Standards-based sensor web for wide area monitoring of power systems

The balance of supply and demand of energy is the key factor in the stability of power systems. A small disturbance in the supply demand relationship, if not properly handled, can cascade into a major outage, costing millions of dollars to the stakeholders. Proper monitoring and exchange of critical information in real time is the only solution to prevent the instability in this vulnerable system. But, the disparity in the protocols used by power utilities and the lack of infrastructure for information exchange are proving to be hindrance to obtaining a reliable de-regularized power industry. In this thesis, an emerging Sensor Web Enablement (SWE) has been adapted for the wide area monitoring of power systems. SWE and CIM provide a solution to both problems; the heterogeneity of data and the lack of central repository of the data for proper action. The sensor data from utilities that are published in CIM were modeled thorough a SensorML and exposed via a Sensor Observation Service (SOS). This provides a standard method for discovering and accessing the sensor data between utilities and facilitates rapid response functionality to handle contingences

Practical applications of multi-agent systems in electric power systems

The transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur

A survey on the development status and application prospects of knowledge graph in smart grids

With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.Comment: IET Generation, Transmission & Distributio

Ontology acquisition and exchange of evolutionary product-brokering agents

Agent-based electronic commerce (e-commerce) has been booming with the development of the Internet and agent technologies. However, little effort has been devoted to exploring the learning and evolving capabilities of software agents. This paper addresses issues of evolving software agents in e-commerce applications. An agent structure with evolution features is proposed with a focus on internal hierarchical knowledge. We argue that knowledge base of agents should be the cornerstone for their evolution capabilities, and agents can enhance their knowledge bases by exchanging knowledge with other agents. In this paper, product ontology is chosen as an instance of knowledge base. We propose a new approach to facilitate ontology exchange among e-commerce agents. The ontology exchange model and its formalities are elaborated. Product-brokering agents have been designed and implemented, which accomplish the ontology exchange process from request to integration

Integration of Legacy Appliances into Home Energy Management Systems

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS

A New Cognitive-Based Massive Alarm Management System in Electrical Power Administration

This paper presents a methodology that integrates several available techniques to manage the massive amount of alarm signals in electrical power dispatch control centres, as well as the contribution of each entity involved in the system. Artificial intelligence techniques that can be used in this problem are reviewed here based on the available information. The final objective is to find the root cause of avalanches of alarms (failure tree) and to reduce their number through grouping or clustering techniques so that the EEMUA 191 standards are followed. Even though other contributions in this topic have been made before, the alarm management problem continues to be practically unsolved for many applications in industry. Here, the integration is developed using the ontology of each system domains, i.e., the ontology corresponding to the alarms, controls, events, energy flow and trigger sequence. Additionally, in this methodology, a rule based expert system is used to treat the alarms with a neural net based approach to treat the historical database of alarms and failures

Intelligent management experience on efficient electric power system

Electric power system is one of the most critical and strategic infrastructures of industrial societies. Nowadays, it is necessary the modernization and automation of the electric power grid to increase energy efficiency, reduce emissions, and transit to renewable energy. Power utilities face the challenge of using information and communication networks more effectively to manage the demand, generation, transmission, and distribution of their commodity services. Communication network constitutes the core of the electric system automation applications, the design of a cost-effective, and reliable network architecture is crucial. To resolve this difficulty in this work we study the integration of advanced artificial intelligence technology into existing network management system. This work focuses on an intelligent framework and a language for formalizing knowledge management descriptions and combining them with existing OSI management model. We have normalized the knowledge management base necessary to manage the current resources in the telecommunication networks. Intelligent agents learn the normal behaviour of each measurement variable and combine the intelligent knowledge for the management of the network resources. We present an analysis of corporate network management requirements and technologies, together with our implementation experience with the development of an integrated management system for a company network