RADIOENGINEERING, VOL. 27, NO. 4, DECEMBER 2018 1119 DOI: 10.13164/re.2018.1119 SIGNALS PD Source Location Utilizing Acoustic TDOA Signals in Power Transformer by Fuzzy Adaptive Particle Swarm Optimization Kalyan Chakravarthi MEKA, A. V. GIRIDHAR, D. V. S. S. SIVA SARMA Dept. of Electrical Engineering, National Institute of Technology Warangal, TS, 506004, India kalyan.mvr@gmail.com, {giridhar, dvss}@nitw.ac.in Submitted February 2, 2018 / Accepted September 18, 2018 Abstract. Partial discharge (PD) source location using acoustic emission (AE) is widely utilized by many trans- former manufacturers and power utility engineers in rou- tine 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 differ- ence of arrival) signals from the sensors fixed on the power transformer. The solution for PD source location is ac- quired by making these nonlinear equations as optimiza- tion problem. In this technique, the inertia weight is effec- tively 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 re- sults 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. Keywords Acoustic emission, partial discharge, fuzzy adaptive particle swarm optimization, fuzzy rules, source localization 1. Introduction Partial discharges are the initial root cause of insula- tion degradation in any high voltage power apparatus. Its accurate detection and diagnosis in time is essential other- wise simultaneously the degradation of insulation may happen progressively and discharge level increases drasti- cally. It may lead to a catastrophic failure and results in outage and penalty costs. Therefore, the identification and diagnosis of PD’s is prerequisite at inception stage where the unexpected failures and further losses can be dimin- ished [1–3]. PD generates acoustic waves which transmit inside the transformer tank. The acoustic sensors are fixed on the transformer tank’s external side to detect the propa- gation of acoustic signals from the PD source [4–6]. This acoustic method is an indirect method and has distinct merits compared to direct electrical methods. They are non- invasive, easier installation and non-interference. The TDOA (Time Difference of Arrival) method [7], [8] is widely used by many researchers for accurate PD source localization. In this paper, the TDOA method is used for time differences of AE signals from a reference sensor to remaining sensors thus PD source location is detected. Lu et al. demonstrated the pattern recognition ap- proach for PD source location in the simulated oil filled transformer tank by partitioning into small modules. The PD source is located with high location error by calculating the minimum distance between the standard and its unde- termined pattern vectors of the total tank. Thereby, this approach is inaccurate for the onsite measurement [5]. Markalous et al. [7] utilized pseudo time approach in global position system (GPS) algorithm for PD source localization detected by acoustic sensors but its drawback is sometimes it gives confused solution. Veloso et al. [9], [10] compared the least square (LS) method and genetic algorithm for localization of PD source and it is located accurately with large population size and iterations by genetic method whereas iterative LS method located with inaccurate manner. Tang et al. [11] compared iterative LS method and PSO method for localization of PD source, when compared to LS method, PSO method gives better location results. Kundu et al. [12] illustrated a non-iterative method for localization of PD source and its demerit is, it also yields two different solutions similar to GPS algorithm and in that only one solution is true. The obtained location error is also high, Kill et al. has demonstrated 2-D co-ordi- nate system using three acoustic sensors and located the PD source with 1% error in laboratory prototype model [13]. Kuo et al. [14] used combined PSO-ANN (artificial neural network) for PD defects recognition under noisy and noiseless conditions with 80% success rate. Bozcar et al. [15] used artificial neural network for recognition of PD defects in paper oil insulation damaged by aging action. H. L. Liu et al. [16] applied sequential quadratic program-