This paper presents a method to detect transient disturbances in a multivariate context, and an extension of that method to handle multi-rate systems. Both methods are based on a time series analysis technique known as nearest neighbors, and on multivariate statistics implemented as a singular value decomposition. The motivation for these developments is that there is an increasing industrial requirement for the analysis of data sets comprising measurements from industrial processes together with their associated electrical and mechanical equipment. These systems are increasingly affected by transient disturbances, and their measurements are commonly sampled at different rates. The paper demonstrates superior results with the multivariate method in comparison to the univariate approach, and with the multi-rate method in comparison to a uni-rate method, for which the fast-sampled measurements had to be downsampled. The method is demonstrated on experimental and industrial case studies.

Transient disturbances in process measurements compromise the accuracy of some methods for plant-wide oscillation analysis. This paper presents a method to remove such transients while maintaining the dynamic features of the original measurement. The method is based on a nearest neighbors imputation technique. It replaces the removed transient with an estimate which is based on the time series of the whole measurement. The method is demonstrated on experimental and industrial case studies. The results demonstrate the efficacy of the method and recommended parameters. Furthermore, inconsistency indices are proposed which facilitate the automation of the method.

Abstract-Efficiency and sustainability considerations have propelled changes in power and process industries. These changes, which include the increased electrification of process industries, are causing concerns about the reliability of future electricity supplies, and therefore motivate the need for a Smart Grid on an industrial scale. This paper presents a method by which process automation engineers can assess the suitability of an oil and gas plant to participate in power system frequency control services. This paper discusses the necessary specifications for an automated system that enables effective variable operation by analysing the safe operating envelope of the plant. To do that, this paper proposes a methodology to characterise the appropriate actuators, variables and limits of set-point change. This methodology is applied to a case study representing an oil processing facility. The resulting analysis indicates demandside response capability that the facility can provide without jeopardising operations on-site.

This paper proposes a correction method, which corrects the actual compressor performance in real operating conditions to the equivalent performance under specified reference condition. The purpose is to make fair comparisons between actual performance against design performance or reference maps under the same operating conditions. Then the abnormal operating conditions or early failure indications can be identified through condition monitoring, which helps to avoid mandatory shutdown and reduces maintenance costs. The corrections are based on an iterative scheme, which simultaneously correct the main performance parameters known as the polytropic head, the gas power, and the polytropic efficiency. The excellent performance of the method is demonstrated by performing the corrections over real industrial measurements.

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