Improving Trust in Power System Measurements

Abstract: The power grid is a large and complex system. The system becomes larger and more complex daily. Distributed energy resources and a more active customer role are factors adding to the complexity. This complicated system is operated by a combination of human operators and automation. Effective control of the power grid requires an increasing amount of automation to support system operators. The need for more support from automation can only increase as operational complexity increases.Actions in controlling the system are entirely dependent on the results of measurements. Measurements inform decisions at all scales. How much trust can be placed in the measurements is essentially an unknown factor. North American Electric Reliability Corporation has generated reports showing that procedures and models have not always worked as expected. Part of the problem lies in the fact that system events can distort signal waveforms. Another part of the problem is that events taking place outside the control area of an operator can affect measured results. The companies involved, and their regulators, have had to change their requirements and guidelines.High “accuracy” measurements are available for most quantities of interest, but the problems are related to trustworthiness, rather than “accuracy.” Accuracy is established for a device within a controlled environment, where a “true value” can be estimated. Real-world conditions can be vastly different. The instrument may provide accurate output according to its specifications, but the measurement might not represent reality because what is happening in the real world is outside the bounds of these specifications. That is a problem that demands a solution. The crux of the matter is this: a real-world measurement’s usefulness as a decision-making aid is related to how believable the measurement is, and not to how accurate the owner’s manual says the instrument is. The concept of “uncertainty” that metrologists have refined over the last few decades is a statistical process that predicts the dispersion of future results. Such a measure is virtually meaningless for real-time power system use. The properties of the power system are not stationary for long periods. A low-quality result can lead to a bad decision, because power system measurements presently lack any kind of real-time “trustworthiness connection.”The signal model generally used in the electric power industry is that the voltages and currents are well-represented by mathematical sinusoids. Given that starting point, we describe two trust metrics that provide verifiable links to the real-time system being measured. The metrics capture any mismatch between the instrument measurement model and the actual signal. Our trust-awareness metrics can lead to ways to develop more robust operating models in the power system environment. Every measurement result is reported with an associated real-time trust (or no-trust) metric, allowing the user (whether human or not) to assess the usefulness of the result. It is, of course, up to the user to determine how a low-quality result should be used in decision-making. Examples of real-time trust metric calculations during real power system events are provided, with evaluation for application in utility user scenarios.

Keywords: Trust metric, Measurement theory, Definitional uncertainty

DOI: 10.54941/ahfe1003577

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