論文

本稿では, 時変の通信遅延を有するテレオペレーションシステムに対して, 安定性が遅延の大きさや粘性摩擦に依存しない制御則を提案する. 提案する制御則は遅延の変化率に依存した時変ゲインを用いており，これにより, 安定条件が遅延の大きさに依存しないテレオペレーションを実現し,予期しない急激な遅延の増加に対しても安定性が保証される. 提案制御手法によってテレオペレーションシステム全体が安定となることをリアプノフの安定定理に基づく安定性解析により示す. 最後に, 2 台の平面2 自由度ダイレクトドライブロボットを用いた検証実験を行い, 提案法の有効性を検証する.

This paper presents a distributed optimal power supply-demand management method by using negawatt trading in a real-time electricity market. In the negawatt trading, power consumers reduce their own power demand and receive monetary reward from the market operator. This paper discusses a power adjustment method via negawatt trading by including charge or discharge power from energy storage systems. In order to derive appropriate electricity incentive prices for consumers and to use energy storage systems in power grids efficiently, we deal with a power adjustment problem by using some future power supply information from generators. We then show a novel market algorithm to solve this problem in a distributed manner through market trading. Finally, numerical simulation results illustrate the effectiveness of our proposed method.

This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity marketin an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity pricewhile considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supplybalance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method.

© 2018 The Society of Instrument and Control Enginners - SICE. In this paper, we propose an event-triggered formation control algorithm for a multi-agent system consists of linear discrete agents in order to reduce energy consumption due to unnecessary calculation and communication of agents. By using the event-triggered protocol, which updates the control input aperiodically only when certain triggering condition is satisfied, calculation and communication frequency are reduced. Also the multi-agent system is guaranteed an almost sure stability by determining the control input update timing by the triggering condition which is based on Lyapunov's stability theorem. Finally we verify the effectiveness of the proposed algorithm by numerical simulation.

© 2018 ASEAN University Network/Southeast Asia Engineering Education Development Network. All rights reserved. In order to utilize the flow of a river to generate power, it is crucial to develop a system that is able to obtain and record the river's characteristics in various aspects. A simple but effective method has to be created to allow researchers to collect and study the parameters of a river in order to predetermine the final build of the power generation model. A floating sensor that is able to collect and give a real-time data has been developed using readily available hardware as well as custom made parts. This paper describes a design methodology for river floating sensors and results from a case study performed in the actual controlled channel are presented to demonstrate the effectiveness of the design decisions.

© 2019 JSME. The purpose of this paper is to minimize the total electricity cost while considering comfort in the home. We propose home energy management (HEMS) method by model predictive control (MPC) based on EV state prediction. If charging and discharging can be planned based on electric vehicle (EV) usage prediction, it can minimize the charging with an external charging station because it can be charged in advance at home. We introduce a model considering the passage of time in order to predict the use of EV. Finally, we confirm the validity of this research through numerical simulations.

This paper discusses the detection of attacks on decentralized and intelligent power networks, so-called smart grids. The approach used here, estimates the states of the power network, which is modelled by its power flow, via an Extended Kalman Filter. The use of this filter allows the consideration of nonlinearities, thus enabling the modelling of the losses of the systems. The final detection of attacks on a generator in a power network is realized by a correlation analysis. The correlation analysis provides the result of the inner correlation of sequential signals. Based on this, an assessment can be made as to whether or not the network is affected by an attack.

In this paper, a distributed event-triggered formation control algorithm for a multi-agent system which consists of linear discrete-time agents is proposed based on control theory. For agents to achieve a formation, it is necessary to communicate with each other to feedback their information. For communication, wireless communications are desirable for various tasks since wired networks can constrain their movement. Also, each agent have to observe their states from sensors. Those energy consumption due to unnecessary calculation and communication of agents may shorten battery life of agents. By using the event-triggered protocol, which updates the control input aperiodically only when certain triggering condition is satisfied, we aim to reduce those calculation and communication by reducing the input update frequency. Also the multi-agent system is guaranteed to achieve a formation by determining the triggering condition of the control input based on Lyapunov’s stability theorem. At the end of the paper, the effectiveness of the proposed algorithm is verified by numerical simulation.

This paper deals with a decentralized control of smart grid by using overlapping information for load frequency of power networks introducing distributed power generations. The control objective is to minimize the cost function of load frequency control problem. We expand the state space of the system of power networks and propose a decentralized state feedback control of subsystems for the expanded system. Then we contract the decentralized feedback law to match the original system. Finally, we show the effectiveness of the load frequency control by using the proposed decentralized control method through the simulation result of decentralized large scale power network systems.

This paper discusses faults diagnosis method of multiple cyber attacks in networked electrical power systems. We deal with a power network of a centralized system, and then, some parameters are estimated by Kalman filter. Using a sensor network system, each of sensor nodes can exchange information by wireless communication. A fault diagnosis method is proposed by using both fault detection diagnosis matrix and fault distinction matrix. The former is composed of estimated values and observed values. On the other hand, the latter is composed of observed values and calculated values via the senor network. Finally, the effectiveness of the proposed approach is validated by a simulation experiment.

This paper deals with a distributed decision procedure for determining the electricity price for a future smart grid. In order to derive the optimal regional prices and improve the convergence speed of the algorithm without private information of market players, we propose a price decision algorithm with an alternating decision making of market players. In this paper, we show that the optimal regional prices derived by using our proposed algorithm maximize the social welfare of power networks which includes profits of power consumers and power suppliers with renewable energy generators. Also, we discuss the convergence property of the proposed price decision algorithm. Finally, the feasibility of our proposed dynamic pricing methodology is shown with numerical simulation results.

This paper discusses a distributed control for load frequency of electrical power networks based on iterative gradient methods. The control objective is to minimize the quadratic cost function of load frequency control problem, and we apply the distributed control methodology by using iterative gradient methods to complicated large scale electrical power networks. Iterative gradient methods have good flexibility and they are effective for complicated and time-varying distributed power networks. Several numerical simulation results of distributed power network systems including distributed generations, batteries, and renewable energies in order to show the effectiveness of the load frequency control compared with conventional decentralized control and centralized control.

This paper presents a distributed power supply demand management method based on negawatt trading including charge or discharge power from energy storage systems. In the negawatt trading, power consumers reduce their own power demand and receive monetary reward from the market operator. In order to derive appropriate electricity incentive prices for consumers and to use energy storage systems in power grids efficiently, we deal with a power adjustment problem using some future power supply information in this paper. We then show a novel market algorithm to solve this problem in a distributed manner through market trading among the market operator and consumers. Finally, we verify the effectiveness of our proposed method with numerical simulation results.

This paper addresses the problem of distributed secure state estimation of a discrete-time linear dynamical system in an adversarial environment. Much of the recent work on this problem has imposed redundancy requirements on measurement and communication networks for state estimation in the presence of attacks. In this paper, the authors aim to relax this redundancy requirement by using prior information about the state to be estimated. A scenario where sparsity information of the state is given is especially considered, and it is shown that the a priori sparsity information alleviates the redundancy requirement. An algorithm for distributed secure state estimation is then derived and a sufficient condition on the convergence of the algorithm is given. Numerical simulations finally illustrate the effectiveness of the algorithm.

This paper deals with a dynamic pricing via the H-infinity control considering uncertainties in market participants' behavior. The dynamic pricing is a decision procedure of an electricity price based on power demand and supply information from selfish power consumers and generators. However, since market participants have uncertainties in their behavior, the price decision procedure is required to have robustness against these uncertainties for stable operation of power systems. Then, in this paper, we propose a novel price decision procedure via the H-infinity control and also show the effectiveness of our proposed price decision method in simulation results.

This paper deals with a model of a real-time electricity market for a smart grid, and discusses how to decide wholesale and retail prices of electricity among market participants: power consumers, power suppliers and an independent system operator. In this paper, we consider a linearized AC model of a power grid with a direct current process and show that the wholesale price for suppliers is determined as the optimal Lagrange multiplier corresponding to the nodal power balance constraints. In addition, we propose a decision procedure for the retail pricing of electricity for consumers. In order to improve the convergence speed of the algorithm, we apply the iterative gradient method with line search to a retail pricing decision procedure, in which the optimal step size is determined in each iteration step. Simulation results, finally, show the effectiveness of the proposed retail pricing decision procedure.

The frequency synchronization problem of wide-area control from the higher level of networked power systems is considered. We investigate the exponential convergence and robustness of the passivity-based distributed averaging controller used in damping the inter-area oscillations of power networks and coping with the time-varying power demand. A strict composite storage function is constructed that allows us to quantify the exponential convergence rate of the closed-loop system. Furthermore, L2 gain performance is utilized as a tool to quantify the impact of time-varying power demand on control areas. We simulate the controller performance on a four-area network which is equivalent to the IEEE New England 39-Bus system.

In this study, we propose a decentralized control method that simultaneously achieves stable formation and obstacle avoidance in cooperative transport by multiple unmanned aerial vehicles. In the past, geometric methods, artificial potential methods, and path planning methods have been widely used for obstacle avoidance control in cooperative transport, but recently, a method based on the Control Barrier Function (CBF) has been proposed and is attracting attention as an approach to realize transport while guaranteeing safety. This research is novel in that $N$ agents determine the optimal control input in a decentralized manner. This enables us to simultaneously realize robustness, in which the remaining agent can complement the system even if one of the agents fails, and high system scalability, which is independent of the number of agents.