Ratio error estimation of the voltage transformers plays an important role in modern power delivery systems. Existing ratio error estimation methods mainly focus on periodical calibration but ignore the time-varying property. Consequently, it is difficult to efficiently estimate the state of the voltage transformers in real time. To address this issue, we formulate a time-varying ratio error estimation (TREE) problem into a large-scale multiobjective optimization problem, where the multiple objectives and inequality constraints are formulated by statistical and physical rules extracted from the power delivery systems. Furthermore, a set of TREE problems from different substations are systematically formulated into a benchmark test suite for characterizing their different properties. The formulation of these TREE problems not only transfers an expensive ratio error estimation task to a relatively cheaper optimization problem, but also promotes the research in large-scale multiobjective optimization by providing a real-world benchmark test suite with complex variable interactions and correlations to different objectives. To the best of our knowledge, this is the first time to formulate a real-world problem into a benchmark test suite for large-scale multiobjective optimization, and it is also the first work proposing to solve TREE problems via evolutionary multiobjective optimization.

With the widespread application of capacitor voltage transformers (CVTs) in relaying, power system control and metering, detecting their measurement error drift has become an important and urgent task. Conventional methods are time-consuming and require the power to be cut, while some novel data-driven methods are impractical given their rigorous preconditions. In this paper, an innovative approach is proposed to detect the measurement error drift of CVTs connected to the same phase. Through principal component analysis, both the ratio error drift and the phase displacement drift of the CVT can be detected on-line. Besides the theoretical analysis, a monitoring system equipped with the proposed approach is installed in a 220 kV thermal power plant. The field application indicates it is not only sensitive to the measurement error drift but also good at locating faults.