Tomonobu Senjyu

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.

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

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

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 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.

A Coordinated Control Method to Smooth Wind Power Fluctuations of a PMSG-Based WECS

This paper presents an output power smoothing method by a simple coordinated control of DC-link voltage and pitch angle of a wind energy conversion system (WECS) with a permanent magnet synchronous generator (PMSG). The WECS adopts an AC-DC-AC converter system with voltage-source converters (VSC). The DC-link voltage command is determined according to output power fluctuations of the PMSG. The output power fluctuationsin low- and high-frequency domains are smoothed by the pitch angle control of the WECS, and the DC-link voltage control, respectively. By using the proposed method, the wind turbine blade stress is mitigated as the pitch action in high-frequency domain is reduced. In addition, the DC-link capacitor size is reduced without the charge/discharge action in low-frequency domain. A chopper circuit is used in the DC-link circuit for stable operation of the WECS under-line fault. Effectiveness of the proposed method is verified by the numerical simulations.

Output levelling of renewable energy by electric double-layer capacitor applied for energy storage system

Utilization of renewable energy are coming up from view points of environmental conservation and depletion of fossil fuel. However, the generated power from renewable energies is always fluctuating due to environmental status. Energy storage system is indispensable to compensate these fluctuating components. Energy capacitor system (ECaSS) connected an electric double-layer capacitor (EDLC) with power-electronics devices is useful for the compensation of fluctuating power since one is capable of controlling both active and reactive power simultaneously. This paper proposes the current-source ECaSS (CS-ECS), which consists of EDLC, bi-directional DC-DC converter, and current-source inverter. We have presented the control system for the active/reactive power control of CS-ECS, and have shown the effectiveness of CS-ECS through computer simulations for case of wind power generation system.

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 Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method

Price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk, and it also plays a key role in the economic optimization of the electric energy industry. This paper explores a technique of artificial neural network (ANN) model based on similar days (SD) method in order to forecast day-ahead electricity price in the PJM market. To demonstrate the superiority of the proposed model, publicly available data acquired from the PJM Interconnection were used for training and testing the ANN. The factors impacting the electricity price forecasting, including time factors, load factors, and historical price factors, are discussed. Comparison of forecasting performance of the proposed ANN model with that of forecasts obtained from similar days method is presented. Daily and weekly mean absolute percentage error (MAPE) of reasonably small value and forecast mean square error (FMSE) of less than 7

A Frequency-Control Approach by Photovoltaic Generator in a PV–Diesel Hybrid Power System

/MWh were obtained for the PJM data, which has correlation coefficient of determination of 0.6744 between load and electricity price. Simulation results show that the proposed ANN model based on similar days method is capable of forecasting locational marginal price (LMP) in the PJM market efficiently and accurately.