PD Source Location Utilizing Acoustic TDOA Signals in Power Transformer by Fuzzy Adaptive Particle Swarm Optimization

Abstract

Partial discharge (PD) source location using acoustic emission (AE) is widely utilized by many transformer manufacturers and power utility engineers in routine and critical situation for optimal operation of the electrical power system as well as further risk management and repair planning. The PD detection is not enough to take solution, so identification of PD source is essential to restore apparatus condition. This work aim is to localize the defect geometrically by means of TDOA (time difference of arrival) signals from the sensors fixed on the power transformer. The solution for PD source location is acquired by making these nonlinear equations as optimization problem. In this technique, the inertia weight is effectively regulated by using 49 and 9 simple IF-THEN fuzzy rules to improve the global optimal solution and impairs the local convergence problem and improves the accuracy in estimating the PD source location. The simulation results reveal that PD location accuracy with minimum of maximum deviation error, absolute error and relative error is better when compared to other constant parameter intelligent methods which were reported in the literature.

Analysis of the Generation of Vibration Signals under Uniaxial Loading of Materials Using the Coherent Properties of Laser Radiation

“…The output laser beam (a) is divided by a translucent mirror (5) into two equal-intensity beams (b and c), one of which (b) is directed through a translucent mirror (6) and lens (7) towards the test sample (8), placed in a loading device (3). The beam (d) is reflected from the surface of the test sample using the lens (7) and the translucent mirror (6) and is directed to the translucent mirror (10), where it is connected to the beam (c), which reaches here after being reflected from the translucent mirrors (5) (9).…”

“…Much attention is paid to the diagnosis of damage and monitoring the development of defects. In [6] a Kalman filter-based algorithm was used to measure the parameters of moving objects; in [7] diagnostic features were used for the condition monitoring of hypoid gear utilising the wavelet transform; in [8] location acoustic signals were used in a power transformer using fuzzy adaptive particle swarm optimisation; in [9] improved least square generative adversarial networks were applied for rail crack detection using the acoustic emission technique; in [10] energy distribution and fractal characterisation of acoustic emissions during coal deformation and fracturing were employed. Acoustic methods are based on the registration of stress waves arising as a result of loading and destruction of the structure of materials [11,12].…”

Impact of Power Transformer Oil-Temperature on the Measurement Uncertainty of All-Acoustic Non-Iterative Partial Discharge Location

“…The approach based on pattern recognition (lookup time delay vector table) is demonstrated in [ 3 ], different approaches employing triangulation based on time-of-flight measurements in [ 4 , 5 ], and the use of a non-iterative algorithm in [ 6 ]. More recent approaches are based on intelligent algorithms: genetic algorithms (GAs) [ 7 , 8 , 9 ], particle swarm optimization (PSO) algorithms [ 10 ], artificial neural networks (ANNs) [ 11 ], PSO in combination with ANNs [ 12 ] or with fuzzy logic [ 13 ], and bat algorithms [ 14 ]. The most effective results in practice are achieved by the simultaneous use of the acoustic and electrical methods, and the all-acoustic method is only used for the localization of PDs that occur in transformer oil [ 15 ].…”