The prediction of the malfunction timing of wind turbines is essential for maintaining the high profitability of the wind power generation industry. Studies have been conducted on machine learning methods that use condition monitoring system data, such as vibration data, and supervisory control and data acquisition (SCADA) data to detect and predict anomalies in wind turbines automatically. Autoencoder-based techniques that use unsupervised learning where the anomaly pattern is unknown have attracted significant interest in the area of anomaly detection and prediction. In particular, vibration data are considered useful because they include the changes that occur in the early stages of a malfunction. However, when autoencoder-based techniques are applied for prediction purposes, in the training process it is difficult to distinguish the difference between operating and non-operating condition data, which leads to the degradation of the prediction performance. In this letter, we propose a method in which both vibration data and SCADA data are utilized to improve the prediction performance, namely, a method that uses a power curve composed of active power and wind speed. We evaluated the method's performance using vibration and SCADA data obtained from an actual wind farm.

This paper discusses the collection of sensor data for power distribution systems. In current power distribution systems, this is usually performed solely by the Remote Terminal Unit (RTU) which is located at the root of a power distribution network. The recent rise of distributed power sources, such as photovoltaic generators, raises the demand to increase the frequency of data collection because the output of these distributed generators varies quickly depending on the weather. Increasing data collection frequency in turn requires shortening the time required for data collection. The paper proposes the use of aggregation points for this purpose. An aggregation point can collect sensor data concurrently with other aggregation points as well as with the RTU. The data collection time can be shortened by having the RTU receive data from aggregation points, instead of from all sensors. This approach then poses the problem of finding the optimal location of aggregation points. To solve this problem, the paper proposes a Mixed Integer Linear Problem (MILP) formulation of the problem. The MILP problem can then be solved with off-the-shelf mathematical optimization software. The results of experiments show that the proposed approach is applicable to rather large scale power distribution systems.

Critical infrastructures are falsely believed to be safe when they are isolated from the Internet. However, the recent appearance of Stuxnet demonstrated that isolated networks are no longer safe. We observe that a better intrusion detection scheme can be established based on the unique features of critical infrastructures. In this paper, we propose a whitelist-based detection system. Network and application-level whitelists are proposed, which are combined to form a novel cross-layer whitelist. Through experiments, we confirm that the proposed whitelists can exactly detect attack packets, which cannot be achieved by existing schemes.

The aim of this research is to study the power energy cost reduction of the mobile telecom industry through the supervisor control and data acquisition (SCADA) system application during globalization and liberalization competition. Yet this management system can be proposed functions: operating monitors, the analysis on load characteristics and dropping the cost of management.

The information technology revolution has transformed all aspects of our society including critical infrastructures and led a significant shift from their old and disparate business models based on proprietary and legacy environments to more open and consolidated ones. Supervisory Control and Data Acquisition (SCADA) systems have been widely used not only for industrial processes but also for some experimental facilities. Due to the nature of open environments, managing SCADA systems should meet various security requirements since system administrators need to deal with a large number of entities and functions involved in critical infrastructures. In this paper, we identify necessary access control requirements in SCADA systems and articulate access control policies for the simulated SCADA systems. We also attempt to analyze and realize those requirements and policies in the context of role-based access control that is suitable for simplifying administrative tasks in large scale enterprises.

Introducing multi-agent (MA) technology into a SCADA (Supervisory Control and Data Acquisition) system can improve the serviceability and enhance maintenance-free operation with the inter-terminal parameter (ITP) method. In addition, the system's distributed intelligent field terminals (IFTs) use a common algorithm that is unaffected by any changes to the system specifications. As a result of these innovations, the proposed system has much better serviceability because it is much easier to make modifications compared to that of conventional systems. This system has been implemented for practical purposes at over 60 sites.

In optical transport networks that use 2R optoelectronic wavelength converters, performance degradation is expected due to the accumulation of timing-jitter. We theoretically analyze the effects of timing-jitter and the cascadability of 2R optoelectronic wavelength converters based on a nonlinear signal model. We measured the cascadability in a 40-km re-circulation loop for 10 Gb/s signal.

A method is described for improving cooperation in supervisory control and data acquisition (SCADA) systems that uses multi-agent (MA) intelligent field terminals (IFTs). The MA function of each IFT evaluates the control conditions of the overall system and the conditions of the other IFTs. To shorten the turn-around time for data transfer among IFTs, the conflicts that occur when the data processed by different IFTs is inconsistent or irregular are cooperatively and autonomously resolved by predictive agents incorporated into each IFT. Experimental results showed that this method not only provides adequate control but also reduces the load on the network and the turn-around time when the number of IFTs is less than 30.