Analysis of unsymmetrical faults based on artificial neural network using 11 kV distribution network of University of Lagos as case study (original) (raw)
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Electric power Transmission lines are characterized by very lengthy transmission lines and thus are more exposed to the environment. Consequently, transmission lines are more prone to faults, which hinder the continuity of electric power supplied, increases the loss of electric power generated and loss of economy. Quick detection and classification of a fault hastens its Clearance and reduces system downtime thus, improving the security and efficiency of the network. Thus, this paper focuses on developing a single artificial neural network to detect and classify a fault on Nigeria 33-kV electric power transmission lines. This study employs feedforward artificial neural networks with backpropagation algorithm in developing the fault detector- classifier. The transmission lines were modeled using SimPowerSystems toolbox in Simulink and simulation is done in MATLAB environment. The instantaneous voltages and currents values are extracted and used to train the fault detector-classifier....
Journal University of Kerbala, 2016
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Classification of Faults in a Test Power System using Artificial Neural Network
IEEE, 2019
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Power System Fault Prediction Using Artificial Neural Networks
The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port Π circuit) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher. 1.0 Introduction Adequate fault detection is vitally important to ensure reliable power system operation. Many system fault studies are concerned mainly with a 'what if' scenario i.e. on considering what would happen after a fault occurred, identifying its location and accessing the nature and degree of damage. To date, few studies have been made concerning early fault detection (EFD) techniques which facilitate the prediction of a major fault before it actually occurs. In a typical power system, the states (voltages and currents) of most bus bar nodes are monitored and gradual changes are analysed. However, because of the complexity of recorded data, faults at an early stage cannot be easily recognised. These faults can be disguised by the complexity of power system operational data [1-6]. The aim of the EFD method is to detect and alert the operator before a catastrophic fault actually occurs. In other words, this is an early warning fault prevention method. ANNs are employed to monitor the states of some important components in power networks, such as switchgear and transformers. The ANN is trained to detect minor changes to the internal parameters modelled as power system equivalent circuits [6]. The small variations of voltages and currents resulting from internal parameters changes, at sending end and receiving ends of the power system can be derived under simulation and then presented to the ANN for training. As some of the internal parameters of the power system do not physically exist, they cannot be measured directly by simple measurement methods. Thus, the application of an intelligent technique, such as an ANN method , is obviously required. The principle of the EFD can be applied to various sections of a power system. A typical extremely simplified example will now be given. Transmission lines in power systems carry high currents and voltages. Small changes in state, caused by partial faults, on transmission lines are often too insignificant to trigger the conventional protection systems. However, these small scale changes may develop and eventually lead to major faults. For example, in winter, snow may gradually accumulate on transmission lines. The impedance of transmission lines could change accordingly. The circuit breaker would trip when the snow formed a short-circuit and this could " blackout " a large area. With early warning fault monitoring, the interruption of power supply could possibly be prevented. The change of impedance of the transmission line provides vital information which can be analysed by EFD technique to provide an early detection capability. This technique could alert the operator before the main fault actually occurs enabling, in some situations, appropriate action to be taken, e.g. providing power supply from another circuit and switching out the endangered line. 2.0 Artificial Neural Network An ANN may be considered as a greatly simplified model of the human brain which can be used to perform a particular task or function of interest. The network is usually implemented using electronic components or simulated in software on a digital computer. The massively parallel distributed structure and the ability to
International Journal of Research in Engineering and Science (IJRES), 2019
The epileptic electricity power supply in Woji Village 11KV distribution network in Port Harcourt, Rivers State, Nigeria is never-ending following inadequate power distribution, human error fault, malfunctioning of power equipment and poor distribution infrastructure upgrade, etc. WojiVillage feeder is connected to Woji Injection substation. The Capacity, Voltage Ratio, Active Power, Reactive Power, Frequency, Power Factor, Complex Power, Transformer Percentage Loading, and Current Rating on each Transformerwere determined. The pre-upgrade and post-upgrade of the system was simulated using Electrical Transient analyser program (ETAP) software.Correction factor of 95% was usedfor the post-upgrade of the highly over loaded transformers in networksince it is verygood and economical for industrial purposes.Furthermore, 10Mvarcapacitor bank compensation was recommended for Woji Village 11KV distribution networkfor improved performance for the affected buses ( B-9, B-14, B-20, B-45) with optimal placement capacity of B-9=2100KVAR, B-14=2500KVAR, B-20=2200KVAR, B-45=2200KVAR respectively. In conclusion, Port Harcourt Electricity Distribution (PHED) should do proper upgrading of their facility in WojiVillage feeders or develop another injection substation for Woji, Port Harcourt, Nigeria, for proper distribution of electricity to its customers.An effective monitoring of the network through load flow analysis should be activated to ensure that the connected load on the network is always equal to the capacity of the installed distribution transformer, thus conforming to IEE regulation of deviation not exceeding ±10% of the nominal voltage.
International Journal of Frontline Research in Engineering and Technology
Increase in size of electrical power network usually results in a rise in fault level and consequently in huge economic losses to energy providers and consumers in the distribution systems. Therefore, it is very important to be proactive in dealing with faults on distribution feeder systems not only to reduce financial havoc, but to save lives and improve the quality of life of the people. A case study of Ayede-Eruwa/Lanlate Oyo State, Nigeria 33kV line is considered. An Artificial Neural Network based Time Series (ANN-TS) fault predictive model is developed for forecasting of faults on the above chosen electrical power network. Daily forced outage readings of the substation’s feeders for three years were collected and modeled using a three-layer feed-forward network ANN-TS. The results in the frequency of fault prediction show that there is an overlap between the observed and predicted values. The annual Mean Average Percentage Error (MAPE) varies between 0.004% and 25%, and the fe...
Fault Detection in Distribution Lines Using Artificial Neural Networks
The fault detection in distribution lines of a power system is a very important aspect to maintain a healthy power system. This paper presents an investigation carried out to predict and detect faults in distribution lines of a power system. In this research, artificial neural network and k-nearest neighbors methods were applied to detect and predict faults in distribution lines of a power system. The performance of these two techniques in fault detection is presented in this paper.
American Journal of Electrical Power and Energy Systems, 2014
The aim of this research work is to carry out fault analysis of 11KV distribution power system. Electric power is an essential facilitator for sustainable development of the modern nation state. While Nigeria is reported to suffer from severe shortages of electric power the condition of some of its newer constitutional units are unknown. In this work, electric power infrastructure and energy availability is studied for Ado-Ekiti, the principal economic and political hub of Ekiti State. During the study, the condition of all relevant equipment for power distribution at the 11 kV level was assessed. Power availability was also considered by collecting necessary data that had to do with energy supplied, faults and other outages. It was discovered that the distribution lines were in a rather poor state with as many as 25% of the poles not meeting a condition of "goodness", 33% of cross-arms being broken or unsatisfactory, about 10% of the insulators defective and almost 40% of the span not complying with standards. Hence this work presents a research on fault analysis of Ado Ekiti distribution power system.