scholarly journals Estimation of Transmission Line Parameters Using Voltage-Current Measurements and Whale Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3239
Author(s):  
Wael S. Hassanein ◽  
Marwa M. Ahmed ◽  
Mohamed I. Mosaad ◽  
A. Abu-Siada
Keyword(s):  
Transmission Line ◽  
Objective Function ◽  
Real Time ◽  
Optimization Algorithm ◽  
Time Estimation ◽  
Distribution Networks ◽  
Measured Data ◽  
Whale Optimization Algorithm ◽  
Whale Optimization ◽  
The Impact

Real-time estimation of transmission line (TL) parameters is essential for proper management of transmission and distribution networks. These parameters can be used to detect incipient faults within the line and hence avoid any potential consequences. While some attempts can be found in the literature to estimate TL parameters, the presented techniques are either complex or impractical. Moreover, none of the presented techniques published in the literature so far can be implemented in real time. This paper presents a cost-effective technique to estimate TL parameters in real time. The proposed technique employs easily accessible voltage and current data measured at both ends of the line. For simplicity, only one quarter of the measured data is sampled and utilized in a developed objective function that is solved using the whale optimization algorithm (WOA) to estimate the TL parameters. The proposed objective function comprises the sum of square errors of the measured data and the corresponding estimated values. The robustness of the proposed technique is tested on a simple two-bus and the IEEE 14-bus systems. The impact of uncertainties in the measured data including magnitude, phase, and communication delay on the performance of the proposed estimation technique is also investigated. Results reveal the effectiveness of the proposed method that can be implemented in real time to detect any incipient variations in the TL parameters due to abnormal or fault events.

A Novel Two-Stage Structural Damage Identification Method Based on Superposition of Modal Flexibility Curvature and Whale Optimization Algorithm

2021 ◽  
pp. 2150169
Author(s):  
Minshui Huang ◽  
Xihao Cheng ◽  
Zhigang Zhu ◽  
Jin Luo ◽  
Jianfeng Gu
Keyword(s):  
Optimization Algorithm ◽  
Damage Identification ◽  
Whale Optimization Algorithm ◽  
Two Stage ◽  
Simply Supported ◽  
Damage Location ◽  
Flexibility Matrix ◽  
Modal Flexibility ◽  
Whale Optimization ◽  
The Impact

A novel two-stage method is proposed to properly identify the location and severity of damage in plate structures. In the first stage, a superposition of modal flexibility curvature (SMFC) is adopted to locate the damage accurately, and the identification index of modal flexibility matrix is improved. The low-order modal parameters are used and a new column matrix is formed based on the modal flexibility matrix before and after the structure is damaged. The difference of modal flexibility matrix is obtained, which is used as a damage identification index. Meanwhile, based on SMFC, a method of weakening the “vicinity effect” is proposed to eliminate the impact of the surrounding elements to the damaged elements when damage identification is carried out for the plate-type structure. In the second stage, the objective function based on the flexibility matrix is constructed, and according to the damage location identified in the first stage, the actual damage severity is determined by the enhanced whale optimization algorithm (EWOA). In addition, the effects of 3% and 10% noise on damage location and severity estimation are also studied. By taking a simply supported beam and a four-side simply supported plate as examples, the results show that the method can accurately estimate the damage location and quantify the damage severity without noise. When considering noise, the increase of noise level will not affect the assessment of damage location, but the error of quantifying damage severity will increase. In addition, damage identification of a steel-concrete composite bridge (I-40 Bridge) under four damage cases is carried out, and the results show that the modified method can evaluate the damage location and quantify 5%–92% of the damage severity.

scholarly journals Optimal Allocation of DSTATCOM in Distribution Network Using Whale Optimization Algorithm

2018 ◽  
Vol 8 (5) ◽  
pp. 3445-3449 ◽  
Author(s):  
P. Balamurugan ◽  
T. Yuvaraj ◽  
P. Muthukannan
Keyword(s):  
Optimization Algorithm ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Stability Index ◽  
Distribution Networks ◽  
Whale Optimization Algorithm ◽  
Voltage Profile ◽  
Power Losses ◽  
Whale Optimization ◽  
Static Compensator

This paper deals with a new approach implemented to decrease power losses and improve voltage profile in distribution networks using Distribution STATic COMpensator (DSTATCOM). DSTATCOM location can be determined by the voltage stability index (VSI) and sizing can be identified by nature inspired, recently developed whale optimization algorithm (WOA). To check efficacy, the proposed technique is tested on two standard buses: Indian rural electrification 28-bus and IEEE 69-bus distribution systems. Obtained results show that optimal allocation of DSTATCOM effectively reduces power losses and improves voltage profile.

scholarly journals Whale Optimization Algorithm-Based DG Allotment for Loss Minimization of Distribution Networks

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Power Systems ◽  
Radial Distribution ◽  
Optimization Algorithm ◽  
Distribution Networks ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Loss Minimization ◽  
Optimum Placement ◽  
Suggested Technique ◽  
Whale Optimization

For optimum placement of distributed generation (DG) units in balanced radial distribution network for loss minimization, implementation of whale optimization algorithm (WOA), a state-of-the-art meta-heuristic optimization algorithm is proposed in this paper. Encouraged by bubble-net hunting strategy of whales, WOA mimes the collective practice of humpback whales. For validating performance in solving the mentioned problem, the suggested technique is implemented on IEEE 33-bus and IEEE 69-bus balanced radial distribution test networks. The obtained results demonstrate that feasible and effective solutions are obtained using the proposed approach and can be used as a propitious substitute in practical power systems to overcome the optimum DG siting and sizing issue. Also concerning the best knowledge of the authors, it is the first report on the application of WOA in solving optimum DG siting and sizing issue.

scholarly journals Controller design for underwater robotic vehicle based on improved whale optimization algorithm

2021 ◽  
Vol 10 (2) ◽  
pp. 609-618
Author(s):  
Mustafa Wassef Hasan ◽  
Nizar Hadi Abbas
Keyword(s):  
Motion Control ◽  
Optimization Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Vertical Motion ◽  
Whale Optimization Algorithm ◽  
Linear Quadratic ◽  
Specific Detail ◽  
Fixed Dimension ◽  
Whale Optimization ◽  
The Impact

This paper presents the impact of introducing a two-controller on the linearized autonomous underwater vehicle (AUV) for vertical motion control. These controllers are presented to overcome the sensor noise of the AUV control model that effect on the tolerance and stability of the depth motion control. Linear quadratic Gaussian (LQG) controller is cascaded with AUV model to adapt the tolerance and the stability of the system and compared with FOPID established by the improved whale optimization algorithm (IWOA) to identify which controlling method can make the system more harmonize and tolerable. The developed algorithm is based on improving the original WOA by reshaping a specific detail on WOA to earn a warranty that the new IWOA will have values for the update position lower than the identified lower-bound (LB), and upper-bound (UB). Furthermore, the algorithm is examined by a set of test functions that include (unimodal, multimodal and fixed dimension multimodal functions). The privileges of applying IWOA are reducing the executing time and obtaining the semi-optimal objective function as compared with the original WOA algorithm and other popular swarm-intelligence optimization algorithms.

Whale Optimization Algorithm With Wavelet Mutation for the Solution of Optimal Power Flow Problem

2018 ◽  
pp. 500-553 ◽  
Author(s):  
V. Mukherjee ◽  
Aparajita Mukherjee ◽  
Dharmbir Prasad
Keyword(s):  
Power Systems ◽  
Transmission Line ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Whale Optimization Algorithm ◽  
Fuel Cost ◽  
Test Power ◽  
Whale Optimization ◽  
Line Loss

This chapter proposes whale optimization algorithm (WOA) with wavelet mutation (WOA-WM) for solving optimal power flow (OPF) problem. The proposed WOA-WM algorithm of the present work utilizes wavelet theory to enhance the optimizing performance of basic WOA in exploring the solution space more effectively for getting better solution. Both WOA and the proposed WOA-WM algorithms are tested on four test power systems under different objective functions (that reflects either minimization of fuel cost or that of transmission line loss or improvement of voltage profile) for getting the optimal solutions of the OPF problem. For multi-objective problem formulation, fuel cost, transmission line loss, and voltage deviation are minimized simultaneously. The simulation results are compared to those offered by some recently reported algorithms surfaced in various recent literature. The WOA-WM-based results demonstrate convincing features in solving the OPF problem of the undertaken test power systems.

Application of Whale Optimization Algorithm (WOA) on Quadratic Knapsack 0-1 Problem

2020 ◽  
Vol 4 (1) ◽  
pp. 13
Author(s):  
Ismail Husein ◽  
Abduh Rizki ◽  
Agustina Pradjaningsih
Keyword(s):  
Objective Function ◽  
Knapsack Problem ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Item Parameter ◽  
Iteration Parameter ◽  
Item Parameters ◽  
Whale Optimization ◽  
Quadratic Knapsack ◽  
The Right

<span lang="EN">Quadratic Knapsack Problem is a variation of the knapsack problem that aims to maximize an objective function. The objective function in this case is quadratic. While the constraints used are binary and linear capacity constraints. The Whale Optimization Algorithm is a metaheuristic algorithm that can solve this problem. Therefore, this paper aims to find out the best solution to solve the Knapsack 0-1 Quadratic Problem using the Whale Optimization Algorithm so that its effectiveness and efficiency are known. Based on the research has been done, the algorithm is said to be effective because, from each experiment, the algorithm is always converging or towards maximum profit. Also, with the right parameters, the algorithm can achieve optimal results. It is said to be efficient because getting optimal profit does not require more time and iteration. The combination of item parameters and maximum iteration dramatically Affect the total value of profit and its running time. However, the addition of item parameter combinations is faster to achieve optimal than the maximum iteration parameter.</span>

Sign in / Sign up

Export Citation Format

Share Document