A novel gas turbine fault detection and identification strategy based on hybrid dimensionality reduction and uncertain rule-based fuzzy logic

2020 ◽  
Vol 115 ◽  
pp. 103131 ◽  
Author(s):  
Shabnam Yazdani ◽  
Morteza Montazeri-Gh
Keyword(s):  
Fuzzy Logic ◽  
Fault Detection ◽  
Dimensionality Reduction ◽  
Gas Turbine ◽  
Rule Based ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Identification Strategy

scholarly journals PWM-VSI Diagnostic and Reconfiguration Method Based on Fuzzy Logic Approach for SSTPI-Fed IM Drives under IGBT OCFs

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mohamed Ali Zdiri ◽  
Mohsen Ben Ammar ◽  
Fatma Ben Salem ◽  
Hsan Hadj Abdallah
Keyword(s):  
Fuzzy Logic ◽  
Fault Detection ◽  
Diagnostic Method ◽  
Operating Conditions ◽  
Drive System ◽  
Diagnostic Methods ◽  
Detection And Identification ◽  
Efficient Detection ◽  
Fault Detection And Identification ◽  
Fuzzy Logic Technique

Due to the importance of the drive system reliability, several diagnostic methods have been investigated for the SSTPI-IM association in the literature. Based on the normalized currents and the current vector slope, this paper investigates a fuzzy diagnostic method for this association. The fuzzy logic technique is appealed in order to process the diagnosis variable symptoms and the faulty IGBT information. Indeed, the design, inputs, and rules of the fuzzy logic are distinct compared with the other existing diagnostic methods. The proposed fuzzy diagnostic method allows the best efficient detection and identification of the single and phase OCF of the SSTPI-IM association. Accordingly, after the fault detection and identification using this proposed FLC diagnostic method, a reconfiguration step of IGBT OCFs must be applied in order to compensate for these faults and ensure the drive system continuity. This reconfiguration is based on the change of the SSTPI-IM topology to the FSTPI-IM topology by activating or deactivating the used relays. Several simulation results utilizing a direct RFOC controlled SSTPI-IM drive system are investigated, showing the fuzzy diagnostic and reconfiguration methods’ performances, their robustness, and their fast fault detection during distinct operating conditions.

Fault Detection and Identification in an IM270 Gas Turbine Using Measurements for Engine Control

2003 ◽  
Author(s):  
Masahiro Kurosaki ◽  
Tadashi Morioka ◽  
Kosuke Ebina ◽  
Masatoshi Maruyama ◽  
Tomoshige Yasuda ◽  
...  
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Identification Algorithm ◽  
Gradual Change ◽  
Engine Control ◽  
Unknown Parameters ◽  
Engine Model ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Parameter Deviation

A unique fault detection and identification algorithm using measurements for engine control use is presented. The algorithm detects an engine fault and identifies the associated component, using a gas path analysis technique with a detailed nonlinear engine model. The algorithm is intended to detect step-like changes in component performance rather than gradual change of all components. Since simultaneous multiple faults are unlikely, a single component fault is assumed, which reduces the number of unknown parameters to less than two. By setting the number of adjustable parameters to that of the available measurements, the parameters are computed using an engine model. After computing all of the six possible combinations of adjustable parameters, the average magnitude of the parameter deviation vectors is used to detect an engine fault. Component performance deviation (efficiency and flow rate) is represented by a magnitude and a phase. The phase is selected to minimize the error of matrices consisting of normalized adjustable parameter deviation vectors. Then the magnitude is computed by the average magnitude ratio of the vectors. Since the algorithm is simple, it is easily applied to newly developed engines. A fault detection and identification program was specifically developed for IM270 engine, a single shaft gas turbine with 2MW output capacity. By utilizing operational data obtained at a remote monitoring center, the algorithm was able to quantitatively identify the compressor and the turbine performance deviation. Although the algorithm correctly identifies the turbine as the faulty component, there remains some ambiguity. Analysis of linear dependency of the measurement deviation vectors shows that identification capability varies with phase. There are several phases where identification is impossible in the current IM270 sensor system.

scholarly journals Fault Identification in Sub-Station by Using Neuro-Fuzzy Technique

2017 ◽  
Vol 6 (4) ◽  
pp. 343
Author(s):  
Anirudh Yadav ◽  
Vinay Kumar Harit
Keyword(s):  
Fuzzy Logic ◽  
Fault Detection ◽  
Power System ◽  
Fuzzy Controller ◽  
Fault Identification ◽  
Double Line ◽  
Detection And Identification ◽  
Neuro Fuzzy ◽  
Fault Detection And Identification ◽  
Artificial Neural Network Ann

Fault identification and its diagnosis is an important issue in present scenario of power system, as huge amount of electric power is utilized. Random types of faults occur in substation, which leads to irregular and discontinue supply of power from generating to consumer point. Fault detection is an important concept of power system which is to be studied and new method has to develop for fault detection and removal of it. This paper proposed on-line fault detection and identification of fault-type by using Neuro-Fuzzy method in substation. Combination of Artificial Neural Network (ANN) and Fuzzy Logic (FL), results in gaining learning capabilities of fuzzy logic. Variation of current according to fault is used for identification. Fuzzy controller display output condition in form of (0,1).Here, single line-to ground (LG) fault, line-to-line (LL) fault, double line-to ground (LLG)/ LLL fault are considered.

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