Tomonobu Senjyu

Bio: Tomonobu Senjyu is an academic researcher from University of the Ryukyus. The author has contributed to research in topics: Electric power system & Wind power. The author has an hindex of 60, co-authored 854 publications receiving 16380 citations. Previous affiliations of Tomonobu Senjyu include Ryukoku University & University of Melbourne.

Neural-network-based maximum-power-point tracking of coupled-inductor interleaved-boost-converter-supplied PV system using fuzzy controller

Abstract: The photovoltaic (PV) generator exhibits a nonlinear V-I characteristic and its maximum power (MP) point varies with solar insolation. In this paper, a feedforward MP-point tracking scheme is developed for the coupled-inductor interleaved-boost-converter-fed PV system using a fuzzy controller. The proposed converter has lower switch current stress and improved efficiency over the noncoupled converter system. For a given solar insolation, the tracking algorithm changes the duty ratio of the converter such that the solar cell array voltage equals the voltage corresponding to the MP point. This is done by the feedforward loop, which generates an error signal by comparing the instantaneous array voltage and reference voltage corresponding to the MP point. Depending on the error and change of error signals, the fuzzy controller generates a control signal for the pulsewidth-modulation generator which in turn adjusts the duty ratio of the converter. The reference voltage corresponding to the MP point for the feedforward loop is obtained by an offline trained neural network. Experimental data are used for offline training of the neural network, which employs a backpropagation algorithm. The proposed peak power tracking effectiveness is demonstrated through simulation and experimental results. Tracking performance of the proposed controller is also compared with the conventional proportional-plus-integral-controller-based system. These studies reveal that the fuzzy controller results in better tracking performance.

Output power leveling of wind turbine Generator for all operating regions by pitch angle control

Abstract: Wind energy is not constant and windmill output is proportional to the cube of wind speed, which causes the generated power of wind turbine generators (WTGs) to fluctuate. In order to reduce fluctuation, different methods are available to control the pitch angle of blades of windmill. In a previous work, we proposed the pitch angle control using minimum variance control, and output power leveling was achieved. However, it is a controlled output power for only rated wind speed region. This paper presents a control strategy based on average wind speed and standard deviation of wind speed and pitch angle control using a generalized predictive control in all operating regions for a WTG. The simulation results by using actual detailed model for wind power system show the effectiveness of the proposed method.

Optimal Distribution Voltage Control and Coordination With Distributed Generation

Abstract: In recent years, distributed generation, as clean natural energy generation and cogeneration system of high thermal efficiency, has increased due to the problems of global warming and exhaustion of fossil fuels. Many of the distributed generations are set up in the vicinity of the customer, with the advantage that this decreases transmission losses. However, output power generated from natural energy, such as wind power, photovoltaics, etc., which is distributed generation, is influenced by meteorological conditions. Therefore, when the distributed generation increases by conventional control techniques, it is expected that the voltage change of each node becomes a problem. Proposed in this paper is the optimal control of distribution voltage with coordination of distributed installations, such as the load ratio control transformer, step voltage regulator (SVR), shunt capacitor, shunt reactor, and static var compensator. In this research, SVR is assumed to be a model with tap changing where the signal is received from a central control unit. Moreover, the communication infrastructure in the supply of a distribution system is assumed to be widespread. The genetic algorithm is used to determine the operation of this control. In order to confirm the validity of the proposed method, simulations are carried out for a distribution network model with distributed generation (photovoltaic generation).

A fast technique for unit commitment problem by extended priority list

Abstract: This paper introduces a new unit commitment problem, adapting extended priority list (EPL) method. The EPL method consists of two steps, in the first step we get rapidly some initial unit commitment problem schedules by priority list (PL) method. At this step, operational constraints are disregarded. In the second step unit schedule is modified using the problem specific heuristics to fulfill operational constraints. To calculate efficiently, however, note that some heuristics applied only to solutions can expect improvement. Several numerical examples demonstrate the effectiveness of proposed method.

A hybrid power system using alternative energy facilities in isolated island

Abstract: A hybrid power system uses many wind turbine generators in isolated small islands. The output power of wind turbine generators is mostly fluctuating and has an effect on system frequency. In order to solve this problem, we propose a new power system using renewable energy in small, isolated islands. The system can supply high-quality power using an aqua electrolyzer, fuel cell, renewable energy, and diesel generator. The generated hydrogen by an aqua electrolyzer is used as fuel for a fuel cell. The simulation results are given to demonstrate the availability of the proposed system in this paper.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques

Abstract: The many different techniques for maximum power point tracking of photovoltaic (PV) arrays are discussed. The techniques are taken from the literature dating back to the earliest methods. It is shown that at least 19 distinct methods have been introduced in the literature, with many variations on implementation. This paper should serve as a convenient reference for future work in PV power generation.

Optimization of perturb and observe maximum power point tracking method

Abstract: Maximum power point tracking (MPPT) techniques are used in photovoltaic (PV) systems to maximize the PV array output power by tracking continuously the maximum power point (MPP) which depends on panels temperature and on irradiance conditions. The issue of MPPT has been addressed in different ways in the literature but, especially for low-cost implementations, the perturb and observe (PO moreover, it is well known that the P&O algorithm can be confused during those time intervals characterized by rapidly changing atmospheric conditions. In this paper it is shown that, in order to limit the negative effects associated to the above drawbacks, the P&O MPPT parameters must be customized to the dynamic behavior of the specific converter adopted. A theoretical analysis allowing the optimal choice of such parameters is also carried out. Results of experimental measurements are in agreement with the predictions of theoretical analysis.