a well-known topic. The stabilized sequential linear programming method, which realizes the stabilized convergence characteristics of an objective function, is proposed and investigated in the 3-D design problem of a magnetic circuit.

SUMMARY Electromagnetic phenomena intrinsically spread over an infinite region. Thus, efficient handling of open boundaries is one of the main issues in electromagnetic field computations. This paper deals with the orthogonalized infinite edge element method that efficiently performs precise analysis of the infinite region. In this method, several parameters are used to achieve high accuracy. As one of the parameters, we focus on the reference point and investigate the effect of its position setting on accuracy. We also evaluate the accuracy of the calculated magnetic field in the far region. By applying boundary element method as postprocessing, it is found that high computational accuracy in the region can be effectively achieved.

A low-voltage regulator (LVR) is investigated as a countermeasure for voltage violation caused by the integration of photovoltaic (PV) systems in distribution systems. To achieve appropriate voltage control by the LVR, the appropriate control parameters need to be determined. We have proposed schemes to determine the appropriate control parameters using PV forecast profiles. In a previous work, the control parameters were determined on the basis of one-scenario PV forecast, which derives one forecast value in each time step. However, voltage violation could not be avoided by this method in a time step with a large forecast error. In this paper, we propose a method for determining the voltage control parameters of the LVR based on the forecast interval of the PV outputs to quantitatively consider the PV forecast error. The numerical simulation results show that the proposed method can reduce the amount of voltage deviation compared with the conventional method.

Topology optimization is one of the mathematical methods for finding the optimal structure of electrical machinery. Because topology optimization can make a design with much higher degree of freedom in terms of structure than other optimization methods, there is a possibility for achieving a completely new shape that is not limited to the conventional model. The optimized structure is strongly dependent on the characteristic function which is an explicit function with respect to the design variable. To identify the practical topology in which the gray scale elements do not exist as much as possible, Heaviside function is suitable to suppress the generation of gray scale. In this paper, Heaviside function is applied to the IH equipment problem, and both influences of the difference between single and multiple design domain and the difference of the constraint value of the magnetic body volume on optimal solution are particularly investigated.

SUMMARY In the past, we have proposed battery operations in a photovoltaic (PV) system using PV output and load power forecasts. In the proposed method, we utilized multiobjective optimization for a residential area model that includes a large number of PV systems with storage batteries. To practically demonstrate that the proposed operation effectively improves economic and environmental efficiency, it is indispensable to evaluate the effects of forecast error on the operation design of a PV system with storage battery. In this paper, we propose a method for evaluating the robustness of battery operation taking account of the statistical characteristics of forecast errors. Some numerical examples, which show the validity of the proposed method, are also presented.