Adaptive whale optimization for intelligent multi-constraints power quality improvement under deregulated environment

2019 ◽  
Vol 17 (3) ◽  
pp. 490-514
Author(s):  
Niharika Thakur ◽  
Y.K. Awasthi ◽  
Manisha Hooda ◽  
Anwar Shahzad Siddiqui
Keyword(s):  
Power Quality ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Quality Improvements ◽  
Content Type ◽  
Facts Devices ◽  
Whale Optimization ◽  
Quality Issues ◽  
Better Than ◽  
Bus System

Purpose Power quality issues highly affect the secure and economic operations of the power system. Although numerous methodologies are reported in the literature, flexible alternating current transmission system (FACTS) devices play a primary role. However, the FACTS devices require optimal location and sizing to perform the power quality enhancement effectively and in a cost efficient manner. This paper aims to attain the maximum power quality improvements in IEEE 30 and IEEE 57 test bus systems. Design/methodology/approach This paper contributes the adaptive whale optimization algorithm (AWOA) algorithm to solve the power quality issues under deregulated sector, which enhances available transfer capability, maintains voltage stability, minimizes loss and mitigates congestions. Findings Through the performance analysis, the convergence of the final fitness of AWOA algorithm is 5 per cent better than artificial bee colony (ABC), 3.79 per cent better than genetic algorithm (GA), 2,081 per cent better than particle swarm optimization (PSO) and fire fly (FF) and 2.56 per cent better than whale optimization algorithm (WOA) algorithms at 400 per cent load condition for IEEE 30 test bus system, and the fitness convergence of AWOA algorithm for IEEE 57 test bus system is 4.44, 4.86, 5.49, 7.52 and 9.66 per cent better than FF, ABC, WOA, PSO and GA, respectively. Originality/value This paper presents a technique for minimizing the power quality problems using AWOA algorithm. This is the first work to use WOA-based optimization for the power quality improvements.

Optimal location of UPQC for power quality improvement: novel hybrid approach

2020 ◽  
Vol 18 (6) ◽  
pp. 1519-1541
Author(s):  
Kaladhar Gaddala ◽  
P. Sangameswara Raju
Keyword(s):  
Cost Function ◽  
Power Quality ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
Reactive Power Compensation ◽  
Content Type ◽  
Whale Optimization ◽  
Better Than

Purpose In general, the optimal reactive power compensation could drastically enhance the performance of distributed network by the reduction of power loss and by enhancement of line loadability and voltage profile. Till now, there exist various reactive power compensation models including capacitor placement, joined process of on-load tap changer and capacitor banks and integration of DG. Further, one of the current method is the allocation of distribution FACTS (DFACTS) device. Even though, the DFACTS devices are usually used in the enhancement of power quality, they could be used in the optimal reactive power compensation with more effectiveness. Design/methodology/approach This paper introduces a power quality enhancement model that is based on a new hybrid optimization algorithm for selecting the precise unified power quality conditioner (UPQC) location and sizing. A new algorithm rider optimization algorithm (ROA)-modified particle swarm optimization (PSO) in fitness basis (RMPF) is introduced for this optimal selections. Findings Through the performance analysis, it is observed that as the iteration increases, there is a gradual minimization of cost function. At the 40th iteration, the proposed method is 1.99 per cent better than ROA and genetic algorithm (GA); 0.09 per cent better than GMDA and WOA; and 0.14, 0.57 and 1.94 per cent better than Dragonfly algorithm (DA), worst solution linked whale optimization (WS-WU) and PSO, respectively. At the 60th iteration, the proposed method attains less cost function, which is 2.07, 0.08, 0.06, 0.09, 0.07 and 1.90 per cent superior to ROA, GMDA, DA, GA, WS-WU and PSO, respectively. Thus, the proposed model proves that it is better than other models. Originality/value This paper presents a technique for optimal placing and sizing of UPQC. To the best of the authors’ knowledge, this is the first work that introduces RMPF algorithm to solve the optimization problems.

A combined effective time series model based on clustering and whale optimization algorithm for forecasting smart meters electricity consumption

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Medhat Abd el Azem El Sayed Rostum ◽  
Hassan Mohamed Mahmoud Moustafa ◽  
Ibrahim El Sayed Ziedan ◽  
Amr Ahmed Zamel
Keyword(s):  
Optimization Algorithm ◽  
Prediction Accuracy ◽  
Electricity Consumption ◽  
Whale Optimization Algorithm ◽  
Computational Time ◽  
Smart Meters ◽  
Content Type ◽  
Individual Level ◽  
Consumption Values ◽  
Whale Optimization

Purpose The current challenge for forecasting smart meters electricity consumption lies in the uncertainty and volatility of load profiles. Moreover, forecasting the electricity consumption for all the meters requires an enormous amount of time. Most papers tend to avoid such complexity by forecasting the electricity consumption at an aggregated level. This paper aims to forecast the electricity consumption for all smart meters at an individual level. This paper, for the first time, takes into account the computational time for training and forecasting the electricity consumption of all the meters. Design/methodology/approach A novel hybrid autoregressive-statistical equations idea model with the help of clustering and whale optimization algorithm (ARSEI-WOA) is proposed in this paper to forecast the electricity consumption of all the meters with best performance in terms of computational time and prediction accuracy. Findings The proposed model was tested using realistic Irish smart meters energy data and its performance was compared with nine regression methods including: autoregressive integrated moving average, partial least squares regression, conditional inference tree, M5 rule-based model, k-nearest neighbor, multilayer perceptron, RandomForest, RPART and support vector regression. Results have proved that ARSEI-WOA is an efficient model that is able to achieve an accurate prediction with low computational time. Originality/value This paper presents a new hybrid ARSEI model to perform smart meters load forecasting at an individual level instead of an aggregated one. With the help of clustering technique, similar meters are grouped into a few clusters from which reduce the computational time of the training and forecasting process. In addition, WOA improves the prediction accuracy of each meter by finding an optimal factor between the average electricity consumption values of each cluster and the electricity consumption values for each one of its meters.

scholarly journals An Improved Whale Algorithm for Setting Standard Scheduled Block Time Based on the Airline Fairness

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Qian Wang ◽  
Yong Tian ◽  
Lili Lin ◽  
Ratnaji Vanga ◽  
Lina Ma
Keyword(s):  
Mathematical Model ◽  
Mathematical Models ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Civil Aviation ◽  
Convergence Factor ◽  
Suggested Approach ◽  
Whale Optimization ◽  
Block Time ◽  
Better Than

Standard scheduled flight block time (SBT) setting is of great concern for Civil Aviation Administration of China (CAAC) and airlines in China. However, the standard scheduled flight block times are set in the form of on-site meetings in practice and current literature has not provided any efficient mathematical models to calculate the flight block times fairly among the airlines. The objective of this paper is to develop and solve a mathematical model for standard SBT setting with consideration of both fairness and reliability. We use whale optimization algorithm (WOA) and an improved version of the whale optimization algorithm (IWOA) to solve the SBT setting problem. A novel nonlinear update equation of convergence factor for random iterations is used in place of the original linear one in the proposed IWOA algorithm. Experimental results show that the suggested approach is effective, and IWOA performs better than WOA in the concerned problem, whose solutions are better compared to the flight block times released by CAAC. In particular, it is interesting to find that MSE, RMSE, MAE, MAPE and Theil of the reliability in 60%–70% range are always the smallest and the average fairness of airlines is better than that of 60%–75% range. The model and solving approach presented in this article have great potential to be applied by CAAC to determine the standard SBTs strategically.

Improved whale optimization algorithm based on random hopping update and random control parameter

2021 ◽  
Vol 40 (1) ◽  
pp. 363-379
Author(s):  
Yanju Guo ◽  
Huan Shen ◽  
Lei Chen ◽  
Yu Liu ◽  
Zhilong Kang
Keyword(s):  
Optimization Algorithm ◽  
Control Parameter ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Original Algorithm ◽  
Multimodal Functions ◽  
Whale Optimization ◽  
Update Strategy ◽  
Random Control ◽  
Better Than

Whale Optimization Algorithm (WOA) is a relatively novel algorithm in the field of meta-heuristic algorithms. WOA can reveal an efficient performance compared with other well-established optimization algorithms, but there is still a problem of premature convergence and easy to fall into local optimal in complex multimodal functions, so this paper presents an improved WOA, and proposes the random hopping update strategy and random control parameter strategy to improve the exploration and exploitation ability of WOA. In this paper, 24 well-known benchmark functions are used to test the algorithm, including 10 unimodal functions and 14 multimodal functions. The experimental results show that the convergence accuracy of the proposed algorithm is better than that of the original algorithm on 21 functions, and better than that of the other 5 algorithms on 23 functions.

scholarly journals Loss Minimization in Optimal Reactive Power Planning (ORPP) Based on Whale Optimization Algorithm

2018 ◽  
Vol 7 (3.15) ◽  
pp. 192
Author(s):  
Muhammad Hakimin Nasru ◽  
Ismail Musirin ◽  
Mohamad Khairuzzaman Mohamad Zamani ◽  
Siti Rafidah Abdul Rahim ◽  
Muhamad Hatta Hussain ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimal Allocation ◽  
Reactive Power ◽  
Transmission Loss ◽  
Whale Optimization Algorithm ◽  
Loss Minimization ◽  
Power Scheduling ◽  
Whale Optimization ◽  
Reactive Power Planning ◽  
Better Than

The key of RPP is the optimal allocation of reactive power considering location and size. This paper presents the loss minimization in optimal reactive power planning (ORPP) based on Whale Optimization Algorithm (WOA). The objective is to minimize transmission loss by considering several load conditions at bus 3, bus 15 and bus 21. Reactive Power Scheduling (RPS) and Transformer Tap Changer Setting (TTCS) were set as the control variables. Validation was conducted on the IEEE 30 Bus RTS. Results from the study indicate that the proposed WOA can minimize transmission loss better than Evolutionary Programming (EP). 

A study of UPQC: emerging mitigation techniques for the impact of recent power quality issues

Circuit World ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 11-21
Author(s):  
Ananthan Nagarajan ◽  
Sivachandran P. ◽  
Suganyadevi M.V. ◽  
Muthukumar P.
Keyword(s):  
Power Quality ◽  
Content Type ◽  
Facts Devices ◽  
Recent Developments ◽  
Quality Issues ◽  
Power Quality Conditioner ◽  
Compensating Devices ◽  
Quality Of Electric Power ◽  
The Impact

Purpose The purpose of this study is to help the researchers, public, industries and government to realize the tremendous trends to improve the power quality of both sources and load side. Design/methodology/approach The work carried out in the Facts device and power quality issues. Findings Maintaining the quality of electric power is always a challenging task. The effect of power electronics devices leads to improper power quality. The use of FACTS devices is preferably the best approach to treat power-quality-related problems. Usually, all FACTS devices are constructed to operate on the side of either the source side or the load. Originality/value This paper explores a broad comprehensive study of various types of power quality problems and classification of FACTS devices with its recent developments. Furthermore unified power quality conditioner (UPQC) is particularly reviewed to highlight the advantages over other compensating devices. An exhaustive study of literature has been carried out and most significant concepts are presented

Hyperparameter tuning of AdaBoost algorithm for social spammer identification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Krithiga R. ◽  
Ilavarasan E.
Keyword(s):  
Social Networks ◽  
Optimization Algorithm ◽  
Online Social Networks ◽  
Whale Optimization Algorithm ◽  
Ensemble Classifiers ◽  
Classification Problems ◽  
Data Set ◽  
Content Type ◽  
Adaboost Algorithm ◽  
Whale Optimization

Purpose The purpose of this paper is to enhance the performance of spammer identification problem in online social networks. Hyperparameter tuning has been performed by researchers in the past to enhance the performance of classifiers. The AdaBoost algorithm belongs to a class of ensemble classifiers and is widely applied in binary classification problems. A single algorithm may not yield accurate results. However, an ensemble of classifiers built from multiple models has been successfully applied to solve many classification tasks. The search space to find an optimal set of parametric values is vast and so enumerating all possible combinations is not feasible. Hence, a hybrid modified whale optimization algorithm for spam profile detection (MWOA-SPD) model is proposed to find optimal values for these parameters. Design/methodology/approach In this work, the hyperparameters of AdaBoost are fine-tuned to find its application to identify spammers in social networks. AdaBoost algorithm linearly combines several weak classifiers to produce a stronger one. The proposed MWOA-SPD model hybridizes the whale optimization algorithm and salp swarm algorithm. Findings The technique is applied to a manually constructed Twitter data set. It is compared with the existing optimization and hyperparameter tuning methods. The results indicate that the proposed method outperforms the existing techniques in terms of accuracy and computational efficiency. Originality/value The proposed method reduces the server load by excluding complex features retaining only the lightweight features. It aids in identifying the spammers at an earlier stage thereby offering users a propitious environment.

Robust optimization of resource-constrained assembly line balancing problems with uncertain operation times

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wenrui Jin ◽  
Zhaoxu He ◽  
Qiong Wu
Keyword(s):  
Optimization Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Whale Optimization Algorithm ◽  
Line Balancing ◽  
Resource Constrained ◽  
Content Type ◽  
Whale Optimization ◽  
Minmax Regret ◽  
Assembly Line Balancing Problem

PurposeDue to the market trend of low-volume and high-variety, the manufacturing industry is paying close attention to improve the ability to hedge against variability. Therefore, in this paper the assembly line with limited resources is balanced in a robust way that has good performance under all possible scenarios. The proposed model allows decision makers to minimize a posteriori regret of the selected choice and hedge against the high cost caused by variability.Design/methodology/approachA generalized resource-constrained assembly line balancing problem (GRCALBP) with an interval data of task times is modeled and the objective is to find an assignment of tasks and resources to the workstations such that the maximum regret among all the possible scenarios is minimized. To properly solve the problem, the regret evaluation, an exact solution method and an enhanced meta-heuristic algorithm, Whale Optimization Algorithm, are proposed and analyzed. A problem-specific coding scheme and search mechanisms are incorporated.FindingsTheory analysis and computational experiments are conducted to evaluated the proposed methods and their superiority. Satisfactory results show that the constraint generation technique-based exact method can efficiently solve instances of moderate size to optimality, and the performance of WOA is enhanced due to the modified searching strategy.Originality/valueFor the first time a minmax regret model is considered in a resource-constrained assembly line balancing problem. The traditional Whale Optimization Algorithm is modified to overcome the inferior capability and applied in discrete and constrained assembly line balancing problems.

Mathematical Modeling of Enhanced Whale Optimization Based Power Quality Enhancement Using Unified Power Quality Conditioner for Implantable Biomedical Devices

2021 ◽  
Vol 11 (12) ◽  
pp. 3153-3163
Author(s):  
T. Arulkumar ◽  
N. Chandrasekaran
Keyword(s):  
Power Quality ◽  
Optimization Algorithm ◽  
Body Movement ◽  
Whale Optimization Algorithm ◽  
Energy Utilization ◽  
Biomedical Devices ◽  
Current Harmonics ◽  
Performance Estimate ◽  
Whale Optimization ◽  
Implantable Biomedical Devices

Implantable biomedical systems that enable the majority of the functions of wireless implantable devices have made significant progress in recent years. Nonetheless, due to limited miniaturization, power distribution limits, and the unavailability of a stable link between implants and external devices, such systems are primarily limited to investigation. Generating electricity from natural sources and human body movement for implantable biomedical devices has emerged as a viable option. Nowadays, energy sources become the emerging use of electricity grid which has formed new challenges for the effectiveness of power quality, efficient energy utilization and voltage stabilization for biomedical applications. Power quality in the implementation of the smart grid in biomedical devices is regarded to be the most problematic. APFs (Active Power Filter) are preferred to reward the related problems, mainly because they can quickly filter out of the PQ and are a dynamic compensation. The UPQC with a PI control unit with DC source to be converted to a three stage inverter based on Enhanced Whale Optimization Algorithm (EWOA) was precisely implemented in the article in order to eliminate voltage and current harmonics inadequate. Similarly, UPQC also used the Enhanced Whale Optimization Algorithm (EWOA). In this approach, UPQC along with EWOA (Enhanced whale optimization) has been introduced for voltage and current harmonics elimination defect specifically. Similarly, EWOA was too implemented with UPQC. UPQC & EWOA conducted a performance estimate by estimating a simulation, results on comparing the parameters of THD levels, load current and voltage. The performance estimate is also used and the results achieved are shown. In order to analyze THD values and validate the system performance, performance estimates are built and compared with THD values, load voltage and current parameters.

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