Katsumi Uezato

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

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.

A hybrid power system using alternative energy facilities in isolated island

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.

One-Hour-Ahead Load Forecasting Using Neural Networks

Load forecasting has always been an essential part of an efficient power systems planning and operation. Several electric power companies are now forecasting load power based on conventional methods. However, since the relationship between load power and factors influencing load power is nonlinear, it is difficult to identify its nonlinearity by using conventional methods. Most papers deal with 24-hour-ahead load forecasting or next-day peak load forecasting. These methods forecast the demand power by using forecasted temperature as forecast information. But, when the temperature curves change rapidly on the forecast day, load power changes greatly and forecast error increases. In conventional methods, neural networks use similar-day data to learn the trend of similarity. However, learning of all similar days data is very complex, and it does not suit learning of neural networks. Therefore, it is necessary to reduce the neural network structure and learning time. To overcome these problems, we propose a one-hour-ahead load forecasting method using the correction of similar-day data. In the proposed prediction method, the forecasted load power is obtained by adding a correction to the selected similar day data.

Voltage-based maximum power point tracking control of PV system

Photovoltaic (PV) generators exhibit nonlinear v-i characteristics and maximum power (MP) points that vary with solar insulation. An intermediate converter can therefore increase efficiency by matching the PV system to the load and by operating the solar cell arrays (SCAs) at their maximum power point. An MP point tracking algorithm is developed using only SCA voltage information thus leading to current sensorless tracking control. The inadequacy of a boost converter for array voltage based MP point control is experimentally verified and an improved converter system is proposed. The proposed converter system results in low ripple content, which improves the array performance and hence a lower value of capacitance is sufficient on the solar array side. Simplified mathematical expressions for a PV source are derived. A signal flow graph is employed for modeling the converter system. Current sensorless peak power tracking effectiveness is demonstrated through simulation results. Experimental results are presented to validate the proposed method.

A unit commitment problem by using genetic algorithm based on unit characteristic classification

This paper presents new approach for thermal unit commitment problem. Unit commitment (UC) problem plays a major role in power systems since the improvement of commitment schedules results in the reduction of operating cost. However, the unit commitment problem is one of the most difficult optimization problems in power system, because they have many constraints. Further, these constraints vary with each unit. To handle these constraints, some cording methods have been proposed. However, these methods require computation time. To overcome these problems, a new genetic operator based on unit characteristic classification and intelligent techniques generating initial populations are introduced. The proposed algorithm was tested on a reported UC problem. From simulation results, satisfactory solutions are obtained in comparison with previously reported results. Numerical results for system of up to 100 units are compared to previously reported results.

Feedforward maximum power point tracking of PV systems using fuzzy controller

A feedforward maximum power (MP) point tracking scheme is developed for the interleaved dual boost (IDB) converter fed photovoltaic (PV) system using fuzzy controller. The tracking algorithm changes the duty ratio of the converter such that the solar cell array (SCA) voltage equals the voltage corresponding to the MP point at that solar insolation. This is done by the feedforward loop, which generates an error signal by comparing the instantaneous array voltage and reference voltage. The reference voltage for the feedforward loop, corresponding to the MP point, is obtained by an off-line trained neural network. Experimental data is used for off-line training of the neural network, which employs back-propagation algorithm. The proposed fuzzy feedforward peak power tracking effectiveness is demonstrated through the simulation and experimental results, and compared with the conventional proportional plus integral (PI) controller based system. Finally, a comparative study of interleaved boost and conventional boost converter for the PV applications is given and their suitability is discussed.

An adaptive dead-time compensation strategy for voltage source inverter fed motor drives

This paper presents an adaptive dead-time compensation strategy to obtain fundamental phase voltage for inverter fed vector controlled permanent magnet synchronous motor drives. The amplitude of phase dead-time compensation voltage (DTCV) to compensate disturbance voltage due to undesirable characteristics of inverter, such as dead-time, turn-on/off time of switching devices, and on-voltages of switching devices and diodes is adaptively determined according to a dead-time compensation time (DTCT). DTCT is identified on-line with using a /spl delta/-axis disturbance voltage in the current reference frame that is synchronized with current vector. The /spl delta/-axis disturbance voltage is estimated by a disturbance observer. The accuracy of identified DTCT is experimentally confirmed by calculating the mean absolute percentage error (MAPE) between a calculated active power and a measured one. MAPE for adaptive DTCT is almost within 5% at any operating point.

Adaptive Dead-Time Compensation Strategy for Permanent Magnet Synchronous Motor Drive

This paper presents an adaptive dead-time compensation strategy to obtain fundamental phase voltage for inverter-fed vector-controlled permanent magnet synchronous motor drives. A phase dead-time compensation voltage (DTCV) to compensate the disturbance voltage due to undesirable characteristics of inverter, such as dead time, turn on/off time of switching devices, and on-voltages of switching devices and diode, is transformed into q-axis DTCV in the rotor reference frame. The relationship between q-axis DTCV and a dead-time compensation time (DTCT) is investigated. DTCT is identified online by using q-axis disturbance voltage, which is estimated by a disturbance observer. The amplitude of phase DTCV is adaptively determined according to the identified DTCT. The accuracy of identified DTCT is experimentally confirmed by calculating the mean absolute percentage error (MAPE) between calculated active power and measured one. MAPE for adaptive DTCT is within 5% at any operating point and is less than that for the fixed DTCT.

A hybrid power system with using alternative energy facilities in isolated island

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

Relationship of parallel model and series model for permanent magnet synchronous motors taking iron loss into account

This paper investigates the relationship of parallel model and series model for permanent magnet synchronous motor taking iron loss into account. The expressions of flux linkage, terminal voltage, and electromagnetic torque are compared. It follows from the investigation that the parallel and series models are mathematically the same. In addition, the properties of the models are exhibited. The parallel model is superior in understanding the physical meaning to the series model. The series model is superior in low order of the state variables to the parallel model.