Multi-agent systems for power engineering applications - part 1 : Concepts, approaches and technical challenges

This is the first part of a 2-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part 1 of the 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 2 of the 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

A Model-Based Holistic Power Management Framework: A Study on Shipboard Power Systems for Navy Applications

The recent development of Integrated Power Systems (IPS) for shipboard application has opened the horizon to introduce new technologies that address the increasing power demand along with the associated performance specifications. Similarly, the Shipboard Power System (SPS) features system components with multiple dynamic characteristics and require stringent regulations, leveraging a challenge for an efficient system level management. The shipboard power management needs to support the survivability, reliability, autonomy, and economy as the key features for design consideration. To address these multiple issues for an increasing system load and to embrace future technologies, an autonomic power management framework is required to maintain the system level objectives. To address the lack of the efficient management scheme, a generic model-based holistic power management framework is developed for naval SPS applications. The relationship between the system parameters are introduced in the form of models to be used by the model-based predictive controller for achieving the various power management goals. An intelligent diagnostic support system is developed to support the decision making capabilities of the main framework. Naïve Bayes’ theorem is used to classify the status of SPS to help dispatch the appropriate controls. A voltage control module is developed and implemented on a real-time test bed to verify the computation time. Variants of the limited look-ahead controls (LLC) are used throughout the dissertation to support the management framework design. Additionally, the ARIMA prediction is embedded in the approach to forecast the environmental variables in the system design. The developed generic framework binds the multiple functionalities in the form of overall system modules. Finally, the dissertation develops the distributed controller using the Interaction Balance Principle to solve the interconnected subsystem optimization problem. The LLC approach is used at the local level, and the conjugate gradient method coordinates all the lower level controllers to achieve the overall optimal solution. This novel approach provides better computing performance, more flexibility in design, and improved fault handling. The case-study demonstrates the applicability of the method and compares with the centralized approach. In addition, several measures to characterize the performance of the distributed controls approach are studied

Multi-agent control and operation of electric power distribution systems

This dissertation presents operation and control strategies for electric power distribution systems containing distributed generators. First, models of microturbines and fuel cells are developed. These dynamic models are incorporated in a power system analysis package. Second, operation of these generators in a distribution system is addressed and load following schemes are designed. The penetration of distributed generators (DGs) into the power distribution system stability becomes an issue and so the control of those DGs becomes necessary. A decentralized control structure based on conventional controllers is designed for distributed generators using a new developed optimization technique called Guided Particle Swarm Optimization. However, the limitations of the conventional controllers do not satisfy the stability requirement of a power distribution system that has a high DG penetration level, which imposes the necessity of developing a new control structure able to overcome the limitations imposed by the fixed structure conventional controllers and limit the penetration of DGs in the overall transient stability of the distribution system. Third, a novel multi-agent based control architecture is proposed for transient stability enhancement for distribution systems with microturbines. The proposed control architecture is hierarchical with one supervisory global control agent and a distributed number of local control agents in the lower layer. Specifically, a central control center supervises and optimizes the overall process, while each microturbine is equipped with its own local control agent.;The control of naval shipboard electric power system is another application of distributed control with multi-agent based structure. In this proposal, the focus is to introduce the concept of multi-agent based control architecture to improve the stability of the shipboard power system during faulty conditions. The effectiveness of the proposed methods is illustrated using a 37-bus IEEE benchmark system and an all-electric naval ship

Research and development of diagnostic algorithms to support fault accommodating control for emerging shipboard power system architectures

The U.S. Navy has proposed development of next generation warships utilising an increased amount of power electronics devices to improve flexibility and controllability. The high power density finite inertia network is envisioned to employ automated fault detection and diagnosis to aid timely remedial action. Integration of condition monitoring and fault diagnosis to form an intelligent power distribution system is anticipated to assist decision support for crew while enhancing security and mission availability. This broad research being in the conceptual stage has lack of benchmark systems to learn from. Thorough studies are required to successfully enable realising benefits offered by using increased power electronics and automation. Application of fundamental analysis techniques is necessary to meticulously understand dynamics of a novel system and familiarisation with associated risks and their effects. Additionally, it is vital to find ways of mitigating effects of identified risks. This thesis details the developing of a generalised methodology to help focus research into artificial intelligence (AI) based diagnostic techniques. Failure Mode and Effects Analysis (FMEA) is used in identifying critical parts of the architecture. Sneak Circuit Analysis (SCA) is modified to provide signals that differentiate faults at a component level of a dc-dc step down converter. These reliability analysis techniques combined with an appropriate AI-algorithm offer a potentially robust approach that can potentially be utilised for diagnosing faults within power electronic equipment anticipated to be used onboard the novel SPS. The proposed systematic methodology could be extended to other types of power electronic converters, as well as distinguishing subsystem level faults. The combination of FMEA, SCA with AI could also be used for providing enhanced decision support. This forms part of future research in this specific arena demonstrating the positives brought about by combining reliability analyses techniques with AI for next generation naval SPS.The U.S. Navy has proposed development of next generation warships utilising an increased amount of power electronics devices to improve flexibility and controllability. The high power density finite inertia network is envisioned to employ automated fault detection and diagnosis to aid timely remedial action. Integration of condition monitoring and fault diagnosis to form an intelligent power distribution system is anticipated to assist decision support for crew while enhancing security and mission availability. This broad research being in the conceptual stage has lack of benchmark systems to learn from. Thorough studies are required to successfully enable realising benefits offered by using increased power electronics and automation. Application of fundamental analysis techniques is necessary to meticulously understand dynamics of a novel system and familiarisation with associated risks and their effects. Additionally, it is vital to find ways of mitigating effects of identified risks. This thesis details the developing of a generalised methodology to help focus research into artificial intelligence (AI) based diagnostic techniques. Failure Mode and Effects Analysis (FMEA) is used in identifying critical parts of the architecture. Sneak Circuit Analysis (SCA) is modified to provide signals that differentiate faults at a component level of a dc-dc step down converter. These reliability analysis techniques combined with an appropriate AI-algorithm offer a potentially robust approach that can potentially be utilised for diagnosing faults within power electronic equipment anticipated to be used onboard the novel SPS. The proposed systematic methodology could be extended to other types of power electronic converters, as well as distinguishing subsystem level faults. The combination of FMEA, SCA with AI could also be used for providing enhanced decision support. This forms part of future research in this specific arena demonstrating the positives brought about by combining reliability analyses techniques with AI for next generation naval SPS

Fault Diagnosis System Based on Multiagent Technique for Ship Power System

Fault diagnosis system of ship power system can assist the crew to deal with faults, shorten the processing time, and prevent faults expanding. Multiagent technique is adopted for the fault diagnosis system. Ship power system is divided into several feeder units. Each one is abstracted as a regional feeder agent (FED-Agent). A multiagent fault diagnosis system is established with FED-Agent and other functional agents. Considering of the characteristics of agent, the multiagent system processes both autonomy and interactivity. It can solve fault diagnosis problem of ship power system effectively

Application of multi-agents to power distribution systems

The electric power system has become a very complicated network at present because of re-structuring and the penetration of distributed energy resources. In addition, due to increasing demand for power, issues such as transmission congestion have made the power system stressed. A single fault can lead to massive cascading effects, affecting the power supply and power quality. An overall solution for these issues can be obtained by a new artificial intelligent mechanism called the multi-agent system. A multi-agent system is a collection of agents, which senses the environmental changes and acts diligently on the environment in order to achieve its objectives. Due to the increasing speed and decreasing cost in communication and computation of complex matrices, multi-agent system promise to be a viable solution for today\u27s intrinsic network problems.;A multi-agent system model for fault detection and reconfiguration is presented in this thesis. These models are developed based on graph theory and mathematical programming. A mathematical model is developed to specify the objective function and the constraints.;The multi-agent models are simulated in Java Agent Development Framework and MatlabRTM and are applied to the power system model designed in the commercial software, Distributed Engineering Workstation(c) . The circuit that is used to model the power distribution system is the Circuit of the Future, developed by Southern California Edison.;The multi-agent system model can precisely detect the fault location and according to the type of fault, it reconfigures the system to supply as much load as possible by satisfying the power balance and line capacity constraints. The model is also capable of handling the assignment of load priorities.;All possible fault cases were tested and a few critical test scenarios are presented in this thesis. The results obtained were promising and were as expected

The role of circumstance monitoring on the diagnostic interpretation of condition monitoring data

Circumstance monitoring, a recently coined termed defines the collection of data reflecting the real network working environment of in-service equipment. This ideally complete data set should reflect the elements of the electrical, mechanical, thermal, chemical and environmental stress factors present on the network. This must be distinguished from condition monitoring, which is the collection of data reflecting the status of in-service equipment. This contribution investigates the significance of considering circumstance monitoring on diagnostic interpretation of condition monitoring data. Electrical treeing partial discharge activity from various harmonic polluted waveforms have been recorded and subjected to a series of machine learning techniques. The outcome provides a platform for improved interpretation of the harmonic influenced partial discharge patterns. The main conclusion of this exercise suggests that any diagnostic interpretation is dependent on the immunity of condition monitoring measurements to the stress factors influencing the operational conditions. This enables the asset manager to have an improved holistic view of an asset's health

SILS MRAT: A Multi-Agent Decision-Support System for Shipboard Integration of Logistics Systems

This report describes work performed by CDM Technologies Inc. on subcontract to ManTech Advanced Systems International, Inc. (Fairmont, West Virginia), and under sponsorship of the Office of Naval Research (ONR). The principal aim of the SILS (Shipboard Integration of Logistics Systems) project is to provide a decision-support capability for Navy ships that integrates shipboard logistical and tactical systems within a near real-time, automated, computer-based shipboard readiness and situation awareness facility. Specifically, SILS is intended to provide the captain of a ship and his staff with an accurate evaluation of the current condition of the ship, based on the ability of all of its equipment, services and personnel to perform their intended functions. The SILS software system consists of two main subsystems, namely: the SILS IE (Interface Engine) subsystem for information interchange with heterogeneous external applications, developed by ManTech Advanced Systems International; and, the SILS MRAT (Mission Readiness Analysis Toolkit) subsystem for intelligent decision-support with collaborative software agents, developed by CDM Technologies. This report is focused specifically on the technical aspects of the SILS MRAT subsystem. The automated reasoning capabilities of SILS MRAT are supported by a knowledge management architecture that is based on information-centric principles. Such an architecture utilizes a virtual model of the real world problem situation, consisting of data objects with characteristics and a rich set of relationships. Commonly referred to as an ontology, this internal information model provides a common vocabulary and context for software agents with reasoning capabilities. The concurrent need for incremental capability increases implies a steadily increasing data load from diverse operational (dynamic) and historical (static) data sources, ranging from free text messages and Web content to highly structured data contained in consolidated operational data stores, Data Warehouses, and Data Marts. In order to provide useful high-level capabilities the architecture is required to support the transformation of these data flows into information and knowledge relevant to the concerns and operational context of individual shipboard users. Accordingly, the system must be capable of not only storing data but also the relationships and higher level concepts that place the data into context. For this reason, to manage an increasing number of relationships and concepts over time, the SILS MRAT subsystem was designed to employ a formalized ontological framework. There were four additional considerations in the selection of the overall SILS architecture. First, utility to support a useful level of automated information management (i.e., the ability to collaboratively analyze data, monitor dynamic operational context, formulate warnings and alerts, and generate recommendations). Second, flexibility to accommodate contributions from multiple team members that may employ differing technologies and implementation paradigms. Third, scalability to allow a progressive increase in the breadth and diversity of the data sources, the volume of data processed, the number of validated components, and the intelligence of the tools (i.e., agents). Fourth, adaptability to facilitate the tailoring of the information management capabilities to different data sources and existing data environments. The current SILS architecture addresses these desirable characteristics by partitioning the system into a lower-level data collection and integration layer, a higher-level information management layer (SILS MRAT), and a translation facility that is capable of mapping the data schema of the lower layer to the information representation (i.e., ontology) of the upper layer (SILS IE). The higher-level information management layer provides a collaborative, distributed communication facility that supports the development of semi-autonomous modules of capability referred to as agents. The agents employ the formalized ontology supported by the communication facility to collaborate with each other and the human users in a meaningful manner