Modified Shuffled Frog Leaping Algorithm Research Articles

Wind power optimization using modified memetic algorithm

This research paper introduces an approach for optimizing the design of wind turbines to enhance their electrical output. To efficiently convert wind energy into electrical energy, a specific turbine design is essential. The study employs a modified shuffled frog-leaping algorithm (MSFLA), an improved variant of the memetic algorithm, to determine the optimal wind power coefficient by adjusting rotor speed at various wind velocities (including rated, cut-in, and cut-out speeds). MSFLA combines global and local search techniques, following divide-and-conquer principles, to achieve better results. By imposing specific turbine constraints, the simulation demonstrates that the wind power coefficient can be increased under optimized conditions.

Distribution Network Reconfiguration in Effective Presence of DGs, EVCSs and DRP: A Novel Multi-Objective Approach

By determining the ideal combination of switch on/off status, distribution network reconfiguration (DNR), a critical operational problem in distribution systems, has been employed to increase system efficiency. Additionally, benefits such as improved voltage profiles and loss reduction through reactive power compensation are provided by placing capacitors in the distribution network optimally. Thus, this study formulates multi-objective DNR with optimal allocation of distributed generators (DGs), electric vehicles charging stations (EVCSs) and capacitors. Additionally, the demand response programme (DRP) is employed to enhance the distribution system’s operation performance. Losses and voltage variation, which are significant objectives for conventional distribution systems, are typically the focus of the DNR problem. Modern distribution systems’ security concerns caused by DGs that could jeopardize power system security have been all but ignored in terms of the issue of power system operation. As a result, the primary objective of this study is to address the DNR issue in order to increase reliability. This study specifies the energy not provided (ENS) in order to do this. This work uses a modified shuffled frog leaping algorithm (MSFLA), which benefits from a novel mutation technique, to reduce processing time and improve solution quality, in particular to avoid becoming stuck in local optima. To evaluate the efficiency and adaptability of the suggested method, it is applied to three distribution systems, comprising 33, 69, and 86-node test networks. After that, the outcomes are contrasted with those of various approaches. Power loss and ENS are reduced by around 45% and 5.5%, respectively, by executing the suggested strategy on a 33-node test system compared to the initial value before the DNR.

A modified shuffled frog leaping algorithm with inertia weight

The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.

Parameter Estimation of Induction Motors using Hybrid GWO-CS Algorithm

This study investigates a hybrid algorithm between Grey Wolf Optimization (GWO) and Cuckoo Search (CS) algorithms to find the parameters of induction motors. The parameters of the induction motor have been estimated by using the data supplied by the manufacturer. The problem for parameter estimation of the induction motor is formulated as an optimization problem. Then, the optimization problem is solved by using GWO and hybrid algorithm based on GWO and CS algorithms for the estimation of induction motor parameters. Numerical results show that both algorithms are capable of solving the optimization problem for finding the parameters of induction motor. Also, two algorithms and other algorithms such as Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Shuffled Frog-Leaping Algorithm (SFLA), and Modified Shuffled Frog-Leaping Algorithm (MSFLA) are compared for the problem. The results show that the hybrid GWO-CS algorithm gives a smaller objective value and closer torque value to the manufacturer’s data than the GWO algorithm and several algorithms for motor 1. Hybrid GWO-CS algorithm gives nearly the same results with GWO algorithm for motor 2.

Optimal Locating and Sizing of DG in Radial Distribution System Using Modified Shuffled Frog Leaping Algorithm

Several advantages have been achieved in distribution systems by adding Distributed Generation (DG). These benefits comprise minimizing power losses and improving the voltage profiles. Such benefits can be enhanced if DGs are optimally sized. This paper presents a DG in the distribution scheme to increase node voltages and reduce the power loss of radial distribution systems by using the Modified Shuffled Frog Leaping Algorithm (MSFLA) technique. The core objective is to decrease active-power losses of the system while keeping the voltage profiles within the defined limits of the system. This paper optimally allocates and sizes three types of DGs in various standard radial test systems and a practical radial feeder. Single and multiple DGs are considered for the proposed work by including some special cases. The standard test systems on which proposed work is performed are 33-bus and 69-bus radial feeders. A practical 43-bus radial feeder of factory area Faisalabad has also been taken under consideration for analysis. The Forward–Backward Sweep (FBS) scheme is selected for the load flow analysis, whereas MSFLA is chosen for finding of optimum size(s) and site(s) of DG(s). Various types of DGs results are retained separately and collectively. We have reduced the losses up to 85.14% and 96.1% for the 33-bus system and 69-bus respectively. The results will depict a significant improvement, both in the minimization of losses and also in improving the voltage profile. The results of multi DGs placement will be much more promising than those achieved after placing them independently.

Control and performance analysis of a grid-tied solar PV system

Considering the environmental, economic, and political context, the integration of renewable energies in electricity production has become essential. Due to its many advantages, solar photovoltaic (PV) energy is one of the most promising solutions. However, the overvoltage of the distribution line network during high power periods and the mismatch between the moments of photovoltaic production and the moments of load consumption impose limits on the integration of photovoltaic systems. This paper proposes a new method to control the integration of photovoltaic systems connected to the grid. This paper proposes a new method of control of the boost chopper for the extraction of the maximum available power (MPPT) and an adequate control system of the inverter interfacing with the grid to solve the problems related to the power quality standards. Indeed, a Modified shuffled frog leaping algorithm (MSFLA) is used to efficiently determine the values of the sliding mode controller (SMC) parameters performing the MPPT task. In addition, the power flow direction is taken into account according to the system architecture to satisfy the grid connection through the coordinated active and reactive power control of the grid side inverter. The found simulation results validate the performance of the proposed control system.

Techno-economic energy management of micro-grid in the presence of distributed generation sources based on demand response programs

In this paper, an energy consumption optimization problem is investigated for a representative Micro-Grid (MG) system including Distributed Energy Resources (DERs) such as Wind Turbines (WTs), Photo-Voltaic (PV) arrays, Combined Heat and Power (CHP), Fuel Cell (FC), and boiler to supply the electricity and thermal load demand instantaneously. This problem is mathematically formulated considering resources and load uncertainties using Beta, Normal, and Weibull distribution function with suitable coefficients. In this regard, a model based on the minimization of objective function consisting of operating costs, pollution, and distribution lines electrical losses is presented and solved using the Modified Shuffled Frog Leaping Algorithm (MSFLA). The main objective of this paper is to choose the best collection of DERs and other resources in MG to study which of them is optimum to achieve the objective function. Demand response program (DR) based on the electricity price is considered as a Time of Use (TOU) tariff in which the simultaneous simulations will be figured out. The comparison of the proposed method with the existing method has verified the superiority of MSFLA approach to solve this optimization problem.

A modified shuffled frog algorithm to improve MPPT controller in PV System with storage batteries under variable atmospheric conditions

Owing to the nonlinear behavior of photovoltaic (PV) power plants, it is essential to utilize the maximum power point tracking (MPPT) methods for generating the maximum possible power. The common MPPT approaches cannot operate appropriately in quickly changing environmental circumstances and under partial shading circumstances. In other words, tracking the global maximum power point (GMPP) is not easy in such conditions and multiple peaks are seen in the power–voltage (P–V) curve. The parameters of the proposed FLC is tuned via the modified shuffled frog leaping algorithm (MSFLA) to optimize the fuzzy system of MPPT. The main problems related to the design of FLCs is to tune fuzzy membership functions. To cope with the above-mentioned challenge, this combination covers the weaknesses of each algorithm, optimizing parameters in FLCs, and enhance the speed and accuracy of the system The suggested design is confirmed for MPPT of a PV-BES system using MATLAB/Simulink software. The results indicate a better performance of the offered FLC comparing with the previous works.

Optimal sizing of distributed generation units and shunt capacitors in the distribution system considering uncertainty resources by the modified evolutionary algorithm

Distributed generation units (DGUs) as auxiliary sources of power generation can play an effective role in meeting the load consumption of the distribution network, also have positive effects such as reducing loss and improving voltage. Moreover, capacitors by reactive power compensation produce positive effects similar to DGUs in the distribution networks. The idea of joint operation of DGUs and shunt capacitors (SCs) in the presence of demand response program (DRP) to derive maximum benefits from their installation is proposed in this paper. The time of use (TOU) mechanism is used as one of the demand response programs (DRPs) to alter the consumption pattern of subscribers and improve the performance of the distribution system. Objective functions include minimization of energy loss, operational cost, and energy not supplied (ENS). In general, the problem of determining the optimal capacity of DGUs and SCs is complex due to the demand variation. Also, considering the effect of uncertainty sources complicate the optimization problem. Hence, a modified shuffled frog leaping algorithm (MSFLA) is proposed to overcome the complexities of this problem. The proposed approach is tested on two 95, and 136-node test networks, and the results are compared with other evolutionary algorithms. According to the obtained results, after using the proposed approach in determining the optimal capacity of DGUs and SCs in the first system, the amount of energy loss, operational cost and ENS dropped by 11, 25.5 and 5% compared to baseline values. After applying the TOU mechanism in allocation of DGUs and SCs simultaneously in the second system by proposed method, the values obtained for the mentioned objectives reduced by 29, 65 and 7% compared to initial values.

A new MPPT design using variable step size perturb and observe method for PV system under partially shaded conditions by modified shuffled frog leaping algorithm- SMC controller

Renewable energies, particularly solar photovoltaic (PV) panels have been largely integrated into power systems, mainly due to the fact that this technology is environmentally friendly and available almost everywhere. However, these systems have some challenges regarding their efficiency. They should always follow the maximum power point (MPP) to deliver the highest power. To this end, maximum power point tracking methods, known as MPPT algorithms, have already been proposed to overcome this problem. The MPPT techniques, commonly used, take into consideration unchanged climate conditions. In this respect, a novel framework is developed in this paper for the MPPT algorithm based on a sliding mode controller (SMC) applicable to PV panels with partial shading conditions (PSC) and uniform conditions. This model employs the modified shuffled frog leaping algorithm (MSFLA) to derive the desired values of the parameters of the controller that utilizes the variable step-size perturb and observe (P&O). The performance of the developed framework is based on using the desired values of the SMC for the variable step-size P&O of the MPPT when the system operates in the grid-connected state. The presented framework is abbreviated MSFLA-SMC. This system shows precise tracking under changing weather conditions and it performs better compared to conventional techniques. The developed control system is associated with desired dynamic response and power flow between the PV system and the grid.

Optimal day-ahead scheduling of microgrid with hybrid electric vehicles using MSFLA algorithm considering control strategies

Microgrids (MGs) have turned into vital components of the modern power system with the capability of efficiently accommodating renewable energies and electric vehicles (EVs) with high flexibility. MGs can contribute to mitigating the operating cost and environmental emissions of power systems. Accordingly, the optimal operation of such systems is of very high significance. In this relation, the problem of optimal day-ahead scheduling of MGs is studied in this paper in the presence of renewable power generation, EVs, and storage systems. The problem is modeled as a scenario-based stochastic optimization problem, characterized using the Monte-Carlo simulation (MCS) method. The developed framework includes one objective function, defined as the total operating cost minimization and the presented single-objective optimization problem is tackled using an effective optimization technique, named “modified shuffled frog leaping algorithm (MSFLA)”. The suggested optimization framework takes into consideration various charging/discharging patterns of EVs. Finally, the problem is simulated on a test MG and the obtained results are compared to those derived by other algorithms to verify the performance of the MSFLA algorithm.

A day-ahead economic scheduling of microgrids equipped with plug-in hybrid electric vehicles using modified shuffled frog leaping algorithm

With the ever-increasing energy demand for energy all across the world and also, the increasing concern on environmental issues due to using fossil fuels mostly in power systems, some appropriate alternatives should be used to solve the issue. In this respect, renewable energy sources (RESs) with almost zero pollution have been turned into the first choice for supplying the required energy. In this study, to achieve the minimum total cost of the grid, a novel problem formulation is proposed to reduce the reliability costs. Simultaneously, the transportation sector has been replacing the current conventional fossil-fuel vehicles with electrified ones, where plug-in electric vehicles (PEVs) as well as plug-in hybrid electric vehicles (PHEV) have captured the attention and there is an increasing rate in using electric vehicles (EVs). These vehicles are capable of connecting to the electrical grid to absorb/deliver energy from/to the grid through the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies. On the other hand, a new concept in power systems, namely microgrid (MG) has been introduced to facilitate the integration of RESs and make the most of EVs’ capabilities using the smart infrastructure. In fact, the vehicle to grid (V2G) capability is used to decrease the operating cost to achieve a proper opportunity to accommodate PEVs in the network. The output power produced by RESs is volatile, which is more obvious in wind energy and solar power. Thus, the resource management issue of MGs would be of very high significance. In this regard, this paper proposes an efficient optimization framework for the optimal day-ahead energy management of MGs, including PEVs and RESs, utilizing an effective stochastic programming method, i.e. unscented transformation (UT). It is significant to note that the problem has tackled as a single-objective stochastic optimization problem, while the objective has been defined as minimizing the total cost of operation, The presented stochastic optimization problem is then solved by employing a nature-inspired efficient method, named “modified shuffled frog leaping algorithm (MSFLA)” and the obtained results are compared to those reported by other methods to verify its performance.

Optimal energy management strategy for a renewable‐based microgrid considering sizing of battery energy storage with control policies

SummaryMicrogrids (MGs) are known as suitable options to accommodatethe high penetration of renewable energies, like solar and wind. MGs have provided the requirements of controlling and adjusting these sources. In addition, batteries are becoming indispensable components of MGs because of their capabilities in addressing the renewable energies' power output intermittency. In MGs, the problem of smart energymanagement along with battery sizing has been introduced as the necessity to ensure the efficient use of renewable sources and decrease traditional fossil‐fuel‐based generation technology penetration level in power systems. Accordingly, a novel method is presented in this paper to effectively address the above‐mentioned requirements, utilizing the modified shuffled frog leaping algorithm (MSFLA), applied to different case studies. The results, obtained from the numerical simulation, are compared to several well‐established optimization approaches to verify the performance of MSFLA. In terms of computational efficiency and quality of the obtained solution, the MSFLA has demonstrated promising outcomes, together with superior performance in comparison with other algorithms. The results show that the presented framework, including the battery sizing, would be very beneficial to minimize the operating cost of MGs.

Analysis and enhancement of PV efficiency with hybrid MSFLA–FLC MPPT method under different environmental conditions

Recently, the tendency towards employing a combined system for generating electricity has significantly increased. In this respect, the combination of a photovoltaic (PV) system and an energy storage system has captured the attention. In this respect, this paper develops a control system for a combined energy system, comprised of a PV and a battery in the grid-connected mode. There are several research works being carried out to exploit the highest power from PV systems, due to their high investment costs. An interesting and effective approach is utilizing a maximum power point tracking so-called “MPPT” algorithm for the PV panel to raise efficiency. This paper proposes the fuzzy logic controller (FLC) and the modified shuffled frog leaping algorithm (MSFLA) to optimally tune the parameters of the controller and follow the maximum power point (MPP). In this respect, an efficient theoretical framework has been developed for the MPPT algorithm to assess its performance under various conditions. Besides, a comprehensive comparison has been made to evaluate the model in different weather conditions, the method developed in this paper is associated with 99% efficiency while other methods result in 95%–97% efficiency with low speed at reaching the MPP that highlight the superior performance of the presented technique and other methods. Accordingly, the techniques studied are ranked as follows based on their performance: MSFLA-FLC > FLC > IC > P&O while their response times are 0.13s, 0.18s, 0.26s and 0.32s,respectively.

Resources Allocation at the Physical Layer for Network Function Virtualization Deployment

Resource Allocation (RA) is one of the important factors in network function virtualization (NFV) deployment. As physical (PHY) layer resources are limited, e.g., transmitted energy and channel uses, the RA problem at the PHY layer for NFV deployment has become a fast-growing problem, especially for supporting ultra-reliable and low-latency communications (URLLC). Moreover, different nodes in NFV have different requirements for end-to-end communication, e.g., a controller has more stringent reliability requirements than does a logical node. There is a need for efficient and robust RA algorithms at the PHY layer for NFV deployment. To illustrate these challenges, we consider an up-link (UL) transmission protocol for NFV deployment, in which wireless transmissions with short packets are considered, and both the packet length and the transmission power are adjustable. Then, for three NFV deployment scenarios, we formulate three RA problems as three optimization problems to obtain the optimal parameters. Since these optimization problems are highly non-convex and they include excessive constraint conditions, the global optimal solutions are hard to obtain and are even infeasible for the conventional heuristic algorithms due to their low convergence efficiency. To address these problems, in this paper, the intelligent scheme of the modified shuffled frog-leaping algorithm (MSFLA) based on improved extremal optimization (EO) is applied to design RA algorithms. Three RA algorithms are designed for three NFV deployment scenarios to evaluate the quality of the solutions produced by the MSFLA-EO scheme. We perform simulations of three proposed RA algorithms in terms of various performance parameters. The experimental results are encouraging and demonstrate the efficiency of the proposed RA algorithms.

Determination of optimal tool path in drilling operation using Modified Shuffled Frog Leaping Algorithm

Determination of optimal tool path in drilling operation using Modified Shuffled Frog Leaping Algorithm

Estimacija parametara jednokavezne asinhrone mašine primjenom teorije haosa

This paper presents the use of chaos theory, specifically the use of the Chaotic Optimization Algorithm - COA, in estimating the parameters of single-cage induction machine equivalent circuit. In order to estimate the parameters of single-cage induction machine equivalent circuit, the values provided by the manufacturer (starting torque, maximal torque, nominal torque and nominal value of the power factor) were used. The results obtained through COA algorithm are compared with the results obtained through the use of Shuffled frog-leaping algorithm (SFLA), Modified shuffled frog-leaping algorithm (MSFLA), Differential evolution (DE), Particle swarm optimization (PSO), GAs (Genetic algorithms ). It has been shown that COA algorithm can be very effectively applied in estimating the parameters of single-cage induction machine equivalent circuit.

Performance analysis of Modified Shuffled Frog leaping Algorithm for Multi-document Summarization Problem

Due to massive growth of Web information, handling useful information has become a challenging issue in now-a-days. In the past few decades, text summarization is considered as one of the solution to obtained relevant information from extensive collection of information. In this paper, a novel approach using modified shuffled frog leaping algorithm (MSFLA) to extract the important sentence from multiple documents is presented. The effectiveness of MSFLA algorithm for summarization model is evaluated by comparing the ROUGE score and statistical analysis of the model with respect to results of other summarization models. The models are demonstrated by the simulation results over DUC datasets. In the present work, it elucidates that MSFLA based model improves the results and find advisable solution for summary extraction

Semantic Representation and Optimized Querying of Cancer Data using Modified Shuffled Frog Leaping Algorithm

Cancer registries are most important to predict and treat the cancer disease. Numerous solutions are available in research to analyze the data in cancer registries. However, there is a lack of well defined data model since there is a link to external web pages. In order to overcome this issue a system is proposed to represent the cancer data using a semantic data model. The data model uses a Resource Description Framework (RDF) format to represent the data from the local cancer databases. It also uses an optimized Querying of the semantically represented data using SPARQL query language. The optimization of the queries is done with the Modified shuffled frog leaping algorithm(MSFL). This helps in treatment of cancer patients in an easy way.

A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis

Fault diagnosis of rotating machinery mainly includes fault feature extraction and fault classification. Vibration signal from the operation of machinery usually could help diagnosing the operational state of equipment. Different types of fault usually have different vibrational features, which are actually the basis of fault diagnosis. This paper proposes a novel fault diagnosis model, which extracts features by combining vibration severity, dyadic wavelet energy time-spectrum, and coefficient power spectrum of the maximum wavelet energy level (VWC) at the feature extraction stage. At the stage of fault classification, we design a support vector machine (SVM) based on the modified shuffled frog-leaping algorithm (MSFLA) for the accurate classifying machinery fault method. Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. Finally, we evaluate our model on real rotating machinery platform, which has four different states, i.e., normal state, eccentric axle fault (EAF), bearing pedestal fault (BPF), and sealing ring wear fault (SRWF). As demonstrated by the results, the VWC method is efficient in extracting vibration signal features of rotating machinery. Based on the extracted features, we further compare our classification method with other three fault classification methods, i.e., backpropagation neural network (BPNN), artificial chemical reaction optimization algorithm (ACROA-SVM), and SFLA-SVM. The experiment results show that MSFLA-SVM achieves a much higher fault classification rate than BPNN, ACROA-SVM, and SFLA-SVM.