جستجوی مقالات مرتبط با کلیدواژه « بهینه سازی چندهدفه » در نشریات گروه « برق »

Existing power grids are inefficient, due to the lack of energy management and the lack of storage, so power plants have to produce more to maintain power quality. But by using network savers, the need to build more power plants is reduced and network stability is improved and many of these inefficiencies will be remedied. By launching this market, consumers are encouraged to optimally manage their consumption, sell power to the grid during peak hours, increase network stability, and compensate grid shortages through the nearest storage network operator due to proximity to the storage. Consumers cause the least transmission line losses. In this paper, using the proposed multi-objective fuzzy multi-objective optimization problem for modeling of energy storage systems (ESS) planning in active distribution networks (ADS) considers the impact of ESS optimization strategy. Finally, the modified IEEE standard 33-bus distribution system will be used to evaluate the feasibility and performance of the proposed model by examining the two hybrid algorithms.

Data security and privacy in data centers is an important issue. The major anxiety in security and privacy is the result of the fact that the topography of important operas can be available to sensitive information. To improve storage in distributed cloud data centers, the Particle Swarm Optimization (PSO) algorithm has been used to copy data between data centers. This paper summarizes the objectives and constraints of the cloud storage problem in order to achieve good performance by considering the shortest data transmission distance, obtaining optimal storage in distributed data centers with reliability based on PSO algorithm between two central data sets. And then it provides an intelligent cryptographic approach that cloud service operators cannot directly access partial data. Numerical results show that the proposed method can provide a good cloud storage strategy  when the number of distributed data centers is equal, the defense of the main threats in the clouds can be done effectively.

In this paper, a stochastic multi-objective optimization model for planning distribution networks has been proposed. After running this model, the optimal configuration will be obtained. The proposed model has two main objectives (1) Minimizing the Net Present Value and (2) Minimizing the Expected Energy Not Supplied. In this model, based on the optimal solution, the network’s buses and feeders can be either AC or DC. Monte Carlo Simulation (MCS) has been used in order to model stochastic variations of load demands and distributed generations’ output. For obtaining the best solution, the planning problem will be solved with NSGA, and then a tradeoff between NPV and EENS will be calculated with Pareto Front. The proposed model will be tested on a 14-Bus network, which includes AC and DC load points, and DGs with AC or DC output. The test network will be planned as a pure AC network, and also as a hybrid AC/DC network for (1) an urban zone and (2) a case in which some of the load points are industrial loads and have a very large interruption cost. Finally, at the end of the paper, some results such as NPV, ENS, network’s loss, and a reliability index such as SAIDI will be compared.

Today, with the increase in data production volume, attention to machine learning algorithms to extract knowledge from raw data has increased. Raw data usually has redundant or irrelevant features that affect the performance of learning algorithms. Feature selection algorithms are used to improve efficiency and reduce the computational cost of machine learning algorithms. A variety of methods for selecting features are provided. Among the feature selection methods are evolutionary algorithms that have been considered because of their global optimization power. Many evolutionary algorithms have been proposed to solve the feature selection problem, most of which have focused on the target space. The problem space can also provide vital information for solving the feature selection problem. Since evolutionary algorithms suffer from the pain of not leaving the local optimal point, it is necessary to provide an effective mechanism for leaving the local optimal point. This paper uses the PSO evolutionary algorithm with a multi-objective function. In the proposed algorithm, a new mutation method that uses the particle feature score is proposed along with elitism to exit the local optimal points. The proposed algorithm is tested on different datasets and examined with existing algorithms. The simulation results show that the proposed method has an error reduction of 20%, 11%, 85%, and 7% in the Isolet, Musk, Madelon, and Arrhythmia datasets, respectively, compared to the new RFPSOFS method.

Quantum computing is a new method of information processing that is based on the concepts of quantum mechanics and leads to strange and powerful events in the quantum field. The logic synthesis of quantum circuits refers to the process of converting a given quantum gate into a set of gates that can be implemented in quantum technologies. The most famous logic synthesis methods are CSD and QSD. The main goal of this study is to present a multi-objective logical synthesis method combining the above two methods in the quantum circuit model with the aim of optimizing the evaluation criteria. In this proposed method, the solution space is created from different combinations of CSD and QSD decomposition methods. The created solution space is a space with a very large exponential size. Then, using a bottom-up approach of multi-objective dynamic programming, a method is presented to search only a part of the entire solution space to find circuits with the optimal Pareto costs. The obtained results show that this method creates a balance between the evaluation criteria and produces many optimal Pareto solutions that can be selected according to different quantum technologies.

This paper deals with the dynamic economic and emission dispatch during a day as an important challenge in engineering. On the other hand, renewable energy provides an undeniable contribution to the energy supply. Therefore, to create an efficient model, the probability wind energy models have been proposed. In principle, this problem has several limitations and to bring it to reality, practical and nonlinear constraints such as power balance, ramp rate, prohibited zone, non-smooth cost function, and production constraints have been considered. Since these functions i.e. emission, cost, and wind models are conflicting in nature, to solve this problem, a multi-objective virus colony search algorithm (VCS) based on Pareto theory has been proposed. To improve the performance of the virus colony search algorithm, the chaos theory has been employed that eliminates the weakness of the standard algorithm, ie the speed of convergence and increased number of iterations of the algorithm to achieve the optimal solution. Chaos theory refers to the nature of complex systems with unpredictable behavior. Using the stochastic properties of chaos system, Chaos theory can improve the quality of population distribution in search space and enhance algorithm convergence function. The fuzzy decision function is also used to select the best solution from the set of solutions. The proposed model and method are applied to different systems and in some cases are compared with other methods in the articles. The results show an improvement in the performance of the proposed algorithm. The results also show that the presence of renewable resources has reduced production costs and thus increased network security.

In this study, a new multi-objective method is proposed to optimal manage the local energy resources and customers’ load in a microgrid in the presence of demand response programs. The wind turbine and photovoltaic panel are the renewable energy sources of the microgrid. The time of use and critical peak pricing programs are also used to improve the consumption pattern of customers. Moreover, consumers try to use electrical energy when renewable sources are available. Maximizing the profit of microgrid and minimizing the pollutant gases of the microgrid are the objective functions of the demand side management problem. The multi-objective ant lion optimizer algorithm is used to optimize the indices of the microgrid and create the Pareto front. Then, the fuzzy method is utilized to select the best particle equal to the optimal management plan of the microgrid. Ultimately, the proposed method is evaluated in a sample microgrid. The results demonstrate the high efficiency of the proposed method in improving the performance of the microgrid by optimal managing the local energy sources and customers’ load. The proposed method of energy management increases the profitability of the distribution company and decreases the environmental pollution of the microgrid.

Wireless Sensor Networks need primal information like location of the nodes to start working and sending environment data. One method for setting a sensor network in a battlefield is to drop them from helicopter or to fire from artillery. It will cost a lot to Equip all network nodes with GPS. So to reduce these costs, only a few of nodes called anchor nodes are equipped with gps. Location of nodes are estimated by using belief propagation algorithm with measuring distance between nodes (with use of recieved signal strength) and location information of anchor nodes. Since the cost of anchor nodes is reletively high,their number is important. also position of anchor nodes affects algorithms error. In this paper, by using multi-objective algorithms with two objectives of error reduction and anchor nodes number reduction, position and number of anchor nodes are determined. results show that by using this methond, a few solutions are provided which are better than similar algorithms in terms of error reduction and energy consumption.

The performance of an Analog/Digital (A/D) converter, various aspects like general architecture of the converter, architecture of the building blocks or design of the blocks can be improved. The comparator block is a fundamental block in data converters. Due to contradicting design purposes, circuit constraints and necessities, design of comparators and obtaining best circuit performance are complicated and challenging. Such challenges in circuit design necessitate presenting approaches which not only satisfy all the objectives but also, they are cost effective in terms of time and cost. One of the approaches which has recently attracted attentions is the heuristic algorithms based intelligent Methods. Inclined Planes system Optimization algorithm (IPO) is a novel heuristic algorithm inspired by dynamic movement of the objects on frictionless inclined planes. But despite its remarkable ability for exploration and exploitation of the search space, its standard model has complex relationships with many structural parameters that often confuse the user in choosing the effective values for them.In this paper, IPO algorithm is simplified to present a heuristic algorithm (called SIPO) and its efficiency in optimization of 10 standard benchmarks has been evaluated. Then, a multi-objective version of the proposed algorithm (called MOSIPO) for design and optimization of double tail comparator is presented and its efficiency in optimization of double tail comparator has been evaluated and compared with popular multi-objective intelligent methods. The results clearly demonstrate the improved performance and superiority of SIPO and MOSIPO compared to the other methods.

Providing constancy of the nominal voltage level is a major concern in the planning of electric power systems. One of the effective methods to achieve a nominal voltage level is controlling the exciter voltage of the generator. This is done by using AVR. To ensure a better performance of the AVR system under whole operating conditions, the employing of a robust control strategy is essential. In this paper, the PID controller is adopted as a control strategy. Despite its simple structure, the setting of the PID controller parameters is difficult to obtain the robust closed-loop system. In general, there is no straightforward relation between the performance indexes and the controller parameters. To overcome this problem, the control design problem is transformed into an optimization one, and a new performance criterion function is introduced. This new function includes both time-domain and frequency-domain specifications. The Gain and phase margined are maximized in this function to ensure the robustness of the system despite uncertainties; meanwhile, the rise time and overshoot of the step response are minimized to achieve the best performance in the time domain. Ant colony optimization for the continuous domain is used for solving this multi-objective optimization problem. Detailed comparative simulations are carried out via the Monte-Carlo method to illustrate the effectiveness of the proposed control strategy.

O Optimization is a process that has long been considered by researchers in various fields and its methods have been utilized to increase productivity while reducing costs. Moreover, it is essential to achieve simultaneously several contradictory goals. In control engineering, optimizing controller parameters to attain several different goals could be considered as a significant challenge. In this paper, a fractional-order PID controller is designed for a training helicopter with two DC motors, called Twin-Rotor Multi Input Multi Output System (TRMS). The design of this controller is based on the optimization of different objective functions with metaheuristic multi-objective optimization algorithms. Finally, their Pareto front and different evaluation criteria such as Spacing Metric and Non- uniformity of Pareto Front are employed to compare the performance of these algorithms for optimization of controller parameters on the decoupled TRMS system. The results show the superiority of the proposed multi-objective fractional-order PID controller for the TRMS system.

Electricity is one of the most important carriers of energy used in buildings. By introducing energy smart grids (SG) and energy micro smart grids (MSGs) alongside smart buildings, a good platform has been provided to optimize electricity production and consumption. In this paper, an MSG consists of renewable resources, diesel generators, and cell batteries in bidirectional connection with an SG is modeled, which is connected to a smart building, including lighting, temperature, and air conditioning loads on the consumer side Fuzzy controllers have been used to implement intelligent building management systems and MSG to manage power generation and sales with SG. The decision variables are fuzzy controller rules optimized by an NSGAII multi-objective optimization algorithm with five different objective functions. The results show that the fuzzy controllers work better than the methods directly using the power allocated to the building loads as the decision variable.

In the mid-term operational management of multi-carrier energy systems (MCES) in a deregulated system, coordination among owners of production units is very important. Scheduling the CCHP resources and also the behavior of the owners of production resources are the main coordinating challenges. The cogeneration resources play a key role in energy generation networks and are affected by the behavior of the owners and their operation policies. In this paper, a model is presented that coordinates resources  in MCES in competitive conditions. It takes the objectives of key players, including system operator into account. The proposed model is a multi-objective model and aims to minimize mid-term costs for the resource owners and ensures the reliability of the network over the medium term from the perspective of the operator of the whole system. To solve the multi-objective problem, the Epsilon-constraint method has been used and the Pareto front has been solved. The Fuzzy method was used to find a unique answer to the problem. The proposed model is tested on a local MCES (Special Economic Zone in Assaluyeh), and the simulation results indicate that coordinated outage window, eliminates the scheduling challenge for the output of concurrent production units in these networks.

With the expansion of social networks, relationship between people has taken a new form. One of the important issues in social networks is social influence. Research on social influences and how information is disseminated in social networks, indicates that accepting or rejecting a new pattern by a person depends on the acceptance or rejection of the friends of that person. That is, because the people usually trust their friends more than other sources of advertising. As a result, many companies are focused on this type of advertisement which is called viral marketing. Given a large number of users in a social network, selecting the most influential users as target users, through which a company can reach the highest expansion in the network with the lowest cost, is of great importance. In this paper, a new method for identifying the influential nodes in social networks is proposed which is called MOSI (Multi-Objective algorithm based on Structured Information). The proposed method has two goals: "maximize profit" and "minimize similarity among selected users". The evaluation of the proposed method on real datasets indicates that our method has a greater expansion power in comparison with other similar methods.

Reliability enhancement is one of the main objectives of transmission expansion planning (TEP), and reliability evaluating of different planning schemes is one of the greatest challenges in solving the TEP problem. Due to the large scale of networks and high computational burden of the reliability assessment procedure for any network structure, it is almost impossible and impractical to accurately analyze the reliability of all planning schemes during optimization of the TEP problem. In this paper, first, the necessity of performing an accurate analysis on transmission network reliability during multi-objective based optimization of the TEP problem is shown. Then, a practical approach is proposed for solving this problem. The main objective of the proposed approach which is based on classification of events and occurrences of faults, is to make it possible to more accurately analyze the reliability of transmission networks in order to make decisions on the multi-objective TEP, and to achieve a more accurate solution as well; so that the process is not time-consuming and the computational burden is low. To investigate the efficiency of the proposed approach, the modified Garver's network is used and the multi-objective TEP problem is solved using NSGA-II as optimization tool. The results are compared with those of conventional approach. The obtained results show the accuracy of the proposed approach in solving the TEP problem.

The effectiveness of multi-objective optimization methods, especially the methods based on Swarm Intelligence, has led the researchers to utilize them significantly to solve complex engineering problems with multiple conflicting objectives. This paper aimed at evaluating the performance of new and powerful multi-objective heuristic methods based on Swarm Intelligence (including multi-objective versions of MOPSO, MOGWO, NSGSA, MOGOA, MOIPO, MOMIPO, and MOALO algorithms), and used them for optimal design of the Sense Amplifier-based Flip-Flop (SAFF) using 0.18-µm CMOS technology. In this paper, the channel's width values of the transistors as designing variables, and total average power and delay as the fitness values of the two objective functions were assessed and optimized in terms of multi-objective optimization problem using intelligent optimization algorithm based on Swarm Intelligence assumption in order to achieve the desired values of power-delay product (PDP). Comparing the results obtained for all of the above multi-objective optimization methods, the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) performed better. This method was able to perform very well in the statistical indices of fitness and multi-objective optimization criteria in comparison with other methods. It creates an appropriate trade-off between conflicting objective functions with average power of 24 µW, delay of 95.4 ps and PDP of 2.29 fJ.

In this paper, a framework for multi-objective optimization of a hybrid photovoltaic/diesel generator (PV/DG) system has been presented. This study follows the design of the PV/DG system at the lowest cost, lowest emission and highest reliability.The total net present cost (TNPC), CO2 emissions and the loss of power supply probability (LPSP) are selected asthe problem objectives. This system has been designed in three scenarios: 1- without considering uncertainty and operating reserve, 2- with considering uncertainties of the system and 3- with considering uncertainties of the system and using DG as operating reserve. In order to effectively solve this problem, multi-objective versionof crow search algorithm (MO-CSA) has been developed and the results are compared with the results obtained by well-known non-dominated sorting genetic algorithm II (NSGA-II). The coding of the problem has been done in MATLAB environment and the Pareto fronts have been compared. Simulation results indicate that combination of DG and PV is a promising alternative for supplying the electrical demand of stand-alone areas. Moreover, the results obtained by MO-CSA outperform the results obtained by NSGA-II.

In this paper we deal with the optimal coordinated voltage control issue. A common control procedure for this problem is the model predictive control. A major step toward model predictive control in coordinated voltage control issue is the prediction of voltage response trajectory. This paper presents a new technique to predict the trajectory of voltage response based on two points. These two points are as follow: 1- The moment immediately after applying control actions 2- The steady state of the system after applying these actions. The new method is based on this principle that in coordinated voltage control the direction of voltage response alterations based on control measures is monotonic. The control method utilized in this paper is adaptive and flexible. The adaptive feature refers to controllability of system with a change at the operating point. By flexibility feature, we mean solving an optimization problem with a multiobjective SPEA2 optimization algorithm and providing a set of nondominated solutions which enable system operator to determine and apply the best control actions based on economic and operational conditions aided by a multiple criteria decision-making technique based on a priority matrix.