An enhanced whale optimized fractional order piλ controller based power quality improvement for sensorless brushless dc motor under dynamic operating conditions

2021 ◽  
pp. 1-20
Author(s):  
R Prakash ◽  
K Ayyar
Keyword(s):  
Power Quality ◽  
Fractional Order ◽  
Optimization Algorithm ◽  
Speed Control ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
The Other ◽  
Brushless Dc ◽  
Whale Optimization ◽  
Dynamic Operating

This paper presents an Enhanced Whale Optimization Algorithm (EWO) approach for tuning to perfection of Fractional Order Proportional Integral and integral order Controller (FOPI λ ) is used to sensorless speed control of permanent magnet Brushless DC (PMBLDC) motor under the operating dynamic condition such as (i) speed change by set speed command signal (ii) varying load conditions, (iii) integrated conditions and (iv) controller parameters uncertainty. On the other hand, it deals with a reduced THD (Total Harmonic Distortion) under dynamic operating conditions to improve the power quality for the above control system. Here present are three optimization techniques, namely (i) Enhanced Whale Optimization (EWO), (ii) Invasive Weed Optimization (IWO), and (iii) Social Spider Optimization (SSO) for fine-tuning of the FOPI λ controller parameters with reduction of THD. The proposed optimization algorithm optimized FOPI λ controller are compared under various BLDC motor operating conditions. Based on the results of MATLAB/Simulink models, the proposed algorithms are evaluated. Here, both the simulation and the results of the experiments are validated for the proposed controller technique. It demonstrates that the effectiveness of the proposed controllers is completely validated by comparing the three intelligent optimization techniques mentioned above. The EWO optimized FOPI λ controller for speed control of sensorless PMBLDC motor clearly outperforms the other two intelligent controllers by minimizing the time domain parameters, THD, performance Indices error, convergence time, control efforts, cost function, mean and standard deviation.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

A review on optimization of antenna array by evolutionary optimization techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
D.D. Devisasi Kala ◽  
D. Thiripura Sundari
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
Optimization Algorithm ◽  
Firefly Algorithm ◽  
Side Lobe ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Content Type ◽  
Gain Enhancement ◽  
Whale Optimization

PurposeOptimization involves changing the input parameters of a process that is experimented with different conditions to obtain the maximum or minimum result. Increasing interest is shown by antenna researchers in finding the optimum solution for designing complex antenna arrays which are possible by optimization techniques.Design/methodology/approachDesign of antenna array is a significant electro-magnetic problem of optimization in the current era. The philosophy of optimization is to find the best solution among several available alternatives. In an antenna array, energy is wasted due to side lobe levels which can be reduced by various optimization techniques. Currently, developing optimization techniques applicable for various types of antenna arrays is focused on by researchers.FindingsIn the paper, different optimization algorithms for reducing the side lobe level of the antenna array are presented. Specifically, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), cuckoo search algorithm (CSA), invasive weed optimization (IWO), whale optimization algorithm (WOA), fruitfly optimization algorithm (FOA), firefly algorithm (FA), cat swarm optimization (CSO), dragonfly algorithm (DA), enhanced firefly algorithm (EFA) and bat flower pollinator (BFP) are the most popular optimization techniques. Various metrics such as gain enhancement, reduction of side lobe, speed of convergence and the directivity of these algorithms are discussed. Faster convergence is provided by the GA which is used for genetic operator randomization. GA provides improved efficiency of computation with the extreme optimal result as well as outperforming other algorithms of optimization in finding the best solution.Originality/valueThe originality of the paper includes a study that reveals the usage of the different antennas and their importance in various applications.

A Novel Binary Whale Optimization Algorithm-Based Optimal Placement of Phasor Measurement Units

2018 ◽  
pp. 115-138 ◽  
Author(s):  
Tapan Prakash ◽  
Vinay Pratap Singh ◽  
Soumya Ranjan Mohanty
Keyword(s):  
Optimization Algorithm ◽  
Phasor Measurement Units ◽  
Operating Conditions ◽  
Whale Optimization Algorithm ◽  
Area Measurement ◽  
Optimal Placement ◽  
Wide Area Measurement System ◽  
Phasor Measurement ◽  
Whale Optimization ◽  
Measurement Units

Wide-area measurement system (WAMS) is an important part of present power system structure as it provides real-time synchronized measurements of the system with the aid of phasor measurement units (PMUs). Due to economic considerations, PMUs should be installed at optimal locations. The optimal placement of PMUs (OPP) is a problem of optimally placing PMUs at strategic locations maintaining the full observability of the system. In this chapter, a novel binary whale optimization algorithm (BWOA) is applied to solve OPP problem. The maximization of measurement redundancy is considered in the objective function. The proposed algorithm is examined on five different test systems operating under normal operating conditions with or without inclusion of zero-injection buses (ZIBs) and compared with the reports available in literature. The results show the effectiveness of the proposed algorithm in solving OPPP.

A parameter estimation method for fractional-order nonlinear systems based on improved whale optimization algorithm

2019 ◽  
Vol 33 (07) ◽  
pp. 1950075 ◽  
Author(s):  
Gong Ren ◽  
Renhuan Yang ◽  
Renyu Yang ◽  
Pei Zhang ◽  
Xiuzeng Yang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Fractional Order ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Whale Optimization Algorithm ◽  
Engineering System ◽  
Grasshopper Optimization Algorithm ◽  
Whale Optimization ◽  
System Characteristics

Compared to the integer-order systems, the system characteristics of the fractional system are closer to the system characteristics of the real engineering system, the study found beyond that, strictly speaking, various physical phenomena in nature are nonlinear. The problem of parameter estimation problem of fractional-order nonlinear systems can be transformed into the problem of parameter optimization problem by constructing an appropriate fitness function. This paper proposes a hybrid improvement algorithm based on whale optimization algorithm (WOA) to solve this problem and verify it both in Lorenz system and Lu system. The simulation result shows that the hybrid improved algorithm is superior to genetic algorithm (GA), particle swarm optimization (PSO), grasshopper optimization algorithm (GOA) and WOA in convergence speed and accuracy.

scholarly journals Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4063 ◽  
Author(s):  
Touqeer Ahmed Jumani ◽  
Mohd Wazir Mustafa ◽  
Nawaf N. Hamadneh ◽  
Samer H. Atawneh ◽  
Madihah Md. Rasid ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Power Quality ◽  
Computational Intelligence ◽  
Research Work ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Inference System ◽  
Study Objective ◽  
Comprehensive Review

The penetration of distributed generators (DGs) in the existing power system has brought some real challenges regarding the power quality and dynamic response of the power systems. To overcome the above-mentioned issues, the researchers around the world have tried and tested different control methods among which the computational intelligence (CI) based methods have been found as most effective in mitigating the power quality and transient response problems intuitively. The significance of the mentioned optimization approaches in contemporary ac Microgrid (MG) controls can be observed from the increasing number of published articles and book chapters in the recent past. However, literature related to this important subject is scattered with no comprehensive review that provides detailed insight information on this substantial development. As such, this research work provides a detailed overview of four of the most extensively used CI-based optimization techniques, namely, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) as applied in ac MG controls from 42 research articles along with their basic working mechanism, merits, and limitations. Due to space and scope constraints, this study excludes the applications of swarm intelligence-based optimization methods in the studied field of research. Each of the mentioned CI algorithms is explored for three major MG control applications i.e., reactive power compensation and power quality, MPPT and MG’s voltage, frequency, and power regulation. In addition, this work provides a classification of the mentioned CI-based optimization studies based on various categories such as key study objective, optimization method applied, DGs utilized, studied MG operating mode, and considered operating conditions in order to ease the searchability and selectivity of the articles for the readers. Hence, it is envisaged that this comprehensive review will provide a valuable one-stop source of knowledge to the researchers working in the field of CI-based ac MG control architectures.

scholarly journals Salp Swarm Optimization Algorithm-Based Fractional Order PID Controller for Dynamic Response and Stability Enhancement of an Automatic Voltage Regulator System

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1472 ◽  
Author(s):  
Ismail Akbar Khan ◽  
Ali S. Alghamdi ◽  
Touqeer Ahmed Jumani ◽  
Arbab Alamgir ◽  
Ahmed Bilal Awan ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Fractional Order ◽  
Optimization Algorithm ◽  
Pid Controller ◽  
Robustness Analysis ◽  
Operating Conditions ◽  
Voltage Regulator ◽  
Swarm Optimization ◽  
Automatic Voltage Regulator ◽  
Avr System

Owing to the superior transient and steady-state performance of the fractional-order proportional-integral-derivative (FOPID) controller over its conventional counterpart, this paper exploited its application in an automatic voltage regulator (AVR) system. Since the FOPID controller contains two more control parameters (µ and λ ) as compared to the conventional PID controller, its tuning process was comparatively more complex. Thus, the intelligence of one of the most recently developed metaheuristic algorithms, called the salp swarm optimization algorithm (SSA), was utilized to select the optimized parameters of the FOPID controller in order to achieve the optimal dynamic response and enhanced stability of the studied AVR system. To validate the effectiveness of the proposed method, its performance was compared with that of the recently used tuning methods for the same system configuration and operating conditions. Furthermore, a stability analysis was carried out using pole-zero and bode stability criteria. Finally, in order to check the robustness of the developed system against the system parameter variations, a robustness analysis of the developed system was undertaken. The results show that the proposed SSA-based FOPID tuning method for the AVR system outperformed its conventional counterparts in terms of dynamic response and stability measures.

scholarly journals Optimal Tuning of SVC-PI Controller using Whale Optimization Algorithm for Angle Stability Improvement

2018 ◽  
Vol 12 (2) ◽  
pp. 620
Author(s):  
N. A. M. Kamari ◽  
I. Musirin ◽  
Z. A. Hamid ◽  
A. A. Ibrahim
Keyword(s):  
Optimization Algorithm ◽  
Damping Ratio ◽  
Optimization Technique ◽  
Computation Time ◽  
Synchronous Generator ◽  
Pi Controller ◽  
Optimization Techniques ◽  
Whale Optimization Algorithm ◽  
Whale Optimization ◽  
Angle Stability

This paper proposed a new swarm-based optimization technique for tuning conventional proportional-integral (PI) controller parameters of a static var compensator (SVC) which controls a synchronous generator in a single machine infinite bus (SMIB) system. As one of the Flexible Alternating Current Transmission Systems (FACTS) devices, SVC is designed and implemented to improve the damping of a synchronous generator. In this study, two parameters of PI controller namely proportional gain, K<sub>P</sub> and integral gain, K<sub>I</sub> are tuned with a new optimization method called Whale Optimization Algorithm (WOA). This technique mimics the social behavior of humpback whales which is characterized by their bubble-net hunting strategy in order to enhance the quality of the solution. Validation with respect to damping ratio and eigenvalues determination confirmed that the proposed technique is more efficient than Evolutionary Programming (EP) and Artificial Immune System (AIS) in improving the angle stability of the system. Comparison between WOA, EP and AIS optimization techniques showed that the proposed computation approach gives better solution and faster computation time.

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