Naotaka Okada

Development of a 500-kW Modular Multilevel Cascade Converter for Battery Energy Storage Systems

Renewable energy sources such as wind turbine and photovoltaic power generators may make the power grid unstable due to their output fluctuations. Battery energy storage systems (BESSs) are being considered as a countermeasure for this issue. A modular multilevel cascade converter (MMCC) is expected as a power conversion circuit for BESSs because each bridge cell can control the state of charge of a battery unit, independent of that of another one, and the harmonic current generated is low enough to eliminate the ac filter, normally installed on conventional two-level converters, from the MMCC. This paper describes the development of a real-scale (500 kW) single-star-bridge-cell-based MMCC for BESSs and reports successful test results obtained from a downscaled grid model and a 6.6-kV real-scale distribution line.

Development of a 500-kW Modular Multilevel Cascade Convertor for battery energy storage systems

Natural energy sources such as wind turbines and photovoltaic power generators cause instability of the power grid due to their output fluctuations. Battery energy storage systems (BESSs) are being considered as a countermeasure for this issue. A Modular Multilevel Cascade Convertor (MMCC) is one suitable circuit topology for BESSs, because each cell can control each batterys state-of-charge independently, and the level of generated harmonics is low enough to eliminate the AC filter normally installed in conventional two level converters. This paper describes the development of a real-scale (500 kW) H-bridge MMCC for BESSs and reports successful test results of the control functions with a scale down grid model and a 6.6-kV real-scale distribution line.

Harmonic state estimation in distribution network based on fifth harmonic current characteristic

Reproductive simulation of the measurement data by the harmonic analyze is start point to understand harmonic voltage distortion mechanism, but the method to estimate the harmonics distribution among the many utility customers has not been established. In the last decade, harmonic analysis of Japanese middle voltage customer and low voltage customer has been done, the distribution of the fifth harmonic current can be classified middle voltage customers single phase load, low voltage customers three phase and single phase load named group A and middle voltage three phase load named group B, and the amplitude per load kW has been investigated from sum of each current vector. By the application of this knowledge, there is a possibility to estimate the harmonic distribution with many harmonic source in the distribution network. In this paper, we developed a harmonic state estimation method with limited measurement condition by using the fifth harmonic current phase angle classification model. Numerical experiments are conducted in seven node distribution network to test the proposed method. The results show that the proposed harmonic state estimation by using the fifth harmonic current phase angle classification model approach can reliably identify harmonic distribution in the change of load conditions, background harmonic voltage phase angle and series inductance of shunt capacitors.

A case study of bad data detection for distribution feeder voltage measurement

Voltage measurement data are obtained from sectionalizing switches with sensors that introduced for distribution automation. In future active distribution network, it will be very important to detect bad data that deteriorate measurement accuracy to utilize the data and for appropriate maintenance. State estimation using the measured data of distribution feeders has been studied. However, the bad data detection method has been mainly discussed for transmission networks. A known method for detecting bad data based on the weighted least square method is tested for voltage distribution estimation of feeder assuming that customer demand profile can be approximated by the normal distribution. Voltage distribution with smaller error can be obtained by discarding the bad data that are detected by the proposed method.