A novel fuzzy based weighted aggregation based multi objective function for AVR optimization

2021 ◽  
pp. 1-24
Author(s):  
Amrit Kaur Bhullar ◽  
Ranjit Kaur ◽  
Swati Sondhi
Keyword(s):  
Objective Function ◽  
Weighting Factor ◽  
Superior Performance ◽  
Computational Time ◽  
Multi Objective ◽  
Space Efficiency ◽  
Point Tracking ◽  
Avr System ◽  
Optimum Solution ◽  
Time And Energy

Today optimization algorithms are widely used in every application to increase quality, quantity and efficiency of making products as well as to minimize the production cost. Most of the techniques applied on different applications try to satisfy more than one parameter of interest in the design problem. In doing so, an objective function based on weighted aggregation has been designed to fulfill multi-objective optimization (MOO). A lot of computational time and energy is wasted in tuning the value of weighting factor in terms of number of trials each having hundreds of iterations to achieve the optimum solution. To reduce such tedious practice of adjustment of weighting factor with multiple iterations, Fuzzy technique is proposed for auto-tuning of weighting factor in this paper that will benefit the researchers who are working upon optimization of their designed objectives using artificial intelligence techniques. This paper proposes MOO settlement method that does not require complex mathematical equations in order to simplify the weight finding problem of weighted aggregation objective function (WAOF). The results have been compared in terms of time and space efficiency to show the importance of Fuzzy-WAOF (F-WAOF). Further the results taken on Automatic Voltage Regulator (AVR) system for set point tracking, load disturbance, controller effort and modelling errors, prove the superior performance of the proposed method as compared to state of the art techniques.

Plant propagation algorithm for integration of distributed generation in power systems

2018 ◽  
Vol 37 (1) ◽  
pp. 401-417
Author(s):  
Shreya Mahajan ◽  
Shelly Vadhera
Keyword(s):  
Power System ◽  
Objective Function ◽  
Distributed Generation ◽  
Weighting Factor ◽  
Voltage Profile ◽  
Plant Propagation ◽  
Content Type ◽  
Multi Objective ◽  
Propagation Algorithm ◽  
Optimisation Techniques

Purpose The purpose of this study/paper is to integrate distributed generation optimally in power system using plant propagation algorithm. Distributed generation is a growing concept in the field of electricity generation. It mainly comprises small generation units installed at calculated points of a power system network. The challenge of optimal allocation and sizing of DG is of utmost importance. Design/methodology/approach Plant propagation algorithm and particle swarm optimisation techniques have been implemented where a weighting factor-based multi-objective function is minimised. The objective is to cut down real losses and to improve the voltage profile of the system. Findings The results obtained using plant propagation algorithm technique for IEEE 33-bus systems are compared to those attained using particle swarm optimisation technique. The paper deals with the optimisation of weighting factor-based objective function, which counterpoises the losses and improves the voltage profile of the system and, therefore, helps to deliver the best outcomes. Originality/value This paper fulfils an identified need to study the multi-objective optimisation techniques for integration of distributed generation in the concerned power system network. The paper proposes a novel plant-propagation-algorithm-based technique in appropriate allocation and sizing of distributed generation unit.

scholarly journals Effect of Crossover Probability on Performance of Genetic Algorithm in Scheduling of Parallel Machines for BI- Criteria Objectives

2019 ◽  
Vol 9 (1) ◽  
pp. 2827-2831
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Parallel Machines ◽  
Optimization Techniques ◽  
Computational Time ◽  
Planning And Scheduling ◽  
Multi Objective ◽  
Mutation Operators ◽  
Crossover Probability ◽  
Wide Range

Optimization of multi objective function gain the importance in the scheduling process. Many classical techniques are available to address the multi objective functions but the solutions yield the unsatisfactory results when the problem becomes complex and large. Evolutionary algorithm would be the solution for such problems. Genetic algorithm is adaptive heuristic search algorithms and optimization techniques that mimic the process of natural evolution. Genetic algorithms are a very effective way of obtaining a reasonable solution quickly to a complex problem. The genetic algorithm operators such as selection method, crossover method, crossover probability, mutation operators and stopping criteria have an effect on obtaining the reasonably good solution and the computational time. Partially mapped crossover operators are used to solve the problem of the traveling salesman, planning and scheduling of the machines, etc., which are having a wide range of solutions. This paper presents the effect of crossover probability on the performance of the genetic algorithm for the bi-criteria objective function to obtain the best solution in a reasonable time. The simulation on a designed genetic algorithm was conducted with a crossover probability of 0.4 to 0.95 (with a step of 0.05) and 0.97, found that results were converging for the crossover probability of 0.6 with the computational time of 3.41 seconds.

INFLUENCE OF OBJECTIVE FUNCTIONS IN STRUCTURAL DAMAGE IDENTIFICATION USING REFINED AND SIMPLE MODELS

2009 ◽  
Vol 09 (04) ◽  
pp. 607-625 ◽  
Author(s):  
RICARDO PERERA ◽  
SHENG-EN FANG
Keyword(s):  
Dynamic Response ◽  
Objective Function ◽  
Structural Damage ◽  
Damage Identification ◽  
Computational Cost ◽  
Computational Time ◽  
Fe Model ◽  
Beam Model ◽  
Objective Functions ◽  
Multi Objective

The most usual approach for solving damage identification problems is the use of the finite element (FE) model updating method. To apply the method, a minimization of an objective function measuring the fit between measured and model predicted data is performed. Then, the success of the procedure depends strongly on the accuracy of the FE model and the choice of a suitable objective function. Although detailed FE models provide an accurate means for calculating the dynamic response of the structure, their size and complexity involve a large number of parameters to be updated and a high computational cost. In order to shorten the computational time, more simplified and practical models able to model the global dynamic response of the structure accurately would be desirable. Furthermore, working with several objective functions instead of only one would increase the robustness and performance of the procedure. In this paper, a multi-objective simple beam model is proposed and compared with a more refined model based on plane elements. Furthermore, in the multi-objective framework, different combinations of objective functions are studied. The reliability and effectiveness of the proposed model has been evaluated in a damage detection problem of a reinforced concrete frame experimentally tested under different levels of damage.

A New Algorithm for the Design of Site Utility for Combined Production of Power, Freshwater, and Steam in Process Industries

2021 ◽  
pp. 1-14
Author(s):  
Mahdi Ghiasi ◽  
Mohammad Hasan Khoshgoftar Manesh ◽  
Kamran Lari ◽  
Gholam Reza Salehi ◽  
Masoud Torabi Azad
Keyword(s):  
Fossil Fuels ◽  
Process Integration ◽  
Water Cycle ◽  
Computational Time ◽  
Multi Objective Optimization ◽  
Process Industries ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Utility System ◽  
Optimum Solution

Abstract Site utility without a doubt is one of the major units in process industries that consumed a lot of fossil fuels and significantly emitted emission pollution. In this paper, a systematic procedure was proposed to optimal design and integration of the utility system based on a combination of targeting approach as process integration technique, exergetic, exergoeconomic, exergoenvironmental analysis associated with Life Cycle Assessment (LCA) and multi-objective optimization through Water Cycle and Genetic Algorithms. Total site analysis was performed to provide an essential understanding of the characteristics and interactions of the equipment in the site utility system. Also, it provides aiming for power production and the temperature of the boiler and each steam level with acceptable accuracy. Furthermore, the exergetic, exergoeconomic, and exergoenvironmental analysis was presented to declare the effects of irreversibility, economic, and environmental impacts matter on the system. The multi-objective optimization using Total Annualized Costs (TAC) as one objective function was conducted through STAR software, GA, WCA, the proposed approach using Multi-Objective Genetic Algorithm (MOGA), and the proposed approach using Multi-Objective Water Cycle Algorithm (MOWCA). The capability of the proposed procedure was applied for the site utility of the petrochemical complex. Results show by using the new procedure the optimum solution has been achieved by a significant reduction of computational time.

scholarly journals Optimal Design of IPMSM for EV Using Subdivided Kriging Multi-Objective Optimization

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1490
Author(s):  
Jong-Min Ahn ◽  
Myung-Ki Baek ◽  
Sang-Hun Park ◽  
Dong-Kuk Lim
Keyword(s):  
Optimal Design ◽  
Objective Function ◽  
Pareto Front ◽  
Permanent Magnet Synchronous Motor ◽  
Kriging Model ◽  
Superior Performance ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Design Variables ◽  
Interior Permanent Magnet

In this paper, subdivided kriging multi-objective optimization (SKMOO) is proposed for the optimal design of interior permanent magnet synchronous motor (IPMSM). The SKMOO with surrogate kriging model can obtain a uniform and accurate pareto front set with a reduced computation cost compared to conventional algorithms which directly adds the solution in the objective function area. In other words, the proposed algorithm uses a kriging surrogate model, so it is possible to know which design variables have the value of the objective function on the blank space. Therefore, the solution can be added directly in the objective function area. In the SKMOO algorithm, a non-dominated sorting method is used to find the pareto front set and the fill blank method is applied to prevent premature convergence. In addition, the subdivided kriging grid is proposed to make a well-distributed and more precise pareto front set. Superior performance of the SKMOO is confirmed by compared conventional multi objective optimization (MOO) algorithms with test functions and are applied to the optimal design of IPMSM for electric vehicle.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

Voltage Stability Enhancement with Optimal Placement of IPFC

2019 ◽  
Vol 8 (4) ◽  
pp. 9465-9471
Keyword(s):  
Transmission Line ◽  
Objective Function ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Test System ◽  
Optimal Location ◽  
Optimal Placement ◽  
Utilization Factor ◽  
Location Selection ◽  
Multi Objective

This paper presents a novel technique based on Cuckoo Search Algorithm (CSA) for enhancing the performance of multiline transmission network to reduce congestion in transmission line to huge level. Optimal location selection of IPFC is done using subtracting line utilization factor (SLUF) and CSA-based optimal tuning. The multi objective function consists of real power loss, security margin, bus voltage limit violation and capacity of installed IPFC. The multi objective function is tuned by CSA and the optimal location for minimizing transmission line congestion is obtained. The simulation is performed using MATLAB for IEEE 30-bus test system. The performance of CSA has been considered for various loading conditions. Results shows that the proposed CSA technique performs better by optimal location of IPFC while maintaining power system performance

scholarly journals Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

2021 ◽  
Author(s):  
Ahmad Reza Jafarian-Moghaddam
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Pollutant Emissions ◽  
Scheduling Problems ◽  
Train Scheduling ◽  
Multi Objective ◽  
Consumption Ratio ◽  
Proposed Model ◽  
Objective Model ◽  
Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

scholarly journals Multi-Objective Energy Management of a Micro-Grid Considering Stochastic Nature of Load and Renewable Energy Resources

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 403
Author(s):  
Deyaa Ahmed ◽  
Mohamed Ebeed ◽  
Abdelfatah Ali ◽  
Ali S. Alghamdi ◽  
Salah Kamel
Keyword(s):  
Renewable Energy ◽  
Objective Function ◽  
Energy Management ◽  
Energy Resources ◽  
Management Problem ◽  
Renewable Energy Resources ◽  
Multi Objective ◽  
Stochastic Nature ◽  
Whale Optimization ◽  
Micro Grid

Optimal inclusion of a photovoltaic system and wind energy resources in electrical grids is a strenuous task due to the continuous variation of their output powers and stochastic nature. Thus, it is mandatory to consider the variations of the Renewable energy resources (RERs) for efficient energy management in the electric system. The aim of the paper is to solve the energy management of a micro-grid (MG) connected to the main power system considering the variations of load demand, photovoltaic (PV), and wind turbine (WT) under deterministic and probabilistic conditions. The energy management problem is solved using an efficient algorithm, namely equilibrium optimizer (EO), for a multi-objective function which includes cost minimization, voltage profile improvement, and voltage stability improvement. The simulation results reveal that the optimal installation of a grid-connected PV unit and WT can considerably reduce the total cost and enhance system performance. In addition to that, EO is superior to both whale optimization algorithm (WOA) and sine cosine algorithm (SCA) in terms of the reported objective function.

scholarly journals Practical Prediction of the Boil-Off Rate of Independent-Type Storage Tanks

2021 ◽  
Vol 9 (1) ◽  
pp. 36
Author(s):  
Dong-Ha Lee ◽  
Seung-Joo Cha ◽  
Jeong-Dae Kim ◽  
Jeong-Hyeon Kim ◽  
Seul-Kee Kim ◽  
...  
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Environmentally Friendly ◽  
Fuel Tank ◽  
Computational Time ◽  
Insulation Material ◽  
Element Analysis ◽  
Vertical Temperature Profile ◽  
Space Efficiency ◽  
Key Aspects

Because environmentally-friendly fuels such as natural gas and hydrogen are primarily stored in the form of cryogenic liquids to enable efficient transportation, the demand for cryogenic fuel (LNG, LH) ships has been increasing as the primary carriers of environmentally-friendly fuels. In such ships, insulation systems must be used to prevent heat inflow to the tank to suppress the generation of boil-off gas (BOG). The presence of BOG can lead to an increased internal pressure, and thus, its control and prediction are key aspects in the design of fuel tanks. In this regard, although the thermal analysis of the phase change through a finite element analysis requires less computational time than that implemented through computational fluid dynamics, the former is relatively more error-prone. Therefore, in this study, a cryogenic fuel tank to be incorporated in ships was established, and the boil-off rate (BOR), measured considering liquid nitrogen, was compared with that obtained using the finite element method. Insulation material with a cubic structure was applied to the cylindrical tank to increase the insulation performance and space efficiency. To predict the BOR through finite element analysis, the effective thermal conductivity was calculated through an empirical correlation and applied to the designed fuel tank. The calculation was predicted to within 1% of the minimum error, and the internal fluid behavior was evaluated by analyzing the vertical temperature profile according to the filling ratio.

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