Fault detection and classification in wind turbine by using artificial neural network

Abstract

Wind turbine is one of the present renewable energy sources that has become the most popular. The operational and maintenance cost is continuously increasing, especially for wind generator. Early fault detection is very important to optimise the operational and maintenance cost. The goal of this project is to study fault detection and classification for a wind turbine (WT) by using artificial neural network (ANN). In this project, a single phase fault was placed at 9 MW doubly-fed induction generator (DFIG) WT in MATLAB Simulink. The WT was tested under different conditions, i.e., normal condition, fault at Phase A, Phase B and Phase C. The simulation results were used as inputs in the ANN model for training. Then, a new set of data was taken under different conditions as inputs for ANN fault classifier. The target outputs of ANN fault classifier were set as ‘0’ or ‘1’, based on the fault condition. Results obtained showed that the ANN fault classifier outputs had followed the target outputs. In conclusion, the WT fault detection and classification method by using ANN were successfully developed.

Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor

“…Faults can also occur in the induction motor due to damage in stator windings, bearings, squirrel cage rotor broken bars, insulation failure [21]- [23]. The incipient faults do not cause the motor to stop unlike short circuit faults.…”

Fault isolation technique for decentralized survivable communication network systems via regions and paths

“…Fault isolation is very important, since a minor fault might generate costly damages to whole network due to the spread of the fault [6]. In computer network, varieties of techniques are available to agree on accurate location of faults [7]. The most generally used methods are due to • Model traversing techniques • Alarm co-relation • Artificial intelligence techniques • Graph theoretical techniques [4].The requisite for security, reliability and availability is rising drastically with growing complexity of communication networks.…”

“…In the example 2 root will receive alarm from {24, 30, 17, 7, 28, 22, 23, 15} from the paths P1, P2, P3, P5, P8, P10, P11, P12. So the regions FF= { 2, 6, 10,16,24,25, 30,17,3,7,5,12,20, 28,4,8,13,22,14,23,9, 15} are fault free.Step 9: Now we need to isolate or identify the faulty regions from the remaining paths. We have not received alarm from leaf nodes { 26, 33, 32, 34 }.…”

DeepOSN: Bringing deep learning as malicious detection scheme in online social network

“…The algorithm focuses on how the neurons process information and conduct computations [6]. Many aspects have implemented AI algorithms to address various problems [7][8][9]. In the neural network, the network elements usually called neurons, which are grouped in different layers.…”