Abstract: Power transformers are the most crucial part of power electrical system, distribution and transmission grid. This part is maintained using predictive or condition-based maintenance approach. The diagnosis of power transformer condition is performed based on Dissolved Gas Analysis (DGA). There are five main methods utilized for analyzing these gases. These methods are International Electrotechnical Commission (IEC) gas ratio, Key Gas, Roger gas ratio, Doernenburg, and Duval Triangle. Moreover, due to the importance of the transformers, there is a need for an accurate technique to diagnose and hence predict the transformer condition. The main objective of this technique is to avoid the transformer faults and hence to maintain the power electrical system, distribution and transmission grid. In this paper, the DGA was utilized based on the data collected from the transformer records available in the General Electricity Company of Libya (GECOL) which is located in Benghazi-Libya. The Fuzzy Logic (FL) technique was implemented as a diagnostic approach based on IEC gas ratio method. The FL technique gave better results and approved to be used as an accurate prediction technique for power transformer faults. Also, this technique is approved to be a quite interesting for the readers and the concern researchers in the area of FL mathematics and power transformer.
Abstract: The pursuit of the MPPT has led to the development of many kinds of controllers, one of which is the Fuzzy Logic controller, which has proven its worth. To further tune this controller this paper will discuss and analyze the use of Genetic Algorithms to tune the Fuzzy Logic Controller. It will provide an introduction to both systems, and test their compatibility and performance.
Abstract: Self-compacting concrete (SCC) developed in Japan
in the late 80s has enabled the construction industry to reduce
demand on the resources, improve the work condition and also
reduce the impact of environment by elimination of the need for
compaction. Fuzzy logic (FL) approaches has recently been used to
model some of the human activities in many areas of civil
engineering applications. Especially from these systems in the model
experimental studies, very good results have been obtained. In the
present study, a model for predicting compressive strength of SCC
containing various proportions of fly ash, as partial replacement of
cement has been developed by using Fuzzy Inference System (FIS).
For the purpose of building this model, a database of experimental
data were gathered from the literature and used for training and
testing the model. The used data as the inputs of fuzzy logic models
are arranged in a format of five parameters that cover the total binder
content, fly ash replacement percentage, water content,
superplasticizer and age of specimens. The training and testing results
in the fuzzy logic model have shown a strong potential for predicting
the compressive strength of SCC containing fly ash in the considered
range.
Abstract: Conventional controller’s usually required a prior knowledge of mathematical modelling of the process. The inaccuracy of mathematical modelling degrades the performance of the process, especially for non-linear and complex control problem. The process used is Water-Bath system, which is most widely used and nonlinear to some extent. For Water-Bath system, it is necessary to attain desired temperature within a specified period of time to avoid the overshoot and absolute error, with better temperature tracking capability, else the process is disturbed.
To overcome above difficulties intelligent controllers, Fuzzy Logic (FL) and Adaptive Neuro-Fuzzy Inference System (ANFIS), are proposed in this paper. The Fuzzy controller is designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. To design ANFIS, Fuzzy-Inference-System is combined with learning capability of Neural-Network.
It is analyzed that ANFIS is best suitable for adaptive temperature control of above system. As compared to PID and FLC, ANFIS produces a stable control signal. It has much better temperature tracking capability with almost zero overshoot and minimum absolute error.
Abstract: Power system stabilizers (PSS) must be capable of providing appropriate stabilization signals over a broad range of
operating conditions and disturbance. Traditional PSS rely on robust
linear design method in an attempt to cover a wider range of operating
condition. Expert or rule-based controllers have also been proposed.
Recently fuzzy logic (FL) as a novel robust control
design method has shown promising results. The emphasis in fuzzy
control design center is around uncertainties in the system parameters
& operating conditions. In this paper a novel Robust Fuzzy Logic Power
System Stabilizer (RFLPSS) design is proposed The RFLPSS
basically utilizes only one measurable Δω signal as input
(generator shaft speed).
The speed signal is discretized resulting in three inputs to the
RFLPSS. There are six rules for the fuzzification and two rules for
defuzzification. To provide robustness, additional signal namely,
speed are used as inputs to RFLPSS enabling appropriate gain
adjustments for the three RFLPSS inputs. Simulation studies
show the superior performance of the RFLPSS compared
with an optimally designed conventional PSS and discrete mode FLPSS.
Abstract: This paper presents a speed fuzzy sliding mode
controller for a vector controlled induction machine (IM) fed by a
voltage source inverter (PWM).
The sliding mode based fuzzy control method is developed to
achieve fast response, a best disturbance rejection and to maintain a
good decoupling.
The problem with sliding mode control is that there is high
frequency switching around the sliding mode surface. The FSMC is
the combination of the robustness of Sliding Mode Control (SMC)
and the smoothness of Fuzzy Logic (FL). To reduce the torque
fluctuations (chattering), the sign function used in the conventional
SMC is substituted with a fuzzy logic algorithm.
The proposed algorithm was simulated by Matlab/Simulink
software and simulation results show that the performance of the
control scheme is robust and the chattering problem is solved.
Abstract: Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.
Abstract: In this paper, we present optimal control for
movement and trajectory planning for four degrees-of-freedom robot
using Fuzzy Logic (FL) and Genetic Algorithms (GAs). We have
evaluated using Fuzzy Logic (FL) and Genetic Algorithms (GAs)
for four degree-of-freedom (4 DOF) robotics arm, Uncertainties like;
Movement, Friction and Settling Time in robotic arm movement
have been compensated using Fuzzy logic and Genetic Algorithms.
The development of a fuzzy genetic optimization algorithm is
presented and discussed. The result are compared only GA and
Fuzzy GA. This paper describes genetic algorithms, which is
designed to optimize robot movement and trajectory. Though the
model represents is a general model for redundant structures and
could represent any n-link structures. The result is a complete
trajectory planning with Fuzzy logic and Genetic algorithms
demonstrating the flexibility of this technique of artificial
intelligence.
Abstract: In this research paper we have presented control
architecture for robotic arm movement and trajectory planning using
Fuzzy Logic (FL) and Genetic Algorithms (GAs). This architecture is
used to compensate the uncertainties like; movement, friction and
settling time in robotic arm movement. The genetic algorithms and
fuzzy logic is used to meet the objective of optimal control
movement of robotic arm. This proposed technique represents a
general model for redundant structures and may extend to other
structures. Results show optimal angular movement of joints as result
of evolutionary process. This technique has edge over the other
techniques as minimum mathematics complexity used.