A Multi Agent System Design for Power Distribution Restoration Using Neural Networks

The state of the art of power distribution systems is to demand a more accurate response. It also provides more reliability for fault location and restoration respectively. A multi-agent system design for power distribution has been developed using the change of current methodology to detect and locate any type of faults. Employing the artificial intelligence for restoration process is the most important contribution to this study. Since feed-forward neural networks are weight training based back propagation concept, radial basis neural networks showed more efficiency by using the minimum error method to optimize the decision. A Probabilistic radial basis Neural Network (PNN) is designated at each feeder agent to implement the reconfiguration by analyzing the impedance and current values for each zone. The appropriate decision for the optimal reconfiguration case is a vector of activation signals associated with each switch to restore the power to the un-faulted zones of distribution feeder.;This study examines the role of Universal Asynchronous Receiver Transmitter (UART) buffer circuits in the laboratory experiment demonstration of the multi-agent system design. The main approach of a self-healing concept is the protection system. A recloser has been developed and improved for more sensitivity and faster response to detecting a fault where ever it occurs and lead the process of isolating and re-configuration. An electronic buffer circuit using digital microcontroller has been associated with the recloser and agents switches in order to offer a satisfying feedback for the proposed approach. Simulation studies, using MATLAB SimPowerSystems and, Neural Network toolboxes, for the proposed power distribution system showed improved results for fault location and restoration using Radbas neural networks. Hardware implementation with high accurate software data scoping of results has been employed to show the difference in time response using Universal Asynchronous Receiver Transmitter buffers at each switching relay in the design

Fault Location Estimator Design for Power Distribution System Using Artificial Neural Network

Fault location in distribution system is critical issue to increase the availability of power supply by reducing the time of interruption for maintenance in electric utility companies. Fault location estimator for power distribution system using artificial neural network is developed for line to ground, line to line, line to line to ground and three phases to ground faults in distribution system. To develop this estimator is one of rural radial power distribution feeder in Ethiopia, south west reign, Aba substation Tarcha line feeder is used as a test feeder. This feeder is simulated using ETAP software to generate data for different fault condition, with different fault resistance and loading conditions, which is the fault phase voltage and current. The generated data is preprocessed and put as an input for neural network to be trained. MATLAB R2016a neural network toolbox to train ANN and programming toolbox is used to develop graphic user interface for fault estimator. The feed forward multi-layer network topologies of neural network with improved back propagation, Liebenberg Marquardt learning algorithm is used to train the network. After the network is trained the mean square error performance, regression plot and error histogram analysis was made and found to have an excellent performance with regression coefficient 0.99929 , validation performance of 0.000102 and error histogram range 0.015 to 0.019. In this thesis for practical implementation the fault records at the test feeder is handled by intelligent electronic device (IED) installed at the substation feeders. The fault record of IED can be read by PCM600 tool using laptop or manually using IEDs human machine interface, this fault recorded data feed to the graphic user interface to estimate the fault location as well as the fault type. Finally it is found that artificial neural networks are one of the alternate options in fault estimator design for distribution system where sufficient distribution network data are available with narrow fault location distance range from the substation. This has benefits in assisting for maintenance plan, saving efforts in fault location finding and economic benefits by reducing interruption time. Keywords: Power distribution, artificial intelligence, neural network, feed forward network DOI: 10.7176/JETP/12-5-01 Publication date: November 30th 202

Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples