M. F. Qureshi

Performance Assessment of Heat Exchanger Using Mamdani Based Adaptive Neuro-Fuzzy Inference System (M-ANFIS) and Dynamic Fuzzy Reliability Modeling

Performance monitoring system for shell and tube heat exchanger is developed using Mamdani Adaptive Neuro-Fuzzy Inference System (M-ANFIS). Experiments are conducted based on full factorial design of experiments to develop a model using the parameters such as temperatures and flow rates. M-ANFIS model for overall heat transfer coefficient of a design /clean heat exchanger system is developed. The developed model is validated and tested by comparing the results with the experimental results. This model is used to assess the performance of heat exchanger with the real/fouled system. The performance degradation is expressed using fouling factor (FF), which is derived from the overall heat transfer coefficient of design system and real system. Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (M-ANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neural networks. M-ANFIS model adopts Mamdani fuzzy inference system which has advantages in consequent part. Experiment results of applying M-ANFIS to evaluate Reliable Performance Assessment of Heat Exchanger show that M-ANFIS, as a new hybrid algorithm in computational intelligence, has great advantages in non-linear modeling, membership functions in consequent parts, scale of training data and amount of adjusted parameters. This paper proposes a new perspective and methodology to model the fouling factor (FF) of the heat exchanger using the fuzzy reliability theory. We propose to use the indicator or performance or substitute variable which is very well understood by the power plant engineer to fuzzify the states of heat exchanger.

Design and development of GUI based model for fault diagnosis of induction motor

Modeling and fault diagnosis of induction motor are main objective of this paper. This paper focused on the motor current signature analysis (MCSA) which utilizes the results of spectral analysis of the stator current. FFT is used for spectrum analysis and frequency of stator current observed for healthy and faulty induction motor. With the help of Matlab Simulink, KQJJ-IMFD model is developed for fault diagnosis of induction motor using FFT. KQJJ-IMFD model is new, simple, and understandable and detects various faults. In this paper various induction motor faults like voltage unbalance, rotor fault, and stator fault are simulated and detected with the help of current spectrum through FFT analysis on the stator current.

Design and development of GUI based model for fault diagnosis of induction motor using interval type-2 fuzzy and genetically tuned interval type-2 fuzzy classifier

In this paper apply interval type-2 fuzzy classifier and genetically tuned interval type-2 fuzzy classifier for diagnostics of induction motor based on spectral analysis of stator current signal. This paper is presented an approach to tune fuzzy based fault diagnosis model of induction motor using Genetic Algorithm (GA). Interval type-2 fuzzy logic controller (IT2FLC) where the fuzzy parameters, e.g. fuzzy membership functions and fuzzy rule bases are tuned by genetic algorithm (GAs) known as genetic interval type-2 fuzzy system. With the help of Matlab Simulink and GUI based KQJJ-IMFD (Kulkarni Qureshi Jha Jogi - Induction Motor Fault Diagnosis) model developed for fault diagnosis of induction motor using FFT and soft computing i.e. interval type-2 fuzzy logic system with genetic algorithm. Motor current signature analysis (MCSA) detection method is used for fault diagnosis of induction motor. All results are simulated and analyzed.

Steady State Stability Analysis of a CSI Fed Synchronous Motor Drive System with Damper Windings Included using ANFIS

The steady state stability analysis (SSSA) is done using small perturbation model. This study presents a detailed steady state stability analysis (SSSA) criterion based on small perturbation model of a adaptive neuro-fuzzy inference system (ANFIS) based current source inverter fed synchronous motor (CSIFSM) drive system taking d-axis and q-axis damper winding into account using ANFIS model. The modeling also clearly shows that even at no load the system satisfies steady state stability analysis (SSSA) criterion. Using the concept of Park‘s transformation the armature current in d-q model has been represented by suitable equations as a function of armature current magnitude in phase model (IS) and the field angle (β). As the system under consideration is basically a current source inverter fed system, IS has been considered as a constant and as a consequence the field angle (β) finally appears as a control variable. The modeling of the system has been done using adaptive neuro fuzzy inference system (ANFIS) by considering the input parameters; inductance (L) and inertia of the rotor (Jrotor) and output as steady state time. The analysis concludes that the absence of damper winding leads to instability of the machine system. This paper describes the fuzzy modeling of CSI fed synchronous motor for studying its steady state stability analysis. The effect of winding parameters on the steady state performance of the synchronous motor is incorporated in this study. Fuzzy models were developed using adaptive neuro-fuzzy inference system (ANFIS). It is observed that system Moment of Inertia (MI) has a significant effect on optimal winding inductance to achieve steady state operation in shortest period of time. The winding leakage inductance should be reduced for achieving steady state operation in shortest time.

Artificial Intelligence Based Fault Diagnosis of Power Transformer-A Probabilistic Neural Network and Interval Type-2 Support Vector Machine Approach

Power transformers has an important role in electrical power transmission and its interruption has financial losses, thus its condition monitoring is essential and performance of this equipment is effective for power system reliability. In this paper, proposed method has advantages of both probabilistic neural network (PNN) and Interval Type-2 Fuzzy Support Vector Machine (IT2FSVM). Firstly, main feature is extracted from primary and secondary three phase currents and search coils differential voltage by wavelet transform and this information is used as probabilistic neural network inputs. AI techniques are applied to establish classification features for faults in the transformers based on the collected gas data. The features are applied as input data to PNN and IT2FSVM combination of classifiers for faults classification. The experimental data from NTPC Korba-India is used to evaluate the performance of proposed method. The results of the various DGA methods are classified using AI techniques. In comparison to the results obtained from the AI techniques, the PNN plus IT2SVM has been shown to possess the most excellent performance in identifying the transformer fault type. The test results indicate that the PNN plus IT2SVM approach can significantly improve the diagnosis accuracies for power transformer fault classification. In addition, the study aims to study the joint effect of PNN and IT2SVM on the classification performance when used together.

Designing Power System Stabilizer for System Damping for Transient Disturbances Using Grey ANFIS Technique

This paper describes a design procedure for a Grey ANFIS based power system stabilizer (GrANFISPSS) and investigates their robustness for a multi-machine power system. Speed deviation of a machine and its derivative are chosen as the input signals to the GrANFIS-PSS. A four-machine and a two-area power system is used as the case study. Computer simulations for the test system subjected to transient disturbances i.e. a three phase fault, were carried out and the results showed that the proposed controller is able to prove its effectiveness and improve the system damping when compared to a conventional lead-lag based power system stabilizer controller. The simulation result shows that the GrANFIS-PSS can be designed to achieve good performance merely using the combination of Grey prediction and Adaptive Neuro-Fuzzy Inference System (ANFIS). GrANFIS-PSS is designed to damp out the low frequency local and inter-area oscillations of the Multi-machine power system. By applying this GrANFIS-PSS to the power system the damping of inter-area modes of oscillations in a multi-machine power system is handled properly. The effectiveness of the proposed GrANFIS-PSS is demonstrated on two area four machine power system (Kundur system), which has provided a comprehensive evaluation of the learning control performance. Finally, several fault and load disturbance simulation results are presented to stress the effectiveness of the proposed GrANFIS-PSS in a multimachine power system and show that the proposed intelligent controls improve the dynamic performance of the GrANFIS-PSS and the associated power network