Fault location estimation for series-compensated double-circuit transmission line using parameter optimized variational mode decomposition and weighted P-norm random vector functional link network

• Published in: Applied soft computing Vol. 85; p. 105860
• Main Authors: Sahani, Mrutyunjaya, Dash, P.K.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2019
• Subjects: Random vector functional link network; Real-time analysis; Series capacitor compensated double circuit transmission line; Variational mode decomposition; Variational mode decomposition; Real-time analysis; Random vector functional link network; Series capacitor compensated double circuit transmission line
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.105860

In this paper, both the proposed parameter optimized variational mode decomposition (POVMD) and weighted P-norm random vector functional link network (WPRVFLN) are integrated for fault detection, location estimation, and classification in a series capacitor compensated double circuit transmission line (SCCDCTL). The required number of decomposition is determined from the magnitude spectrum of full-cycle current signals from the point of fault inception. An entropy index is used to obtain the optimum value of data-fidelity factor for extracting the most suitable band-limited intrinsic mode functions (BLIMFs). The four efficacious features namely standard deviation of the magnitude, energy, Renyi entropy and crest factor are computed from the Hilbert transformed array of the BLIMFs to construct the feature vector. A diagonal matrix W is computed based on zero sequence current of the original current signals as a weighting factor to categorize the ground fault accurately. Numerous faults are generated with a wide variation of the system conditions in MATLAB/Simulink simulation environment. An efficient WPRVFLN computational intelligence technique is proposed to recognize the fault by taking the extracted feature vector with weight factor, and its performances are compared with the recently developed classifiers in the MATLAB interface. The lesser computational complexity, faster learning speed, superior fault location estimation accuracy, and short event detection time prove that the proposed POVMD–WPRVFLN method can be implemented in the real power system for online fault recognition. Finally, the feasibility of the proposed method is tested and validated by using the fast FPGA digital circuitry in a loop. [Display omitted] •Optimized VMD is used to extract features from faulted transmission line signals.•A weighted P-Norm RVFLN is used.•This novel approach has faster learning speed and better accuracy for faulted systems.•Real-time validation is achieved using the fast FPGA digital circuitry in a loop.