Distribution systems are continuously exposed to fault occurrences due to various reasons, such as lightning strike, failure of power system components due to aging of equipment and human errors. These phenomena affect the system reliability and results in expensive repairs, lost of productivity and power loss to customers. Since fault is unpredictable, a fast fault location and isolation is required to minimize the impact of fault in distribution systems. Therefore, many methods have been developed since the past to locate and detect faults in distribution systems with distributed generation. The methods can be divided into two categories, conventional and artificial intelligence techniques. Conventional techniques include travelling wave method and impedance based method while artificial intelligence techniques include Artificial Neural Network (ANN), Support Vector Machine (SVM), Fuzzy Logic, Genetic Algorithm (GA) and matching approach. However, fault location using intelligent methods are challenging since they require training data for processing and are time consuming. In this paper, most of the techniques that have been developed since the past and commonly used to locate and detect faults in distribution systems with distributed generation are reviewed. Research works in fault location area, the working principles, advantages and disadvantages of past works related to each fault location technique are highlighted in this paper. Hence, from this review, the opportunities in fault location research area in power distribution system can be explored further.

Fault location and detection techniques in power distribution systems with distributed generation: A review

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field.

A Review of Fault Diagnosing Methods in Power Transmission Systems

An important challenge for our society is the transformation of traditional power systems to a decentralized model based on renewable energy sources. In this new scenario, advanced devices are needed for real-time monitoring and control of the energy flow and power quality (PQ). Ideally, the data collected by Internet of Thing (IoT) sensors should be shared to central cloud systems for online and off-line analysis. In this paper openZmeter (oZm) is presented as an advanced low-cost and open-source hardware device for high-precision energy and power quality measurement in low-voltage power systems. An analog front end (AFE) stage is designed and developed for the acquisition, conditioning, and processing of power signals. This AFE can be stacked on available quadcore embedded ARM boards. The proposed hardware is capable of adapting voltage signals up to 800 V AC/DC and currents up to thousands of amperes using different probes. The oZm device is described as a fully autonomous open-source system for the computation and visualization of PQ events and consumed/generated energy, along with full details of its hardware implementation. It also has the ability to send data to central cloud management systems. Given the small size of the hardware design and considering that it allows measurements under a wide range of operating conditions, oZm can be used both as bulk metering or as metering/submetering device for individual appliances. The design is released as open hardware and therefore is presented to the community as a powerful tool for general usage.

An Open Hardware Design for Internet of Things Power Quality and Energy Saving Solutions

Distributed generation (DG) is the future of energy. This technology allows the bidirectional flow of power within an electrical network. Researchers are faced with many challenges to the accurate implementation of protection schemes for DG-connected distribution network. The schemes designed must satisfy the performance requirements of selectivity, reliability, and sensitivity. Most researchers opine that conventional protection schemes based on over current detection are insufficient to completely and accurately protect a DG-connected distributed power system. There are many challenges that need to be tackled before embarking upon the journey to successfully implement these schemes. This paper summarizes the major challenges which one can encounter while designing protection schemes for DG-connected distribution networks. Some possible solutions from the literature are also mentioned. Moreover, a suggested solution for protecting future active distribution networks is provided. It is expected that this paper will act as a benchmark for future researchers in this field to tackle the challenges related to the protection of active distribution networks.

/pdf/protection-of-distributed-generation-challenges-and-3p3ffhba8u.pdf

Protection of Distributed Generation: Challenges and Solutions

This paper depicts an approach with an artificial neural network (ANN) for classification and localization of faults on the power transmission line. A huge number of transmission and distribution lines are used for the transportation of electrical energy in the power system. Moreover, developing a completely reliable system is not possible within the economic and technical limitations. So, there always remains a probability of faults in the transmission lines. When occurs, it is aimed to locate and classify the fault to restore the sound condition of the transmission line. For analyzing the transmission line fault, this paper adopts the fault-tolerant neural network-based method as it can process incomplete and noisy data. This approach can deal with nonlinear problems and can carry out the prediction if trained. Feedforward neural networks along with the backpropagation algorithm are used each of the three phases of the transmission line for the fault localization process. The proposed method uses the instantaneous measure of the fault current to return the fault type and the distance from the experimental end. The modeling of the power system and the development of the neural network for this method have been conducted in the MATLAB/Simulink environment. The simulation results illustrated in this paper manifests that the proposed model is promising in performance.

An Intelligent Approach of Fault Classification and Localization of a Power Transmission Line

This study presents fuzzy logic based online fault detection and classification of transmission line using Programmable Automation and Control technology based National Instrument Compact Reconfigurable i/o (CRIO) devices. The LabVIEW software combined with CRIO can perform real time data acquisition of transmission line. When fault occurs in the system current waveforms are distorted due to transients and their pattern changes according to the type of fault in the system. The three phase alternating current, zero sequence and positive sequence current data generated by LabVIEW through CRIO-9067 are processed directly for relaying. The result shows that proposed technique is capable of right tripping action and classification of type of fault at high speed therefore can be employed in practical application.

/pdf/fuzzy-logic-based-on-line-fault-detection-and-classification-1fqve38cw5.pdf

Thingam Dorendrajit

Papers

Fuzzy logic based on-line fault detection and classification in transmission line