小岩 健太

Crowbar is a popular device used to facilitate the fault ride-through of doubly fed induction generator (DFIG), which protects the power converter from the damage of overcurrent/overvoltage. The crowbar is activated once an overcurrent/overvoltage is detected, and it is desirable to release the crowbar as soon as possible in order to output active/reactive powers to the grid. But releasing the crowbar at an inappropriate timing may result in the recurrence of overcurrent. So, it is important to release the crowbar at an adequate timing. To this end, we analytically investigate the behavior of rotor current, and reveal that the current amplitude fluctuates periodically with a decaying magnitude. Hence, there is a set of release timings which may prevent overcurrent/overvoltage. Further, it is shown that these release timings are robust to the variations of voltage dip depth, generator parameters as well as wind speed. This analysis not only provides a justification for existing release algorithms, but also forms a solid basis for further improvement of crowbar operation. Finally, an algorithm is proposed for the crowbar operation which can prevent overcurrent/overvoltage while minimizing the adverse impact on the grid. The analysis is validated and compared via detailed simulations.

Passive power filters are indispensable for grid-connected inverters (GCIs) to eliminate current harmonics. However, typical filters require large inductance, which not only increases the filter dimension and costs but also leads to slower current response and a high voltage drop across the filter. This article proposes a novel filter called LCTCL filter, which effectively attenuates harmonics with small inductance. In addition, this article presents a controller synthesis to the GCI with LCTCL filter without an additional resistor. We demonstrate the effectiveness of the proposed LCTCL filter through comparative analysis with typical filters via simulations and experiments. We clarify that the proposed LCTCL filter has excellent features in terms of filter dimension, frequency characteristics, robustness for parameter variations, harmonic suppression, and total harmonic distortion.

This paper proposes a novel optimal control method for integrated systems of Wind farms (WFs) and hydrogen production systems (HPSs). Green hydrogen production via renewable power generation (RPG), such as wind power generation, is a promising technology to overcome environmental problems. RPG has the potential to become more widespread if we can produce hydrogen in an HPS using the output fluctuation and the output surplus of RPG, which cause power outages due to supply-demand imbalances. The proposed optimal control maximizes the capacity factor of HPS while producing hydrogen constantly and satisfying the technical requirement related to the output fluctuation of the WF. The proposed control is also easy to implement and needs no WF output forecast. We demonstrate the effectiveness of the proposed optimal control through simulated comparative analysis with conventional methods.

This paper proposes a novel coordinated control method of wind farms (WFs) and hydrogen production systems (HPSs). In a grid-connected system where the WF and the HPS are connected to the grid, the WF can supply the power to the grid, producing hydrogen in the HPS. Moreover, the output fluctuation of the WF can be mitigated when the HPS produces hydrogen from the output surplus. The purpose of the grid-connected system is to smooth the WF output fluctuation sufficiently, produce hydrogen constantly, and maintain a high capacity factor in the HPS. The proposed coordinated control achieves the mitigation of the WF output and the high capacity factor in the HPS. The key ideas are 1) utilizing the kinetic energy of wind generators and 2) virtual discharge of the HPS. The fluctuation components of the WF output are compensated by both the WF and the HPS. The proposed coordinated controller enables us to produce hydrogen constantly in the HPS with a low-rated power. The advantage of the proposed coordinated control is verified by a comparative analysis with conventional methods through simulations using real wind data.

In robust control problems, how to elicit and utilize the uncertainty information is the key in achieving a good robust performance. For passive uncertainties, the phase bound plays a fundamental role. For nonpassive uncertainties, the gain bound is used up to date. The former leads to the passivity approach and the later to the small-gain approach. A recently developed bounded positive real model succeeded in extracting both the phase and the gain information from passive uncertainties. However, it is still not known how to model the phase bound of a nonpassive uncertainty. Aiming at achieving an even higher performance for nonpassive uncertain systems, this article proposes a new model together with a modeling procedure for a class of nonpassive uncertainties. This model well captures both the gain and the phase features of the nonpassive uncertainty. Further, a numerically tractable design method is developed by extending the passivity method. The advantage of the proposed method is demonstrated through a real-world case study.

These days, networked control systems (NCSs) in which data is transmitted via communication have been actively studied for many potential applications. In an NCS, data dropout degrades control performance depending on network conditions. For an NCS with data dropout, the authors propose a model-predictive-control-based input optimisation, representing data dropout as both a Bernoulli model and a finite-order Markov chain. Using the proposed NCS data dropout model, the authors derive an optimal input that provides the estimated error between the expected state of the plant and a given reference. The proposed control problem is formulated as its equivalent quadratic programming, as executed at each online sampling. The authors also demonstrate simulations and experiments to show the effectiveness of the proposed method.

Networked control systems have received increasing attention from many researchers because of their vast potential. The insertion of communication networks in a controlled system brings network-induced defects, which are mainly caused by limited network resources. This paper proposes a codesign of periodic communication scheduling and a controller using sparsity for efficient use of the network while improving initial control performance. The effectiveness of the proposed method is verified through two numerical simulations.

This paper proposes a novel and easily implementable over-current suppression control for voltage-controlled virtual synchronous generators (VSGs). The VSG is vulnerable to grid voltage disturbances such as voltage dip/recovery because it consists of voltage source inverters (VSIs). In the worst case, the over-current caused by the voltage disturbance damages the VSI. The proposed VSG control can suppress the over-current caused by not only symmetrical but also asymmetrical voltage dip/recovery. The key idea is to use novel virtual grid voltages for the VSG reference output voltage. The proposed VSG control requires no complicated analysis/tuning for the implementation and no additional equipment such as a fault current limiter. Because of these advantages, the over-current in the VSG can be suppressed without increasing the cost and the tuning effort. The effectiveness of the proposed method is verified through simulations and experiments.

In recent years, formation control has received a great deal of attention as one of the most interesting issues in multiple mobile robots systems. In variable formation control systems, multiple mobile vehicles can form an appropriate formation from a list of possible formations, such that all mobile vehicles can pass even when the width of the course is narrow, and simultaneously the vehicles maintain a set distance between the course and each vehicle. This paper presents a predictive-based automatic formation control. In our formation control system, a unique formation is determined from feasible formations at any given sampling point. This control is formulated by its corresponding mixed-integer quadratic programing by introducing binary variables that are used to specify the formation. The effectiveness of the proposed methods is verified by applying two-wheeled mobile vehicles through simulations and experiments.

The recent development of the communication technology accelerates studies of real-time networked control systems using networks. The data dropout is essentially unavoidable, especially in wireless networks and it results from transmission errors and network traffic congestion. Multiple time-varying network traffic status given by discrete-time homogeneous Markov chains is assumed. The authors estimate the network traffic status characterised by the probability matrix of the Markov chain online from the data dropout history. According to the estimation of network traffic status, an appropriate controller is selected to improve the control performance. The effectiveness of the proposed method is verified through simulations and experiments.

Filters are inevitable for grid-connected inverters to attenuate the current harmonics caused by the pulse width modulation which is usually used in power conversion systems. High-order filters have attracted much attention because they attenuate the current harmonics effectively. Nevertheless, the high-order filters have some resonances which cause instability of the system. In addition, the resonance frequencies shift to high as the inductors and capacitors are smaller. It implies that the resonance frequencies may be beyond the Nyquist frequency in downsizing the filter. This complicates the stability and performance analyses of the system. This paper investigates rigorous input-output stability and analyses a robust performance based on the sampled-data control theory regardless of whether the resonance frequencies are beyond the Nyquist frequency or not. Our analysis contributes to downsizing the filter synthesis while the stability and the robustness are guaranteed even if the resonance frequencies are beyond the Nyquist frequency. The effectiveness of the proposed method is verified through simulations and experiments.

Position sensorless vector control of interior permanent magnet synchronous motors (IPMSMs) has been used for downsizing, cost reduction, and high reliability. In the position sensorless vector control, the q-axis inductance decreases as the q-axis current increases due to the load variation, which results in deterioration of the position estimation performance without considering the q-axis inductance. Here, the authors propose an innovative adaptive estimation based on the dynamic certainty equivalence principle for high-performance position sensorless control for IPMSMs. The effectiveness of the proposed adaptive scheme is verified through experiments.

This paper proposes a novel optimization method for energy storage systems (ESSs) to smooth wind farm output to satisfy the technical requirements and reduce the rated power (rated energy capacity) and charge/discharge loss of the ESS. The state of charge of the ESS operated by the proposed method can be regulated, guaranteeing some constraints. The proposed method has a very simple structure, does not require the wind farm output forecast and numerical optimization, such as particle swarm optimization. Therefore, a high-grade functional computation device is not needed. The effectiveness of the proposed method is verified by comparative analysis with conventional approaches through simulations.

<p>In recent years, it is required to extend the operation regions of induction motors (IMs) even at low-speed in regenerating while maintaining a high control performance. Conventional rotor speed estimation methods using an adaptive ux observer are unstable at low-speed in regenerating, particularly near the zero frequency. In this paper, we propose a simultaneous estimation method of the rotor speed and the stator resistance to improve the control performance of the speed-sensorless vector control of IMs. In addition, we prove that the proposed estimation method is stable even at low-speed in regenerating. Furthermore, the effectiveness of the proposed method is demonstrated experimentally.</p>

In position sensorless positioning servo systems, the parameter mismatch between interior permanent magnet synchronous motors (IPMSMs) and a position controller and/or a position estimator due to thermal variation and aged deterioration has not been sufficiently investigated. This paper proposes a convolutional integration-based second-order differential calculation to identify the parameters of IPMSMs for a high-performance positioning servo system in an adaptive scheme. In addition, we propose a high-frequency voltage injection strategy considering the trade-off between acoustic noise suppression and estimation performance. The effectiveness of the proposed second-order differential calculation calculation method, and the high-frequency voltage injection for the acoustic noise suppression is verified experimentally.

In the robust performance design, the celebrated mu-synthesis provides a reputed tool for systems with norm-bounded uncertainty. However, such small-gain methods inevitably limit the achievable robust performance when the dynamic uncertainty is not characterized by its gain bound. To achieve a high robust performance for systems with positive real (PR) uncertainty, an output strict passivity (OSP) approach is proposed recently which makes use of the positive realness and the maximum gain information of the uncertainty. Aiming at an even higher robust performance, this article proposes a novel model for PR uncertainties which captures the frequency-dependent gain bound, and establishes the corresponding design theory to actively utilize both gain and phase bounds of the uncertainty in the robust performance design. A case study reveals that the new uncertainty model alone yields a substantial improvement over the OSP method in performance even under the same design setup. Moreover, the best tuned controller outperforms the OSP controller.

This article addresses a controller for a full converter of variable-speed wind generators composed of permanent magnet synchronous generators. Conventionally, several proportional-integral (PI) controllers and a phase-locked loop (PLL) are used in the full converter for voltage-oriented control. The use of cascaded control loops with PI controllers degrades the control performance due to the windup of the integrators and complicates the tuning task. In this article, we propose a new full converter control scheme based on predictive control and a single P-controller in the alpha beta stationary frame to replace the integrators and the PLL. The proposed controller can reduce the parameter tuning effort, is free from integrator windup, and is robust to symmetrical and asymmetrical voltage dips. The effectiveness of the proposed method is verified through simulations.

Wind power generation provides an attractive method for tackling global environmental issues. However, the power grid cannot accommodate large amount of wind farms (WFs) because the fluctuation of WF output degrades the power quality (frequency and voltage) in the power grid. Technical requirements that are related to WF power fluctuation are issued in many countries in order to introduce the WF without degrading power quality. Therefore, it is essential to smooth the WF output in order to satisfy the technical requirements. This paper proposes an operation methodology for a system that is composed of energy storage systems (ESSs) and WF by kinetic energy (KE) control. Moreover, an optimal KE control is presented. The economical aspect and the advantage of the proposed system are verified through scenario simulations.

Networked control systems (NCSs) are control systems closed through communication network and applied to remote control of robots, tele-surgery and numerous industrial applications. In NCS, data have to coarsely quantised when the communication network is restricted due to the traffic limit. Under the traffic limitation, the design of an appropriate quantiser is highly important in realising a desired control performance. Further, the coordination of quantisation and control input also plays a decisive role. Towards an optimal balance between the traffic capacity of network and the control performance, this study proposes an MPC based approach for quantised state feedback control systems with dynamic quantisers. In the proposed approach, the control input and the quantisation interval of the quantiser are optimised online. The proposed method can explicitly treat input/state constraints, the parameter of quantiser and other physical restriction. The effectiveness of the proposed method is demonstrated by simulations and experiments.

This paper proposes a novel control approach to reduce the rated power of energy storage system (ESS) in the smoothing of wind power output. Wind power generation causes frequency fluctuations in power systems. Therefore, ESSs are used to absorb the fluctuating power of wind generators. Generally, the controller in an ESS is composed of a first-order low-pass filter (FLF) with a large time constant. However, the FLF incurs a high cost because its phase-lag induces an unnecessary increase in the rated power of ESS. This study addresses the reduction in the rated power of ESS without using a complex controller. First, the requirement of the controller is analyzed and its structure is proposed. Then, the absorption of the short-period wind power output is reduced by tuning a gain in the controller until the grid code is violated. The effectiveness of the proposed controller is verified by comparing it to the conventional controllers via scenario simulations using real wind data.

This study presents a model predictive-based current control for permanent magnet synchronous motors (PMSMs). In the authors' proposed approach, a reference voltage to an inverter can be strictly treated as a discrete voltage vector so that the output saturation of the inverter is avoided. Various constraints can be also taken into consideration in their approach. Designing the band of frequency is incorporated into a single objective function. The effectiveness of the proposed method is verified through simulations and experiments.

This paper proposes a novel model for the analysis and design of a virtual synchronous generator (VSGs). To enhance the stability in power systems with distributed generators, the concept of VSG has been proposed. The VSG is designed based on the swing equation of a synchronous generator ignoring the dynamics of current flowing through an interconnection reactor. However, responses obtained with actual system are different from those calculated from the model. It implies that the VSG cannot be correctly analyzed and designed by using the conventional model. This paper constructs an accurate VSG model considering the dynamics of current. Moreover, we address the stability analysis of the VSG and reveal a relation between the parameters of VSG controller and the rated power/energy capacity of energy storage systems equipped with the VSG based on the proposed model. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Permanent-magnet synchronous motors have attracted much attention due to their high efficiency and high-torque density. For higher control performance, finite control set model predictive control (FCS-MPC) and continuous control set MPC (CCS-MPC) have been developed. However, the former requires high computing power, whereas inverter voltage saturation is not considered in the latter. Therefore, this study proposes an optimal current control law taking into consideration the inverter output voltage. The effectiveness of the proposed method is verified by comparison with the FCS-MPC and the standard CCS-MPC through experiments.

Dense deployment of small cell base stations (SBSs) powered by renewable energy is spotlighted as a solution of the increasing demand of communication services. How to utilize renewable energy efficiently under the temporal change of traffic loads is a brand new challenge. In this paper, we design real-time controllers for cell zooming, that is, to determine transmission power and activation/deactivation of energy harvesting SBSs. The proposed method maximizes the total number of accommodated users, considering the remaining energies of batteries. First, we solve a problem of finding a number of active SBSs that maximizes energy efficiency with full accommodation of active users. Then, we apply model predictive control to the real-time computation of an optimal transmission power. Moreover, we present an explicit formula of an approximate optimal transmission power, by simplifying the proposed method. Numerical simulations illustrate that the proposed method achieves high performance on energy efficiency with low computational effort, compared with a Q-learning-based approach.

This paper proposes a new filter design method that can smooth the wind power generation output while keeping the energy capacity and power rating of the energy storage system (ESS) small. Wind power generation causes frequency fluctuations in power systems. Therefore, in many cases, the ESS is needed for smoothing the fluctuations of wind generator output. In order to improve the performance of the ESS controlled with the conventional low-pass filter (LPF), this paper investigates other types of filters. First, the relations between filter and energy capacity/power rating of the ESS are disclosed through the worst-case study. Then, the frequency fluctuation analysis of the power system with respect to the wind power generation is carried out in order to realize a pin-point smoothing. Finally, a metaheuristic optimization method is proposed for the multiobjective design of the filter based on these relations so as to achieve an optimal tradeoff between the output smoothing and energy capacity/power rating of the ESS. It is shown via simulation analysis with real wind data that the proposed method can suppress the frequency fluctuation of the power system effectively with the ESS having smaller energy capacity and power rating.

Recently, wind power has been attracting much attention because of its positive effect on the global warming. However, wind power generation causes frequency variations in electric power system. Therefore, battery systems for smoothing fluctuations of wind generator output are needed. Though battery control systems with simple low-pass filter (LPF) have been reported so far widely, filters other than LPF have not been studied sufficiently. This paper proposes a new method for designing energy capacity and control system of battery system with a new type of filter, which is based on the frequency characteristics of power system.

Renewable energies having little effect on the global environment have been attracting much attention. Among them, wind power has been introduced widely. However, output power of wind power generation is unstable, and thus it can have bad influence on power system operation. Therefore, control of the introduction or battery system for smoothing the unstable output is required. In this paper, a new method of power system frequency control is proposed which is based on the output frequency band control of wind farm by using variable speed wind generators.