A Novel Fault Detection and Classification Strategy for Photovoltaic Distribution Network Using Improved Hilbert–Huang Transform and Ensemble Learning Technique

Younis M. Nsaif, Molla Shahadat Hossain Lipu (), Aini Hussain (), Afida Ayob, Yushaizad Yusof and Muhammad Ammirrul A. M. Zainuri
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Younis M. Nsaif: Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Molla Shahadat Hossain Lipu: Department of Electrical and Electronic Engineering, Green University of Bangladesh, Dhaka 1207, Bangladesh
Aini Hussain: Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Afida Ayob: Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Yushaizad Yusof: Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Muhammad Ammirrul A. M. Zainuri: Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia

Sustainability, 2022, vol. 14, issue 18, 1-19

Abstract: Due to the increased integration of distributed generations in distributed networks, their development and operation are facing protection challenges that traditional protection systems are incapable of addressing. These problems include variations in the fault current during various operation modes, diverse distributed network topology, and high impedance faults. Therefore, appropriate and reasonable fault detection is highly encouraged to improve the protection and dependability of the distributed network. This paper proposes a novel technique that employs an improved Hilbert–Huang Transform (HHT) and ensemble learning techniques to resolve these challenges in a photovoltaic distributed network. First, improved HHT is utilized to extract energy features from the current signal. Second, variational mode decomposition (VMD) is applied to extract the intrinsic mode function from the zero component of the current signal. Then, the acquired energy feature and intrinsic mode function are input to the ensemble learning technique for fault detection and classification. The proposed technique is implemented using MATLAB software environment, including a classification learner app and SIMULINK. An evaluation of the results is conducted under normal connected mode (NCM) and island mode (ISM) for radial and mesh-soft normally open point (SNOP) configurations. The accuracy of the ensemble bagged trees technique is higher when compared to the narrow-neural network, fine tree, quadratic SVM, fine-gaussian SVM, and wide-neural network. The presented technique depends only on local variables and has no requirements for connection latency. Consequently, the detection and classification of faults using the proposed technology are reasonable. The simulation results demonstrate that the proposed technique is superior to the neural network and support vector machine, achieving 100%, 99.2% and 99.7% accurate symmetrical and unsymmetrical fault detection and classification throughout NCM, ISM, and dynamic operation mode, respectively. Moreover, the developed technique protects DN effectively in radial and mesh-SNOP topologies. The suggested strategy can detect and classify faults accurately in DN with/without DGs. Additionally, this technique can precisely detect high and low impedance faults within 4.8 ms.

Keywords: distributed network; distributed generation; fault detection; protection scheme; variational mode decomposition; improved Hilbert–Huang transform; ensemble learning technique (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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