This study proposes a novel method of partial discharge (PD) pattern recognition based on the Hilbert-Huang transform (HHT) with fractal feature enhancement. First, this study establishes three common defect types with one blank sample of 25 kV cross-linked polyethylene (XLPE) power cable joints and uses a commercial acoustic emission sensor to measure the acoustic signals caused by the PD phenomenon. The HHT can represent instantaneous frequency components through empirical mode decomposition, and then transform to a 3D Hilbert energy spectrum. Finally, this study extracts the fractal theory feature parameters from the 3D energy spectrum by using a neural network for PD recognition. To demonstrate the effectiveness of the proposed method, this study investigates its identification ability using 120 sets of field-tested PD patterns generated by XLPE power cable joints. Unlike the fractal features extracted from traditional 3D PD images, the proposed method can separate different defect types easily and shows good tolerance to random noise.
This paper primarily discusses the measurement of partial discharge (PD) phenomena and clustering in the defect pattern of a cross-linked polyethylene power cable joint. First, a high-speed data acquisition and pretreatment were performed for PD electrical signals at a sampling rate of 20 MS/s. The crucial characteristic signals were reversed to reduce the calculated amount of noise. A characteristic matrix was created according to the resulting dynamic error of chaos synchronization. The characteristic parameters were extracted using the fractal theory. Finally, the extension theory was used to develop a diagnostic system and anti-interference test. A comparison with the existing Hilbert-Huang transform (HHT) method revealed that the two characteristics extracted from the chaos synchronization results using the fractal theory were recognized at a higher pattern recognition rate by employing the extension theory. The proposed method can extract crucial information concerning PD as a defect in power cable joints.
With a rapid charge/discharge
feature, vanadium
redox flow batteries (VRBs) are green, large-scale energy storage
devices useful for power smoothing in unstable renewable power generation
facilities, such as those involving solar and wind energy. This study
developed a VRB model to establish a relationship between electrolyte
concentration, equilibrium potential, and state of charge (SOC), to
simulate the dynamic responses in charge/discharge cycles of the electrolyte
concentration, terminal voltage, and SOC, and to evaluate the internal
loss and battery efficiency. The proposed model not only serves as
the basis of a dynamic analysis tool for future studies for designing
a large-scale VRB but also reveals the time-varying electrolyte status
for a long-term VRB operation.
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