Automatic surveillance in power system telecontrol applying embedded and multi-agent system technologies

Sivianes, F.; Romero, Manuel; Hernández, Marta; Carrasco, Alejandro; Escudero, J.I.

doi:10.1109/isie.2008.4676938

Cited by 3 publications

(5 citation statements)

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“…This implementation of the proposed Bayesian network was carried out over a previously developed multiagent system, based on JADE [37] and called CARISMA [31,38,39,40]. This multiagent system uses expert systems to make decisions or produce recommendations.…”

Section: Methodsmentioning

confidence: 99%

“…Since the CARISMA system had already been implemented with an expert system developed with Drools [31,42], this was reused in order to undertake the objectives with the Bayesian network. First, as discussed earlier, the initial proposal was to connect the Bayesian network to the expert system so that the system would be capable of generating a response to particular input events.…”

Section: Methodsmentioning

confidence: 99%

“…In order to study the performance of the implemented system, a specific scenario was set up where its operation was studied in detail. Bear in mind that the implementation was carried out within the base system developed: CARISMA [31,38,39,40]. This system originates from a solar farm that consists of three zones, of which all zones have a common teleoperator agent, although each zone has its own coordinator and operator agents.…”

Section: Measurements and Efficiency Testsmentioning

confidence: 99%

“…In order to add this reasoning to a multiagent system, a Bayesian model is proposed and developed, which determines the state of each battery, controlled by this multiagent system CARISMA [31], in a probabilistic way. CARISMA already incorporates an expert system for decision-making control.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms

Romero-Ternero

Oviedo-Olmedo

Carrasco

et al. 2019

Sensors

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A common problem in solar farms is to predict when accumulators stop working optimally and start losing efficiency. This paper proposes and describes how to use Bayesian networks together with expert systems to predict this moment by using a telecontrol multiagent system for monitoring solar farms with distributed sensors, which was developed in a previous work. To this end, a Bayesian network model and its implementation are proposed. The resulting system meets the requirements of telecontrol systems (reliability, flexibility, and response time), yields a solution for the prediction of lifespan batteries, and provides the multiagent system with autonomous intelligent capabilities and integrated learning.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Measurements and Efficiency Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms

Romero-Ternero

Oviedo-Olmedo

Carrasco

et al. 2019

Sensors

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“…One agent node is attached various sensors and actuator devices. The sensors include thermal sensors, humidity sensors, CO2 sensors, sensors for signal from solar plant appliances and various intelligent meters such as solar irradiation and video control [6]. The attached actuators include various valves, motors, and switchers for heating system, ventilation, humidity control, screen control, etc.…”

Section: Architecture Overviewmentioning

confidence: 99%

Architecture for Multiagent-Based Control Systems

Oviedo

Romero-Ternero

Hernández

et al. 2010

Advances in Intelligent and Soft Computing

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Abstract. This paper presents a multiagent architecture that covers the new requirements for the new control systems such as the distribution and decentralisation of system elements, the definition of communications between these elements, the fast adaptation in the control and organizational changes. The agents in this architecture can cooperate and coordinate to achieve a global goal, encapsulate the hardware interfaces and make the control system easily adapt to different requirements through configuration. Finally, the proposed architecture is applied to a control system of a solar power plant, obtaining a preliminary system that achieve the goals of simplicity, scalability, flexibility and optimization of communications system.

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