The Electrostatic Wind Energy Converter: Electrical performance of a high voltage prototype

Wind energy is converted to electrical energy by letting the wind move charged particles against the direction of an electric field. The advantage of this type of conversion is that no rotational movement, which occurs in conventional wind turbines, is required. An electrostatic wind energy converter (EWICON) has been developed. Charged particles have been created using two spraying methods, electrohydrodynamic atomisation and high pressure monodisperse spraying. Using both methods, wind energy has been converted to electric energy and delivered to an electrical load with positive efficiency.Electrical Engineering, Mathematics and Computer Scienc

Windenergie zonder wieken

Electrical Engineering, Mathematics and Computer Scienc

Upcoming Role of Condition Monitoring in Risk-Based Asset Management for the Power Sector

The electrical power sector is stimulated to evolve under the pressures of the energy transition, the deregulation of electricity markets and the introduction of intelligent grids. In general, engineers believe that technologies such as monitoring, control and diagnostic devices, can realize this evolvement smoothly. Unfortunately, the contributions of these emerging technologies to business strategies remain difficult to quantify in straightforward metrics. Consequently, decisions to invest on these technologies are still taken in an ad hoc manner. This is far from the risk-based approach commonly recommended for asset management (AM). The paper introduces risk-based management as a guiding principle for maintenance management. Then, the triple-level AM model (strategic, tactical and operational) as the foundation to define risk-based AM is described. Afterwards, two categories of risks, one triggered by technical stimuli and the other by non-technical stimuli are introduced. It is shown that the main challenge of managing risks with technical stimuli is to have the ability to understand the technical cause of failures, which is located at the operational level within the triple-level AM model. One method to quantitatively understand the technical cause of failures is by means of condition diagnostic and monitoring technologies. Therefore, the aim of this paper is to clarify the potential contribution of condition diagnostic and monitoring technologies to risk-based decision making for the power sector. This paper shows that, in practice, the implementation of condition diagnostic and monitoring technologies is mainly driven by purely technical asset based considerations without evaluating the contribution to, for instance, risks. This paper provides a list of aspects in which condition diagnostic and monitoring may contribute to risk evaluation with technical stimuli. The listed aspects (which are: (1) asset specific condition data, (2) timely condition data and (3) predictive condition data) can be regarded as input for the probability of failure and as influencing input for the consequence of failure, hence benefiting quantitative risk studies and AM activities (such as condition assessment/maintenance or replacement). Finally, these benefits can be evaluated afterwards in a risk-based AM planning stage, so that asset managers can justify investments on necessary technical improvements of condition monitoring systems.Electrical Sustainable EnergyElectrical Engineering, Mathematics and Computer Scienc

Optimization of condition-based asset management using a predictive health model

In this paper, a model predictive framework is used to optimize the operation and maintenance actions of power system equipment based on the predicted health sate of this equipment. In particular, this framework is used to predict the health state of transformers based on their usage. The health state of a transformer is hereby given by the hot-spot temperature of the paper insulation of the transformer and is predicted using the planned loading of the transformer. The actual loading of the transformer is subsequently optimized using these predictions. If you want to cite this report, please use the following reference instead: G. Bajracharya, T. Koltunowicz, R.R. Negenborn, Z. Papp, D. Djairam, B. De Schutter, J. J. Smit. Optimization of condition-based asset management using a predictive health model. In Proceedings of the 16th International Symposium on High Voltage Engineering (ISH 2009), Cape Town, South Africa, August 2009.Electrical Sustainable EnergyElectrical Engineering, Mathematics and Computer Scienc