Advances in Electrical Engineering

Volume 2016, Article ID 8651630, 10 pages

http://dx.doi.org/10.1155/2016/8651630

## Intelligent Fault Diagnosis in a Power Distribution Network

^{1}Centre for Space Transport and Propulsion, National Space Research and Development Agency (NASRDA), Epe, Lagos, Nigeria^{2}Department of Electrical and Electronics Engineering, University of Lagos, Lagos, Nigeria

Received 27 June 2016; Revised 15 September 2016; Accepted 26 September 2016

Academic Editor: Pascal Venet

Copyright © 2016 Oluleke O. Babayomi and Peter O. Oluseyi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper presents a novel method of fault diagnosis by the use of fuzzy logic and neural network-based techniques for electric power fault detection, classification, and location in a power distribution network. A real network was used as a case study. The ten different types of line faults including single line-to-ground, line-to-line, double line-to-ground, and three-phase faults were investigated. The designed system has 89% accuracy for fault type identification. It also has 93% accuracy for fault location. The results indicate that the proposed technique is effective in detecting, classifying, and locating low impedance faults.

#### 1. Introduction

Fault diagnosis and resolution in a power system network are essential for clearing faults that manifest in an electrical power transmission or distribution network. The process of fault resolution comprises three stages: first, the detection and identification or classification of unusual voltage and current characteristics at the affected portions of the network; next, the location of the incidence of the fault to enable quick access and solution to the problems that arise in the power network; and finally, the fault being cleared within the shortest time possible to prevent damage to unaffected parts of the network.

##### 1.1. Related Work

Numerous studies have been carried out on the use of intelligent methods for electric fault diagnosis in an electrical system. Some of these methods include expert systems, artificial neural networks, and fuzzy logic. The following review of related literature will be delimited to artificial neural network and fuzzy logic applications.

In certain studies, neural network principles were not applied to the power fault diagnostic process. Thus, they lacked the capabilities to learn from data gathered from the electrical network. This was the case in [1], where fuzzy logic-based fault identification in an electric power distribution system was studied and proven to produce accurate classifications of fault types. In addition, the fuzzy logic method was used in combination with discrete wavelet transform and resulted in accurate fault identification [2]. In [3], the data collected by alarms and protection relays in a power network was analyzed with neurofuzzy techniques. A classification based on input signals into faulty component type with a high degree of accuracy was achieved in spite of corrupted alarm signals. Petri net and neurofuzzy methods were used in [4, 5] for fault location in power lines and sections. The adaptive neurofuzzy inference system (ANFIS) was employed in [6] for accurate fault location for transmission lines and underground cables. However, the described procedures in [3–6] are not suitable for power distribution networks.

A neurofuzzy means of fault classification, location, and power restoration plan in an electric power distribution system was developed in [7]. Three ANFIS modules were employed for fault type classification, -coordinates, and -coordinates of the fault location, respectively. The resulting system performed with a high degree of accuracy. However, it has a shortcoming in the level of accuracy of fault type classification which can be improved upon. The robustness and precision of ANFIS were validated in [8] by testing the characteristics of the system after the addition of white noise to input data.

The accuracy of the fuzzy inference system method of fault diagnosis varies with complementary analytical tools employed to enhance the capabilities of the system. Reference [8] reported 78% accuracy in fault type identification when the fuzzy inference was used to analyze data derived by the wavelet transform method. 91% accuracy was reported in [9] for fault detection, while 93% accuracy was achieved for fault location when the first and third harmonic data sets derived from discrete Fourier transform of fault current were applied to intelligent fault diagnosis. In [10], a wavelet multiresolution analysis was used to extract harmonics generated by current transients due to fault incidences. This data was used to develop an ANFIS model that gave accurate fault location with a maximum error of 5%. Furthermore, through a Monte Carlo simulation, the derived algorithm was proven to be immune to the effects of fault impedance, power angle, fault distance, and fault inception angle. While past researchers have focused on the application of either fuzzy logic or ANFIS for fault detection, classification, and location, we propose the use of a fuzzy logic controller for fault identification and ANFIS for fault location for enhanced accuracies.

This research work focuses on the detection, identification, and location of faults in the distribution network of a power system. Fault detection is achieved by a fault analysis and the determination of positive, negative, and zero sequence currents and voltages of the power network. Thereafter, a fuzzy controller is included in the network to identify the fault type on the occurrence of a fault. In addition, a neurofuzzy based fault location model is developed. A distribution network in the Nigerian power grid is used as a case study. The ensuing discussion starts with a computational model, describes the methodology employed in the study, and rounds off with insights drawn from the results obtained.

#### 2. Computational Model

##### 2.1. Fault Analysis

Electric power faults in a distribution system occur randomly and their severity varies in intensity. There are four major types of faults: namely, three-phase fault, single line-to-ground fault (LG), line-to-line fault (LL), and double line-to-ground fault (LLG). Amongst these, the three-phase fault is the most severe when it occurs in a power grid, while the line-to-ground fault arises most commonly. LG, LL, and LLG faults cause unbalanced currents to flow through the network and sequence diagrams are employed in the analysis of unbalanced fault incidents in electric networks. The fault currents due to three-phase LG, LL, and LLG faults are given by (1) to (4), respectively. represents the fault impedance, represents the impedance at bus , are the zero, positive, and negative sequence fault currents in Phase A, respectively, and is the per unit voltage at the fault point:

##### 2.2. Fuzzy Inference System

Fuzzy logic involves reasoning algorithms that mimic human thinking in a manner describable as a kind of gray logic, as opposed to binary logic that uses only two values. Therefore, fuzzy logic associates input data with a range of values between 0 and 1. The data is thus processed by a fuzzy controller in three stages: namely, fuzzification, fuzzy processing, and defuzzification. Fuzzification translates input data into a fuzzy form with the aid of input membership functions. Common membership functions include the triangular-shaped, bell-shaped, S-shaped, and Z -shaped functions. Fuzzy processing associates the fuzzified inputs via a set of IF…THEN rules to determine how the input membership functions will associate. Finally, defuzzification converts the value from the processing stage into an output using methods such as the centre of gravity method and the maximum value method. There are two common fuzzy inference systems (FIS): namely, Sugeno-type FIS and Madami-type FIS. The Sugeno FIS is efficient and works well with mathematical, linear, optimization, and adaptive techniques. On the other hand, the Mandami FIS is intuitive and suitable for human input.

##### 2.3. Adaptive Neurofuzzy Inference System

The difference between the adaptive neurofuzzy inference system (ANFIS) and the FIS is that, with the FIS, only fixed membership functions that are chosen arbitrarily are used. However, ANFIS membership functions are adapted to a historical data set. FIS modeling relies heavily on the user’s interpretation of the relationship between the input and output data. On the other hand, ANFIS improves the process by adapting the input and output membership functions to the relationship of a sample set of input/output data. This adaptation for bespoke membership functions is attained through neuroadaptive learning. The learning process works similarly to that of neural networks and calculates membership function parameters that optimally permit the fuzzy inference system to track the input/output data according to the following steps:(1)Postulate a model structure that relates inputs to outputs through membership functions and fuzzy rules using the Sugeno-type FIS.(2)Collect input/output data for training by ANFIS.(3)Train the initial model with the data provided constrained by an error criterion.

##### 2.4. Case Study

Nigeria’s electricity transmission grid is at a voltage of 330 kV, while the distribution network is a 33 kV/11 kV system managed by eleven distribution companies across the country. The test network shown in Figure 1 is an extract from Orile District distribution system, under Eko Electricity Distribution Company (EKEDC).