On-line fault detection technique for voltage transformers

Abstract

Conventional voltage transformers (CVTs) are still widely used as transducers in power networks, thanks to their high reliability, insulation capability, low drift over time and temperature. Their rugged construction is very often used as justification for skipping periodical tests and calibrations, that require putting them off-line, thus avoiding a time-consuming and expensive procedure. For this reason, in the last years, a growing interest has been addressed towards the study of online diagnostic and calibration procedures. The typical approach is based on the frequency response analysis that permits, under sinusoidal excitation, to detect possible deterioration of the behavior of CVT. Anyway, the real interest is to check the CVT fleet already installed and operating on the grid without requiring their disconnection from the grid. As well known, distribution grid voltage features a non negligible harmonic distortion, which may allow the online evaluation of the frequency response of the transformer, by simply connecting a reference transducer. Unfortunately, being the harmonics much lower than the fundamental, this approach cannot be employed in a straightforward way because of the nonlinear behavior of the CVT. This paper proposes an innovative condition monitoring technique of CVTs based on a simplified Volterra model. This opens the way to a new approach to the on-site characterization of CVTs, exploiting the actual voltage of the grid and thus not requiring its disconnection

Feature Extraction and Comprehension of Partial Discharge Characteristics in Transformer Oil from Rated AC Frequency to Very Low Frequency

“…As shown in Figure 17, discharge occurs after a discharge delay τ delay when the applied voltage exceeds the theoretical discharge value. For u(t) = U m sin(2πft), where u(t) is the applied voltage and U m is its peak value with the unit of kV, the initial time is shown in Equation (4). Under lower frequency, the voltage phase that τ delay occupies decreases, causing the initial phase to be put forward, as shown in Figure 8.…”

“…To detect the defect in time and correctly avoid the serious faults of electrical equipment and interruption of power supply. One category of defect recognition methods is based on the operation condition, for example, the armature current of the direct-current (DC) generator [1], the current signal of three phase induction motor [2], sweep frequency response analysis to check the deformation [3], a simplified frequency-domain Volterra model to detect inter-turn faults [4], and so on. Another category of methods is based on the discharge information.…”

Online Evaluation Method of CVT Internal Insulation Abnormality Based on Self-Supervised Learning

“…The literature [11] monitors and analyzes changes in the secondary voltage of the CVT to sense an early abnormal internal insulation status of the CVT in advance, but this method relies on the rich experience of professionals to analyze it. The literature [12] compares the actual voltage input of the power grid with the output of the prediction model to evaluate the health status of the CVT. However, this method does not take into account the impact of the CVT secondary measurement voltage offset caused by the CVT primary voltage fluctuations.…”

Identification of Power Transformer Winding Fault Types by a Hierarchical Dimension Reduction Classifier

“…According to a statement in [8], statistics show that 70% to 80% of failure regarding power transformers are caused by undetected winding faults. Commonly used approaches for detecting incipient winding faults are usually offline tests, such as the short-circuit impedance measurement (SCI) [9], low-voltage impulse tests (LVI) [10], and frequency response analysis (FRA) [11][12][13][14][15].…”

“…Among the existing test approaches, FRA is a well-recognized technique for diagnosing incipient winding faults in power transformers [11][12][13][14][15]. Depending on the types of injected signals, there are two major types of FRA, which are the sweep frequency response analysis (SFRA) and impulse frequency response analysis (IFRA) [8].…”