High performance of Maximum Power Point Tracking Using Ant Colony algorithm in wind turbine

Mokhtari, Y.; Rekioua, Djamila

doi:10.1016/j.renene.2018.03.049

Cited by 87 publications

(36 citation statements)

References 11 publications

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“…Another work [10] focus on the studies devoted to the increased interest in wind energy as a source of electric energy with minimal environmental impact, including the wind turbines, and the development of aerodynamic structures. The main goal of this work is to develop and improve the management strategies to achieve maximum strength using these methods.…”

Section: Study Of Related Workmentioning

confidence: 99%

Application of Ant Algorithm for Software Optimization

Mahmudova¹

2020

Review of Information Engineering and Applications

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Article HistoryKeywords Software Optimization Ant algorithm Soft computing Technology Method. Different technologies, methods and algorithms are used when developing high-quality software systems. There are various ways to create optimal software. One of them is an ant algorithm. The ant algorithm can be attributed to the field of biomimetic. Ant algorithm is one of the most effective polynomial algorithms for finding approximate solutions, and it also solves similar route search problems in graphs. Various methods, including the ant algorithm, are used to improve software efficiency and optimize it. The essence of the concept is to analyze, use, and meta-heuristically optimize the behavior of ants in search of paths to the food source of the colony. This article analyzes the studies in this area, and explains the idea of the algorithm used, compares different ant systems, shows program code operators as ants, and applies the ant algorithm to them. As a result of applying the algorithm, the shortest path to some operators (cycle, condition) contained in the program code is found. The experiments perform good results.Contribution/Originality: The ant algorithm, various methods including the ant algorithm was studied.Analyzes the studies in this area, and explains the idea of the algorithm used, compares different ant systems, shows program code operators as ants, and applies the ant algorithm to them. Figure-1. Software efficiency characteristics. Note that biomimetic is an imitation model of the systems and elements in the nature to solve complex human problems. Living organisms have well-adapted structures and materials for natural selection and have developed over many years. The study of biomimetic technologies and their application in various fields can play an important role in the successful development of the economy.Ant algorithm (Ant Colony Optimization, ACO) is one of the most effective polynomial algorithms for finding approximate solutions, and it also solves similar route search problems in graphs. The essence of the concept is to 13 In this step, the right transition from one operator to others has to be found. As seen from the values, the probability value varies within 0, 1. P11 -[0, 0.3] P12 -[0, 0.2] P13 -[0, 0.1] P14 -[0, 0.1] P15 -[0, 0.1] P16 -[0, 0.1] P17 -[0, 0.3] Step 2. All transitions of the 2nd node are assessed. P23 -[0, 0.2] P24 -[0, 0.3] P25 -[0, 0.2] P26 -[0, 0.2] P27 -[0, 0.2] P28 -[0, 0.3]As seen from these values, their probability value also varies within 0.1.Step 3. All transitions of the 3rd node is assessed. P34 -[0, 0.5] P35 -[0, 0.2] P38 -[0, 0.1] P37 -[0, 0.2] P36 -[0, 0.1] Step 4. All transitions of the 4th node are assessed. P45 -[0, 0,5] P48 -[0, 0.3] P47 -[0, 0.2] P46 -[0, 0.2] Review of Information Engineering and Applications, 2020, 9(1): 6-17 15 Step 5. All transitions of the 5th node is assessed. P55 -[0, 0,7] P58 -[0, 0.2] P57 -[0, 0.3] Step 6. All transitions of the 6nd node are assessed. P67 -[0, 0,4] P68 -[0, 0.7]Step 7. The 7th node will have one value.P78 -[0...

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Section: Study Of Related Workmentioning

confidence: 99%

Application of Ant Algorithm for Software Optimization

Mahmudova¹

2020

Review of Information Engineering and Applications

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“…Using ACO to optimize proportional-integral-derivative (PID) controller parameters, for wind turbine tower disturbances has been studied in [53]. In [54] an ACO is employed to determine the optimal PI controller parameters for speed control to get high performance of maximum power point tracking. A fully tuned RBF neural network controller based on ACO algorithm and Lyapunov functions has been used for ultrasound hyperthermia cancer tumor therapy in [55].…”

Section: Introductionmentioning

confidence: 99%

Optimization of ANFIS controllers using improved ant colony to control an UAV trajectory tracking task

2020

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Development of unmanned aerial vehicles (UAVs) has become the most important research areas in the field of autonomous aeronautical control. This paper proposes a robust and intelligent controller based on adaptive-network-based fuzzy inference system (ANFIS) and improved ant colony optimization (IACO) to govern the behavior of a three degree of freedom quadrotor UAV. The quadrotor was chosen due to its simple mechanical structure; nevertheless, these types of aircraft are highly nonlinear. Intelligent control such as fuzzy logic is a suitable choice for controlling nonlinear systems. The ANFIS controller is used to reproduce the desired trajectory of the quadrotor in 2D Vertical plane and the IACO algorithm aims is to facilitate convergence to the ANFIS's optimal parameters in order to reduce learning errors and improve the quality of the controller. To evaluate the performance of the proposed IACO tuned ANFIS controller, a comparison between the proposed ANFIS-IACO controller and other controller's performance such us ANFIS only and proportional-integral-derivative controllers is illustrated using the same system. As expected, the hybrid ANFIS-IACO controller gives very satisfactory results than the others methods already developed in the same study.

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“…Adaptive neuro-fuzzy (ANFIS) control in [12], [13] are also in trend, however, besides of requiring a large volume of data like variation of rotor angular speed a, TSR values concerning variation in wind speed to train Artificial neural network (ANN), it also suffers from the bounded output limitation of fuzzy logic. Artificial Bee Colony (ABC) based MPPT [14] is one of the cutting-edge advancements in this sector, which is proven to an effective and most accurate, yet complex to realize.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Fuzzy-PID based MPPT Enhancement of Grid-Connected DFIG Wind Energy Eystem with TLBO Optimization

Bhati¹,

Gaur²

2020

IJERT

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This research article emphasizes the enhancement of Maximum Power Point Tracking (MPPT) action using a Fuzzy supervised PID (f-PID) controller for a grid-connected 2MW Doubly fed Induction Generator (DFIG) connected to a passive filter and grid via Back-to-Back PWM Converter topology. This article also proposes the implementation of Teaching Learningbased optimization (TLBO) technique for tuning gain parameters of the proposed as well as extant controllers used in the MPPT algorithm. The primary objective is to improvise the conventional Rotor side current-based field-oriented control (MSC) of the understudy system, by employing a modified Tip-Speed Ratio (TSR) based MPPT algorithm with wind speed estimator. This algorithm ensures extraction and regulation of maximum active power while minimizing stator reactive power drawn under a step-wise variable wind speed profile. It is realized by employing a PID controller to restrict rotor angular speed variation around its optimal value, estimated by a fuzzy logic system. After the completion of a comprehensive simulation analysis using MATLAB/Simulink R2017a, the performance of the proposed controller in steady as well as transient states is being fairly found to be superior as compared to that of PID and Non-linear PID (NPID) based controllers in terms of settling time, MPP steady-state error and tracking efficiency.

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High performance of Maximum Power Point Tracking Using Ant Colony algorithm in wind turbine

Cited by 87 publications

References 11 publications

Application of Ant Algorithm for Software Optimization

Application of Ant Algorithm for Software Optimization

Optimization of ANFIS controllers using improved ant colony to control an UAV trajectory tracking task

Hybrid Fuzzy-PID based MPPT Enhancement of Grid-Connected DFIG Wind Energy Eystem with TLBO Optimization

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