scholarly journals Electrical Load Prediction Using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning Approach

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3591
Author(s):  
Mojtaba Ahmadieh Khanesar ◽  
Jingyi Lu ◽  
Thomas Smith ◽  
David Branson
Keyword(s):  
State Of The Art ◽  
Renewable Energy Sources ◽  
Superior Performance ◽  
Electrical Load ◽  
Load Prediction ◽  
Prediction Algorithms ◽  
Simulation Results ◽  
Gravitational Search ◽  
Interval Type

Establishing accurate electrical load prediction is vital for pricing and power system management. However, the unpredictable behavior of private and industrial users results in uncertainty in these power systems. Furthermore, the utilization of renewable energy sources, which are often variable in their production rates, also increases the complexity making predictions even more difficult. In this paper an interval type-2 intuitionist fuzzy logic system whose parameters are trained in a hybrid fashion using gravitational search algorithms with the ridge least square algorithm is presented for short-term prediction of electrical loading. Simulation results are provided to compare the performance of the proposed approach with that of state-of-the-art electrical load prediction algorithms for Poland, and five regions of Australia. The simulation results demonstrate the superior performance of the proposed approach over seven different current state-of-the-art prediction algorithms in the literature, namely: SVR, ANN, ELM, EEMD-ELM-GOA, EEMD-ELM-DA, EEMD-ELM-PSO and EEMD-ELM-GWO.

scholarly journals Design Interval Type-2 Fuzzy Like (PID) Controller for Trajectory Tracking of Mobile Robot

2019 ◽  
pp. 1-15
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Pid Controller ◽  
Kinematic Model ◽  
Superior Performance ◽  
Fuzzy Like ◽  
Interval Type

One of the major problems in the field of mobile robots is the trajectory tracking problem. There are a big number of investigations for different control strategies that have been used to control the motion of the mobile robot when the nonlinear kinematic model of mobile robots was considered. The trajectory tracking control of autonomous wheeled mobile robot in a changing unstructured environment needs to take into account different types of uncertainties. Type-1 fuzzy logic sets present limitations in handling those uncertainties while type-2 fuzzy logic sets can manage these uncertainties to give a superior performance. This paper focuses on the design of interval type-2 fuzzy like proportional-integral-derivative (PID) controller for the kinematic model of mobile robot. The firefly optimization algorithm has been used to find the best values of controller’s parameters. The aim of this controller is trying to force the mobile robot tracking a pre-defined continuous path with minimum tracking error. The Matlab simulation results demonstrate the good performance and robustness of this controller. These were confirmed by the obtained values of the position tracking errors and a very smooth velocity, especially with regards to the presence of external disturbance or change in the initial position of mobile robot. Finally, in comparison with other proposed controllers, the results of nonlinear IT2FLC PID controller outperform the nonlinear PID neural controller in minimizing the MSE for all control variables and in the robustness measure.

scholarly journals Wall-Following Behavior for a Disinfection Robot Using Type 1 and Type 2 Fuzzy Logic Systems

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4445
Author(s):  
M. A. Viraj J. Muthugala ◽  
S. M. Bhagya P. Samarakoon ◽  
Madan Mohan Rayguru ◽  
Balakrishnan Ramalingam ◽  
Mohan Rajesh Elara
Keyword(s):  
Fuzzy Logic ◽  
Infectious Diseases ◽  
Fuzzy Logic System ◽  
Superior Performance ◽  
Fuzzy Logic Systems ◽  
Interval Type ◽  
Wall Following ◽  
Logic System

Infectious diseases are caused by pathogenic microorganisms, whose transmission can lead to global pandemics like COVID-19. Contact with contaminated surfaces or objects is one of the major channels of spreading infectious diseases among the community. Therefore, the typical contaminable surfaces, such as walls and handrails, should often be cleaned using disinfectants. Nevertheless, safety and efficiency are the major concerns of the utilization of human labor in this process. Thereby, attention has drifted toward developing robotic solutions for the disinfection of contaminable surfaces. A robot intended for disinfecting walls should be capable of following the wall concerned, while maintaining a given distance, to be effective. The ability to operate in an unknown environment while coping with uncertainties is crucial for a wall disinfection robot intended for deployment in public spaces. Therefore, this paper contributes to the state-of-the-art by proposing a novel method of establishing the wall-following behavior for a wall disinfection robot using fuzzy logic. A non-singleton Type 1 Fuzzy Logic System (T1-FLS) and a non-singleton Interval Type 2 Fuzzy Logic System (IT2-FLS) are developed in this regard. The wall-following behavior of the two fuzzy systems was evaluated through simulations by considering heterogeneous wall arrangements. The simulation results validate the real-world applicability of the proposed FLSs for establishing the wall-following behavior for a wall disinfection robot. Furthermore, the statistical outcomes show that the IT2-FLS has significantly superior performance than the T1-FLS in this application.

scholarly journals Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms

2016 ◽  
Vol 6 (2) ◽  
pp. 81-101 ◽  
Author(s):  
Khalid Almohammadi ◽  
Hani Hagras ◽  
Daniyal Alghazzawi ◽  
Ghadah Aldabbagh
Keyword(s):  
Fuzzy Logic ◽  
Learning Environment ◽  
Adaptive Learning ◽  
Learning System ◽  
Superior Performance ◽  
Teaching Models ◽  
Learning Platform ◽  
E Learning ◽  
Interval Type

Abstract Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.

scholarly journals Electrical Daily Load Forecasting In Ramadhan Using Type-2 Fuzzy Logic In Sulselrabar System

2018 ◽  
pp. 146
Author(s):  
Marhatang Marhatang ◽  
Muhammad Ruswandi Djalal ◽  
Herman Nawir ◽  
Sonong Sonong
Keyword(s):  
Fuzzy Logic ◽  
Power Systems ◽  
Peak Load ◽  
Load Forecasting ◽  
Small Error ◽  
Electrical Load ◽  
Electricity Load ◽  
Interval Type

 This study discusses the daily electricity load forecasting 24 hours on 150 kV electric power systems sulselrabar. Forecasting electrical load requires the accuracy of the results with a small error. Peak load forecasting methods used to use smart methods Interval Type-1 Fuzzy Logic (IT1FL) and Interval Type-2 Fuzzy Logic (IT2FL) to predict the needs of the electrical load 1 Ramadan 2016. As input data, it was used load data from 2012 through 2016 for the same day each 1st of Ramadan each year, and as comparative data, it was used actual load data 1, 2016. For the Ramadan input variable, it was used two of the data Variation Load Difference (VLD Max) 2015 as an input variable X, VLD Max 2016 as an input variable Y. From the simulation results obtained highly accurate results where each method produces a very small error, where for methods of using IT1FL of 1.607778264% while using IT2FL by, 1.344510913%.

scholarly journals Stabilization of inter-area oscillations in two-area test system via centralized interval type-2 fuzzy-based dynamic brake control

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohamed Fayez ◽  
Mohamed Mandor ◽  
Mohamed El-Hadidy ◽  
Fahmy Bendary
Keyword(s):  
Power Systems ◽  
Case Studies ◽  
Test System ◽  
Interconnected Power Systems ◽  
Power Oscillations ◽  
Simulation Results ◽  
Interval Type ◽  
Degrees Of Severity ◽  
Weak Damping

AbstractInter-area oscillations are, by far, the most detrimental oscillation category to the integrity of synchronously interconnected power systems. Inter-area oscillations are characterized by the inherent weak damping. The inherent poor damping associated with the inter-area oscillations leaves open wide probabilities for irrevocable widespread blackouts with the consequent eventual devastating outcomes measured in terms of the huge economic casualties and the possible human fatalities. The main purpose of this work is to mitigate inter-area power oscillations. This article explores the effectiveness of dual thyristor controlled braking resistor units with Interval Type-2 fuzzy-based centralized architecture for neutralizing the jeopardy of inter-area power oscillations in Kundur’s two-area test system using MATLAB™/Simulink environment. The effectiveness of the proposed scheme is examined by considering four case studies with different degrees of severity. The simulation results show that the proposed scheme is simple yet effective in treating the inter-area oscillations appropriately under the considered case studies.

scholarly journals Adaptive Non-singleton Type-2 Fuzzy Logic Systems: A Way Forward for Handling Numerical Uncertainties in Real World Applications

2011 ◽  
Vol 6 (3) ◽  
pp. 503 ◽  
Author(s):  
Nazanin Sahab ◽  
Hani Hagras
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Real World ◽  
Superior Performance ◽  
Great Success ◽  
Real World Applications ◽  
Interval Type ◽  
Linguistic Labels

Real world environments are characterized by high levels of linguistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applications such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first generation of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents’ linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties.

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