1000 resultados para Electricity Monitoring


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Maintaining the ecosystem is one of the main concerns in this modern age. With the fear of ever-increasing global warming, the UK is one of the key players to participate actively in taking measures to slow down at least its phenomenal rate. As an ingredient to this process, the Springer vehicle was designed and developed for environmental monitoring and pollutant tracking. This special issue paper highlighted the Springer hardware and software architecture including various navigational sensors, a speed controller, and an environmental monitoring unit. In addition, details regarding the modelling of the vessel were outlined based mainly on experimental data. The formulation of a fault tolerant multi-sensor data fusion technique was also presented. Moreover, control strategy based on a linear quadratic Gaussian controller was developed and simulated on the Springer model.
Gaussian controller is developed and simulated on the Springer model.

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This paper provides an overview of the current field in wireless networks for monitoring and control. Alternative wireless technologies are introduced, together with current typical industrial applications. The focus then shifts to wireless Ethernet and the specialised requirements for wireless networked control systems (WNCS) are discussed. This is followed by a brief look at some current WNCS research, including reduced communication control.

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This paper introduces a fast algorithm for moving window principal component analysis (MWPCA) which will adapt a principal component model. This incorporates the concept of recursive adaptation within a moving window to (i) adapt the mean and variance of the process variables, (ii) adapt the correlation matrix, and (iii) adjust the PCA model by recomputing the decomposition. This paper shows that the new algorithm is computationally faster than conventional moving window techniques, if the window size exceeds 3 times the number of variables, and is not affected by the window size. A further contribution is the introduction of an N-step-ahead horizon into the process monitoring. This implies that the PCA model, identified N-steps earlier, is used to analyze the current observation. For monitoring complex chemical systems, this work shows that the use of the horizon improves the ability to detect slowly developing drifts.