Embedded Intelligence for Electrical Network Operation and Control

Catterson, Victoria M.; Davidson, E.M.; McArthur, S.D.J.

doi:10.1109/mis.2011.5

Cited by 20 publications

(8 citation statements)

References 8 publications

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“…Information fusion between systems for condition assessment and, say, power flow management or voltage control would allow asset health to be taken into account when planning control actions, and allow network events such as faults to be considered when assessing insulation health [46]. This type of systems integration requires a platform or method of communication between each sub-system, such as the multi-agent architecture discussed in the previous section.…”

Section: Increased Network Datamentioning

confidence: 99%

The impact of smart grid technology on dielectrics and electrical insulation

Catterson

Castellon

Pilgrim

et al. 2015

IEEE Trans. Dielect. Electr. Insul.

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Delivery of the Smart Grid is a topic of considerable interest within the power industry in general, and the IEEE specifically. This paper presents the smart grid landscape as seen by the IEEE Dielectrics and Electrical Insulation Society (DEIS) Technical Committee on Smart Grids. We define the various facets of smart grid technology, and present an examination of the impacts on dielectrics within power assets. Based on the trajectory of current research in the field, we identify the implications for asset owners and operators at both the device level and the systems level. The paper concludes by identifying areas of dielectrics and insulation research required to fully realize the smart grid concept. The work of the DEIS is fundamental to achieving the goals of a more active, self-managing grid.

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Section: Increased Network Datamentioning

confidence: 99%

The impact of smart grid technology on dielectrics and electrical insulation

Catterson

Castellon

Pilgrim

et al. 2015

IEEE Trans. Dielect. Electr. Insul.

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“…To improve the automation of network flows, recently, the autonomous agent-based type of control has been gaining popularity [15][16][17][18]. However, despite the intensive recent research on multi-agent systems control, currently there is a lack of algorithms for optimal flow management, which guarantees that the independent interventions of the autonomous agents upon overloading and congestion will eventually lead to a minimum generation shedding from the sources and to an optimum utilization of the residual capacity of the network.…”

Section: Additional Start Nodementioning

confidence: 99%

“…In the case of component failures, the mitigating actions from the autonomous agents are reduced to sending signals to shed load from the sources of flow. This approach requires special control systems in place, each monitoring for a different scenario and requiring a different control [15]. This approach not only leads to very complex control actions that are not at all straightforward and transparent.…”

Section: Additional Start Nodementioning

confidence: 99%

The Throughput Flow Constraint Theorem and its Applications

Todinov¹

2014

IJACSA

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Abstract-The paper states and proves an important result related to the theory of flow networks with disturbed flows:"the throughput flow constraint in any network is always equal to the throughput flow constraint in its dual network".After the failure or congestion of several edges in the network, the throughput flow constraint theorem provides the basis of a very efficient algorithm for determining the edge flows which correspond to the optimal throughput flow from sources to destinations which is the throughput flow achieved with the smallest amount of generation shedding from the sources.In the case where a failure of an edge causes a loss of the entire flow through the edge, the throughput flow constraint theorem permits the calculation of the new maximum throughput flow to be done in ) (m O time, where m is the number of edges in the network.In this case, the new maximum throughput flow is calculated by inspecting the network only locally, in the vicinity of the failed edge, without inspecting the rest of the network.The superior average running time of the presented algorithm, makes it particularly suitable for decongesting overloaded transmission links of telecommunication networks, in real time.In the paper, it is also shown that the deliberate choking of flows along overloaded edges, leading to a generation of momentary excess and deficit flow, provides a very efficient mechanism for decongesting overloaded branches. Keywords-networks with disturbed flows; congestion; decongestion; maximum throughput flow; telecommunication networks I. THE NEED FOR A HIGH-SPEED CONTROL OF FLOW NETWORKSAlthough almost all real networks are networks with disturbed flows, the focus of existing research on flow networks has been exclusively on static flow networks. Research and algorithms related to static flow networks has been presented in [1][2][3]. The first majorcategory of algorithms for maximising the throughput flow in networks includes the augmentation algorithms which preserve the feasibility of the network flow at all steps, until the maximum throughput flow is attained [4][5].The second major category of algorithms are based on the preflow concept used in [6] and subsequently in [7] and [8]. The central idea behind these algorithms is converting the preflow into a feasible flow.The best of these methods however, have a polynomial running time and do not provide the necessary computational speed for re-optimising the throughput flow in a large and complex network in real time, after an edge failure or congestion. The main reason is that classical algorithms for maximising the throughput flow start from a network with empty edges and do not make use of special properties of the network providing a short cut to determining the maximum throughput flow.The central question for networks with disturbed flows is how to re-optimise the network flows after an edge flow disturbance (caused by edge failure or congestion), so that a new optimal throughput flow is attained quickly.The concept 'new optimal throughput flow' me...

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“…So far, neural networks, reinforcement learning, genetic algorithms, fuzzy systems, particle swarm optimization and multi-agent systems are the usual AI techniques used for confronting Smart Grid problems. For instance, Cartterson et al [25] discuss the processing of electric grid data through a number of artificial intelligence techniques (e.g., Constraint Programming, rule-based Expert Systems considering fuzziness) applied to diverse application domains such as autonomous control of distribution networks, condition monitoring, post-fault analysis or voltage sag and swell monitoring. To this end, they also put forward a multi-agent architecture but do not go beyond stating that CIM and the IEC 61850 should be the key standard for syntactical interoperability (not semantic).…”

Section: Related Workmentioning

confidence: 99%

Distributed Semantic Architecture for Smart Grids

et al. 2012

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The smart grid revolution demands a huge effort in redesigning and enhancing current power networks, as well as integrating emerging scenarios such as distributed generation, renewable energies or the electric vehicle. This novel situation will cause a huge flood of data that can only be handled, processed and exploited in real-time with the help of cutting-edge ICT (Information and Communication Technologies). We present here a new architecture that, contrary to the previous centralised and static model, distributes the intelligence all over the grid by means of individual intelligent nodes controlling a number of electric assets. The nodes own a profile of the standard smart grid ontology stored in the knowledge base with the inferred information about their environment in RDF triples. Since the system does not have a central registry or a service directory, the connectivity emerges from the view of the world semantically encoded by each individual intelligent node (i.e., profile + inferred information). We have described a use-case both with and without real-time requirements to illustrate and validate this novel approach.

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Embedded Intelligence for Electrical Network Operation and Control

Cited by 20 publications

References 8 publications

The impact of smart grid technology on dielectrics and electrical insulation

The impact of smart grid technology on dielectrics and electrical insulation

The Throughput Flow Constraint Theorem and its Applications

Distributed Semantic Architecture for Smart Grids

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