Victoria M. Catterson

Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges

This is the first part of a two-part paper that has arisen from the work of the IEEE Power Engineering Societys Multi-Agent Systems (MAS) Working Group. Part I of this paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part II of this paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented.

Multi-Agent Systems for Power Engineering Applications—Part II: Technologies, Standards, and Tools for Building Multi-agent Systems

This is the second part of a two-part paper that has arisen from the work of the IEEE Power Engineering Societys Multi-Agent Systems (MAS) Working Group. Part I of this paper examined the potential value of MAS technology to the power industry, described fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications, and presented a comprehensive review of the power engineering applications for which MAS are being investigated. It also defined the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part II of this paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented. Given the significant and growing interest in this field, it is imperative that the power engineering community considers the standards, tools, supporting technologies, and design methodologies available to those wishing to implement a MAS solution for a power engineering problem. This paper describes the various options available and makes recommendations on best practice. It also describes the problem of interoperability between different multi-agent systems and proposes how this may be tackled.

Issues in integrating existing multi-agent systems for power engineering applications

Multi-agent systems (MAS) have proven to be an effective platform for diagnostic and condition monitoring applications in the power industry. For example, a multi-agent system architecture, entitled condition monitoring multi-agent system (COMMAS) (McArthur et al., 2004), has been applied to the ultra high frequency (UHF) monitoring of partial discharge activity inside transformers. Additionally, a multi-agent system, entitled protection engineering diagnostic agents (PEDA) (Hossack et al., 2003), has demonstrated the use of MAS technology for automated and enhanced post-fault analysis of power systems disturbances based on SCADA and digital fault recorder (DFR) data. In this paper, the authors propose the integration of COMMAS and PEDA as a means of offering enhanced decision support to engineers tasked with managing transformer assets. By providing automatically interpreted data related to condition monitoring and power system disturbances, the proposed integrated system offer engineers a more comprehensive picture of the health of a given transformer. Defects and deterioration in performance can be correlated with the operating conditions it experiences. The integration of COMMAS and PEDA has highlighted the issues inherent to the inter-operation of existing multi-agent systems and, in particular, the issues surrounding the use of differing ontologies. The authors believe that these issues need to be addressed if there is to be widespread deployment of MAS technology within the power industry. This paper presents research undertaken to integrate the two MAS and to deal with ontology issues

Online Conditional Anomaly Detection in Multivariate Data for Transformer Monitoring

Retrofitting condition monitoring systems to aging plant can be problematic, since the particular signature of normal behavior will vary from unit to unit. This paper describes a technique for anomaly detection within the context of the conditions experienced by an in-service transformer, such as loading, seasonal weather, and network configuration. The aim is to model the aged but normal behavior for a given transformer, while reducing the potential for anomalies to be erroneously detected. The paper describes how this technique has been applied to two transmission transformers in the U.K. A case study of 12 months of data is given, with detailed analysis of anomalies detected during that time.

Learning Models of Plant Behavior for Anomaly Detection and Condition Monitoring

Providing engineers and asset managers with a tool which can diagnose faults within transformers can greatly assist decision making on such issues as maintenance, performance and safety. However, the onus has always been on personnel to accurately decide how serious a problem is and how urgently maintenance is required. In dealing with the large volumes of data involved, it is possible that faults may not be noticed until serious damage has occurred. This paper proposes the integration of a newly developed anomaly detection technique with an existing diagnosis system. By learning a hidden Markov model of healthy transformer behavior, unexpected operation, such as when a fault develops, can be flagged for attention. Faults can then be diagnosed using the existing system and maintenance scheduled as required, all at a much earlier stage than would previously have been possible.

Embedded Intelligence for Electrical Network Operation and Control

The paper states that integrating multiple types of intelligent, mulit-agent data analysis within a smart grid can pave the way for flexible, extensible, and robust solutions to power network management.

On-line Transformer Condition Monitoring through Diagnostics and Anomaly Detection

This paper describes the end-to-end components of an on-line system for diagnostics and anomaly detection. The system provides condition monitoring capabilities for two in- service transmission transformers in the UK. These transformers are nearing the end of their design life, and it is hoped that intensive monitoring will enable them to stay in service for longer. The paper discusses the requirements on a system for interpreting data from the sensors installed on site, as well as describing the operation of specific diagnostic and anomaly detection techniques employed. The system is deployed on a substation computer, collecting and interpreting site data on-line.

Integrating an Agent-Based Wireless Sensor Network within an Existing Multi-Agent Condition Monitoring System

The use of wireless sensor networks for condition monitoring is gaining ground across all sectors of industry, and while their use for power engineering applications has yet been limited, they represent a viable platform for next-generation substation condition monitoring systems. For engineers to fully benefit from this new approach to condition monitoring, new sensor data must be incorporated into a single integrated system. This paper proposes the integration of an agent-based wireless sensor network with an existing agent-based condition monitoring system. It demonstrates that multi-agent systems can be extended down to the sensor level while considering the reduced energy availability of low-power embedded devices. A novel agent-based approach to data translation is presented, which is demonstrated through two case studies: a lab-based temperature and vibration monitoring system, and a proposal to integrate a wireless sensor network to an existing technology demonstrator deployed in a substation in the UK.

Circuit breaker prognostics using SF 6 data

Control decisions within future energy networks may take account of the health and condition of network assets, pushing condition monitoring within the smart grid remit. In order to support maintenance decisions, this paper proposes a circuit breaker prognostic system, which ranks circuit breakers in order of maintenance priority. By monitoring the SF6 density within a breaker, the system not only predicts the number of days to a critical level, but also incorporates uncertainty by giving upper and lower bounds on the prediction. This prognostic model, which performs linear regression, will be described in this paper, along with case studies demonstrating ranking breakers based on maintenance priority and prognosis of a leaking breaker. Providing an asset manager with this type of information could allow improved management of his/her assets, potentially deferring maintenance to a time when an outage is already scheduled.

The role of intelligent systems in delivering the Smart Grid

The development of “smart” or “intelligent” energy networks has been proposed by both EPRIs IntelliGrid initiative and the European SmartGrids Technology Platform as a key step in meeting our future energy needs. A central challenge in delivering the energy networks of the future is the judicious selection and development of an appropriate set of technologies and techniques which will form “a toolbox of proven technical solutions”. This paper considers functionality required to deliver key parts of the Smart Grid vision of future energy networks. The role of intelligent systems in providing these networks with the requisite decision-making functionality is discussed. In addition to that functionality, the paper considers the role of intelligent systems, in particular multi-agent systems, in providing flexible and extensible architectures for deploying intelligence within the Smart Grid. Beyond exploiting intelligent systems as architectural elements of the Smart Grid, with the purpose of meeting a set of engineering requirements, the role of intelligent systems as a tool for understanding what those requirements are in the first instance, is also briefly discussed.