Volleyball Premier League Research Articles

An Intelligence-Based Controller for Improved Frequency Regulation in Deregulated Power Environment

A methodology for designing and assessing an optimal controller to regulate frequency in a two-area interconnected power system with Renewable Energy Sources (RES) is demonstrated in this paper. The Load Frequency Control (LFC) system makes it possible to restore both system frequency and scheduled tie-line power to their nominal values in a deregulated environment. The introduction of an advanced controller has the potential to improve the LFC mechanism's performance. The present article employs the Inertia Emulation Technique (IET) to demonstrate the potential influence of the novel High Voltage Direct Current (HVDC) tie-line model and converter capacitors. The Integral Time-weighted Absolute Error (ITAE) has been taken as an objective function for the proposed controller. This investigation proposes a novel adaptive control strategy i.e., ANN-based (PIλf + PIλDN) controller for the anticipated LFC mechanism. The modified Quasi-Opposition-learning-based Volleyball Premier League (QOVPL) method is utilized to assess the optimal control parameters with the highest efficacy. The effectiveness of the proposed LFC framework has been evaluated through the implementation of established methodologies for managing step and random load perturbations. The supremacy and effectiveness of the proposed control scheme are validated over recently published work.

A Competitive Parkinson-Based Binary Volleyball Premier League Metaheuristic Algorithm for Feature Selection

Abstract A novel proposed Binary Volleyball Premier League algorithm (BVPL) has shown some promising results in a Parkinson’s Disease (PD) dataset related to fitness and accuracy [1]. This paper evaluates and provides an overview of the efficiency of BVPL in feature selection compared to various metaheuristic optimization algorithms and PD datasets. Moreover, an improved variant of BVPL is proposed that integrates the opposite-based solution to enlarge search domains and increase the possibility of getting rid of the local optima. The performance of BVPL is validated using the accuracy of the k-Nearest Neighbor Algorithm. The superiority of BVPL over the competing algorithms for each dataset is measured using statistical tests. The conclusive results indicate that the BVPL exhibits significant competitiveness compared to most metaheuristic algorithms, thereby establishing its potential for accurate prediction of PD. Overall, BVPL shows high potential to be employed in feature selection.

Volleyball premier league algorithm with ACO and ALNS for simultaneous pickup–delivery location routing problem

The reverse logistics problem, which involves customer returns and incurs high additional logistics costs, has become a topic of increasing interest. This problem is modeled as the location routing problem with simultaneous pickup and delivery under time constraints (LRPSPDTW), which is a more complex version of the location routing problem (LRP). Previous studies have shown that existing algorithms are unable to provide satisfactory solution accuracy for this problem. In order to address this, a novel algorithm called the hybrid volleyball premier league (HVPL) algorithm is proposed. Drawing inspiration from the competition mechanism of the original volleyball premier league (VPL) algorithm, the HVPL algorithm incorporates adaptive large neighborhood search (ALNS) and improved ant colony optimization (ACO) as sub-algorithms, allowing them to work together synergistically and strike a balance between exploration and exploitation. The learning and elimination phases of the VPL algorithm have been redesigned to enhance convergence speed. Furthermore, the Bellman’s algorithm has been enhanced to determine depot locations and construct vehicle paths for the solution. Prior to solving the LRPSPDTW problem, benchmark testing was conducted using 35 latest benchmark functions. Experimental results and Friedman mean rank test demonstrated that HVPL exhibits superior exploration and exploitation capabilities. HVPL was subsequently tested on 66 instances based on the modified Solomon’s benchmark and the NEO research group benchmarks, and the results showcased its outperformance over other methods in the majority of cases.

Assessment of Power System Resiliency with New Intelligent Controller and Energy Storage Systems

This research investigates the role of various energy storage systems (ESS) in improving the power system resiliency. Different ESS configurations are analyzed individually and in combination to assess their impact on system performance. Additionally, a fuzzy-based controller (PIλDN + FFOPI) is proposed as a secondary controller for load frequency control, with control parameters optimized using an improved Volleyball Premier League algorithm (IVPL). This algorithm leverages the powerful synergy of opposition-based learning and the versatility of the VPL algorithm, resulting in a highly effective and innovative approach. To validate the proposed approach, the modified Kundur’s 2-area test system under deregulation is first utilized, and its effectiveness is confirmed through testing with both step and random load disturbances. Sensitivity analysis is conducted to evaluate system performance across a wide range of parameter variations. Furthermore, the modified IEEE 39 bus system is utilized to assess compatibility and scalability. Results demonstrate the effectiveness of combined ESS configurations and the fuzzy-based controller in enhancing system stability and reliability. This research contributes to power system engineering by offering insights into the benefits of energy storage systems for dynamic response enhancement. The proposed fuzzy-based control strategy, tuned by the IVPL algorithm, presents a promising approach for improving power system performance and stability.

Antropometric Characteristics Structure of “VC Jahorina” Pale Volleyball Players

Background: Morphological dimensions represent the functioning of the growth and development of the musculoskeletal system and also play a role in specific volleyball activities. In the sphere of volleyball, it is imperative to identify the anthropometric constitution, generated by exogenous and endogenous factors. Aim study: The main goal of the research was to determine the factor structure of isolated latent dimensions of the population of female volleyball players of VC "Jahorina" Pale, a member of the volleyball Premier League of Bosnia and Herzegovina. Methods: The study involved 18 active players of the women's volleyball club Jahorina (BH=173±8.77cm; BW=66.04±9.09kg; BMI=22.03±2.19kg/m2, age=19.11±2.63 years old) members of the volleyball Premier League of Bosnia and Herzegovina. A set of 15 anthropometric variables was measured in the morphological space with the aim of determining correlations between anthropometric characteristics and identifying the latent morphological structure of volleyball players. Result: Statistical processing of the data was performed by Pearson correlation coefficients and Hoteling principal components analysis (PCA). The results of the correlation analysis showed statistically significant linear correlations between most anthropometric variables (pConclusion: It was structured by hypothetical factors with characteristic roots (Eig.>1), which were interpreted as Factor of volume and longitudinal dimensionality of the skeleton (F1=42.45%; Eig. 6.37>1), Factor of skin folds of the trunk (F2=28. 96; Eig. 4.08>1) and Subcutaneous adipose tissue factor of the lower extremities (F3=10.04; Eig.2.12>1). The correlation of the factors confirmed the inverse relationship of the factors (F1-F2=-0.85; F1-F3=-0.81). It can be concluded that the increased volume of volleyball players is not accompanied by higher values of skin folds, but is a consequence of increased muscle mass.

Assessment of Amelioration in Frequency Regulation by deploying Novel Intelligent based Controller with Modified HVDC Tie-Line in Deregulated Environment

ABSTRACT This article emphasizes the design and analysis of an optimal intelligent controller for frequency regulation application. The load frequency control (LFC) mechanism is an eminent and essential mechanism to reinstate the system frequency and scheduled tie-line power to their nominal values. Employing an appropriate controller enhances the operation of the LFC mechanism. Hence, this article put forward an adoptive control policy-based fuzzy-fractional ordered PI controller parallel with fractional PIDN controller (i.e., Fuzzy (PIλf)+PIλDN) for the LFC mechanism. Besides that, a maiden attempt of using a new opposition-learning-based volleyball premier league (OVPL) algorithm is carried out to obtain optimal control parameters. The proposed optimal intelligent controller is explored for a nonlinear multi-area interconnected power system under deregulated environment. The proposed LFC scheme<apos;>s performance has been compared with several popular strategies for step and random perturb in load. Also, the robustness of the proposed scheme has been verified for a wide range of system parameter variations. On the other hand, the modified HVDC tie-line model and the impact of the inertia emulation technique (IET) using converter capacitors on transient behavior are illustrated in this article. Finally, the efficacy of the proposed LFC scheme has been verified over published literature on its platform.

Exploration of novel optimal fuzzy‐based controller for enhancement of frequency regulation of deregulated hybrid power system with modified HVDC tie‐line

Recent advancements in the power system put forth various challenges to frequency regulation studies. These challenges show an immense impact on the power system's inertia and load pattern. The traditional load frequency control (LFC) mechanism may not be efficient to sustain the frequency regulation against these new challenges. Hence an intelligent, effective, and efficient controller is indispensable for various operating conditions of the power system. With this motive, a new intelligence-based controller (i.e., fuzzy (FOPIλ) + PIDN) is proposed and explored for the LFC of the modern power system. Besides, an opposition-based volleyball premier league algorithm is deployed to obtain optimal control parameters for the most promising results. The justification of the controller is evaluated on a nonlinear interconnected hybrid deregulated power system for several case studies. The effectiveness of the proposed controller has been verified by comparing it with several popular strategies for the step and random perturbation in load. Also, the firmness in the dynamic response against a wide range of system parameter variations has been analyzed. Moreover, the participation of a modified high voltage direct current model with inertia emulation technique in the system's inertia build-up has been demonstrated. Finally, the efficacy of the proposed LFC scheme has been verified over previously published literature on their platform.

Optimal CC-2DOF(PI)-PDF controller for LFC of restructured multi-area power system with IES-based modified HVDC tie-line and electric vehicles

This article is introducing an optimal cascaded two-degree of freedom proportional-integral (2DOF(PI)) and proportional-derivative with filter (PDF) controller, i.e., CC-2DOF (PI)-PDF for load frequency control (LFC) mechanism. The designed controller increases the degree of freedom and rejects disturbances faster with fewer dynamics. Further, the application of an opposition-based volleyball premier league algorithm (OVPLA) has been explored to optimize the designed controller's parameters. The proposed LFC mechanism has been studied on a two-area thermal-hydro-gas restructured power system. The thermal system present in each area is equipped with generation rate constraints (GRC) for more realistic analysis. The performance of the suggested OVPLA optimized 2DOF(PI)-PDF controller has been verified over several classical controllers and algorithms separately for step and random load disturbances. Moreover, accurate modeling of the HVDC tie-line and inertia emulation strategy (IES) is discussed, and the power system’s inertia enhancement is verified. This article also highlights the ancillary services of electric vehicles (EVs) and distributed generations (DGs) to tackle any contract violation in the power system. Next, the proposed LFC mechanism's efficacy has been validated on extending a three-area power system. Finally, several case studies and comparative performance analysis with published literature demonstrate the effectiveness and efficiency of investigated LFC in handling any load disturbances.

Impact of Inertia Emulation Based Modified HVDC Tie-Line for AGC Using Novel Cascaded Fractional Order Controller in Deregulated Hybrid Power System

This article deals with the Automatic Generation Control (AGC) mechanism for an interconnected, deregulated hybrid power system. The physical generation constraints such as boiler dynamics, governor dead band (GDB) and generation rate constraints (GRC) are included for a more realistic approach. The integration of distributed generation (DG) systems is also considered to match with present scenario. The intermittency nature of the DGs ruins the system's inertia and dynamic behaviour. A novel cascaded fractional order controller configuration 2-DOF (FOPIλDN)-PDN is proposed for the AGC mechanism and a new quasi opposition based volleyball premier league (QVPL) algorithm is employed to optimize the controller gains. On the other hand, the inertia emulation strategy (IES) based modified HVDC tie-line is used parallel to the AC tie-line to retrace the power system's inertia. The impact of the proposed controller and IES based modified HVDC tie-line has been analysed by subjecting to several case studies. The effectiveness of the QVPL based proposed controller has also been tested by comparing it with the previously published works on the same platform.

Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm.

Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.

A multi objective volleyball premier league algorithm for green scheduling identical parallel machines with splitting jobs

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms.

Fuzzy mutation embedded hybrids of gravitational search and Particle Swarm Optimization methods for engineering design problems

Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) are nature-inspired, swarm-based optimization algorithms respectively. Though they have been widely used for single-objective optimization since their inception, they suffer from premature convergence. Even though the hybrids of GSA and PSO perform much better, the problem remains. Hence, to solve this issue, we have proposed a fuzzy mutation model for two hybrid versions of PSO and GSA — Gravitational Particle Swarm (GPS) and PSOGSA. The developed algorithms are called Mutation based GPS (MGPS) and Mutation based PSOGSA (MPSOGSA). The mutation operator is based on a fuzzy model where the probability of mutation has been calculated based on the closeness of particle to population centroid and improvement in the particle value. We have evaluated these two new algorithms on 23 benchmark functions of three categories (unimodal, multimodal and multimodal with fixed dimension). The experimental outcome shows that our proposed model outperforms their corresponding ancestors, MGPS outperforms GPS 13 out of 23 times (56.52%) and MPSOGSA outperforms PSOGSA 17 times out of 23 (73.91%). We have also compared our results against those of some recently proposed optimization algorithms such as Sine Cosine Algorithm (SCA), Opposition-Based SCA, and Volleyball Premier League Algorithm (VPL). In addition, we have applied our proposed algorithms on some classic engineering design problems and the outcomes are satisfactory. The related codes of the proposed algorithms can be found in this link: Fuzzy-Mutation-Embedded-Hybrids-of-GSA-and-PSO.

Multi-Objective Volleyball Premier League algorithm

This paper proposes a novel optimization algorithm called the Multi-Objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of this study lies in extending the multi-objective version of the Volleyball Premier League algorithm (VPL), which is recently used in such scientific researches, with incorporating the well-known approaches including archive set and leader selection strategy to obtain optimal solutions for a given problem with multiple contradicted objectives. To analyze the performance of the algorithm, ten multi-objective benchmark problems with complex objectives are solved and compared with two well-known multi-objective algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Computational experiments highlight that the MOVPL outperforms the two state-of-the-art algorithms on multi-objective benchmark problems. In addition, the MOVPL algorithm has provided promising results on well-known engineering design optimization problems.

An improved volleyball premier league algorithm based on sine cosine algorithm for global optimization problem

Volleyball premier league (VPL) simulating some phenomena of volleyball game has been presented recently. This powerful algorithm uses such racing and interplays between teams within a season. Furthermore, the algorithm imitates the coaching procedure within a game. Therefore, some volleyball metaphors, including substitution, coaching, and learning, are used to find a better solution prepared by the VPL algorithm. However, the learning phase has the largest effect on the performance of the VPL algorithm, in which this phase can lead to making the VPL stuck in optimal local solution. Therefore, this paper proposed a modified VPL using sine cosine algorithm (SCA). In which the SCA operators have been applied in the learning phase to obtain a more accurate solution. So, we have used SCA operators in VPL to grasp their advantages resulting in a more efficient approach for finding the optimal solution of the optimization problem and avoid the limitations of the traditional VPL algorithm. The propounded VPLSCA algorithm is tested on the 25 functions. The results captured by the VPLSCA have been compared with other metaheuristic algorithms such as cuckoo search, social-spider optimization algorithm, ant lion optimizer, grey wolf optimizer, salp swarm algorithm, whale optimization algorithm, moth flame optimization, artificial bee colony, SCA, and VPL. Furthermore, the three typical optimization problems in the field of designing engineering have been solved using the VPLSCA. According to the obtained results, the proposed algorithm shows very reasonable and promising results compared to others.

HVDC tie-link modeling for restructured AGC using a novel fractional order cascade controller

This paper deals with the Automatic Generation Control (AGC) strategy for multi-source interconnected power system in deregulated environment. Physical constraints like Generation Rate Constraints (GRC), Governor Dead Band (GDB) and Boiler Dynamics (BD) are incorporated in the system for the realistic approach. In this paper, an accurate model of HVDC tie-link is considered for the proposed AGC mechanism. Inertia Emulation based Control (INEC) technique is adopted to utilize the stored energy of HVDC tie-link of the interconnected power system. Distributed Generation (DG) system is also considered in the proposed AGC structure. The DG system comprises of Solar Photovoltaic (SPV), Wind Turbine System (WTS), Diesel Engine Generator (DEG), Aqua Electrolyzer (AE) and Fuel Cell (FC). Finally, a novel application of Volleyball Premier League (VPL) algorithm has been introduced to obtain the optimized gains of the proposed controller. The proposed controller includes the cascade structure of 2-Degree of Freedom Proportional-Integral controller and Fractional Order Proportional-Derivative controller with filter (2-DOF-PI-FOPDN). The robustness and effectiveness of VPL algorithm based proposed controller has been tested by comparing with the published literatures on same platform.

Assessing and Comparing Mental Skills of Men Players' Positions in Volleyball Premier League Team of Iran

Mental skills are important aspects of successful sport performance in all sport competitions. This study was designed to assess and compare the mental skills of men players' positions in premier league volleyball team in year 2015. This cross sectional research includes 69 Iran men's premier league volleyball team players in year 2015 who participated in the premier league competitions in year 2015. Mental skills as well as demographic data of the players was assessed by OMSAT-3 questioner and a personal data form. One-way ANOVA was employed to analyze the data. The result of analysis indicated that there was no significant differences between the main components of fundamental (p=0.21), psychosomatic (p=0.12), and cognitive skills (p=0.584) of different positions of the volleyball players. However, there was a significant difference between the sub skills of reaction to stress (0.002) and fear control (0.020) of different positions of volleyball players. Outside hitters and opposite hitter were significantly weaker than the setters, libero and middle blocker in fear control (p<0.05) and reaction to stress (p<0.05). Given the importance of mental skills in the achievement of volleyball players, focusing on improving the mental skills of the mentioned positions in volleyball especially in outside hitters and opposite hitter is highly recommended. © 2019 Kure Iletisim Grubu A.S.. All Rights Reserved.

Volleyball Premier League Algorithm

This article proposes a novel metaheuristic algorithm called Volleyball Premier League (VPL) inspired by the competition and interaction among volleyball teams during a season. It also mimics the coaching process during a volleyball match. To solve global optimization problems using the volleyball metaphor, there are terms such as substitution, coaching, and learning, which are captured in the VPL algorithm. The proposed algorithm is benchmarked on 23 well-known test functions, which are categorized into three groups, namely unimodal, multimodal and fixed-dimension multimodal functions. The solutions obtained using the VPL have been compared with other metaheuristic algorithms including Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Harmony Search (HS), Sin Cosine Algorithm (SCA), Soccer League Competition (SLC), and League Championship Algorithm (LCA). In addition, VPL has been used to solve three classical engineering design optimization problems. Results show that VPL algorithm possesses a strong capability to produce superior performance over the other well-known metaheuristic algorithms. The results of the experiments also show that the VPL is effectively applicable to solve problems with complex search space.