Optimization
Maryam Alipour; Samaneh Soradi zeid
Abstract
This paper deals with a general form of fractional optimal control problems involving variable-order fractional integro differential equation using orthonormal Laguerre wavelets expansions. By effectively employing these functions, product variable-order operational matrices have been ...
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This paper deals with a general form of fractional optimal control problems involving variable-order fractional integro differential equation using orthonormal Laguerre wavelets expansions. By effectively employing these functions, product variable-order operational matrices have been obtained. By using these fractional operational matrices and collocation points, the study transforms the original continuous-time optimal control problems of variable-order fractional integro-differential equations into a system of linear or non-linear algebraic equations. Attempts have been made to use the collocation method with a joint application of Lagrange multiplier technique, to obtain the approximate cost function based on determining the state and control functions. The main components for applying these wavelets is to have viable solutions due to their orthogonality. In addition, the convergence analysis is presented with respect to the operational matrices of this scheme. Simulation results indicate that the proposed method works well and provides satisfactory results with regard to accuracy and computational effort.
Optimization
Shahpour Rahmani; Nasser Yazdani
Abstract
High-speed rail systems, operating at speeds up to 350 km/h, face significant challenges in delivering reliable network connectivity due to frequent handovers, signal degradation, and network congestion. This paper proposes the 5G-R framework, an optimized solution integrating beamforming, network slicing, ...
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High-speed rail systems, operating at speeds up to 350 km/h, face significant challenges in delivering reliable network connectivity due to frequent handovers, signal degradation, and network congestion. This paper proposes the 5G-R framework, an optimized solution integrating beamforming, network slicing, railway-specific Long Short-Term Memory (LSTM) algorithms, and Multi-Access Edge Computing (MEC) to enhance connectivity performance. By leveraging real-time train data, such as speed and GPS location, the framework optimizes handover prediction and traffic management, achieving robust performance in diverse environments. Compared to 4G LTE and standard 5G, the 5G-R framework demonstrates significant improvements, including a 250 Mbps throughput, 15 ms latency, and 95% handover success rate. Network slicing optimizes resource allocation, reducing congestion by approximately 30%, while MEC enables low-latency control for train systems. Field trials along the Beijing-Zhangjiakou railway (174 km, urban/suburban) and simulations validate the framework’s adaptability across urban and rural routes. Designed for compatibility with the Future Railway Mobile Communication System (FRMCS), the 5G-R framework lays a foundation for future advancements, including 6G and satellite communications. Future research should focus on optimizing performance in extreme environments and densely populated routes to support autonomous transport systems. This optimization-driven approach establishes a scalable model for next-generation rail communication systems.
Optimization
Mahdi Alinaghizadeh Ardestani; Parham Parham Haji Ali Mohamadi
Abstract
Low back pain and spinal disorders are widespread issues affecting individuals globally, often requiring effective rehabilitation methods. This paper proposes a cable-driven parallel robot designed to assist in rehabilitation by moving patients' legs along frontal and sagittal axes. A novel Current Iterative ...
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Low back pain and spinal disorders are widespread issues affecting individuals globally, often requiring effective rehabilitation methods. This paper proposes a cable-driven parallel robot designed to assist in rehabilitation by moving patients' legs along frontal and sagittal axes. A novel Current Iterative Learning Control (CILC) algorithm is introduced to enhance the system's precision and reliability. The CILC ensures the convergence of system states and outputs to desired trajectories, maintaining bounded tracking errors even under disturbances, noise, or initial condition inaccuracies. Simulations demonstrate the controller's effectiveness when applied to the robotic structure, highlighting its potential for accurate and robust rehabilitation applications. By addressing challenges such as system nonlinearity and external uncertainties, the proposed solution offers a promising advancement in electromechanical rehabilitation equipment. This innovation not only improves patient outcomes but also provides a cost-effective and adaptable tool for diverse therapeutic needs. The integration of advanced control strategies with robotic systems marks a significant step forward in spinal rehabilitation technology.
Optimization
Hamed Maleki; Mohammad Sadegh Sepasian; Mohammad Reza Aghamohammadi; Mousa Marzband
Abstract
This study investigates the optimal design configuration of a hydrogen refueling station located in southern Iran, focusing on the integration of renewable energy sources and seawater desalination technology to achieve self-sufficiency. The station integrates various components, including photovoltaic ...
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This study investigates the optimal design configuration of a hydrogen refueling station located in southern Iran, focusing on the integration of renewable energy sources and seawater desalination technology to achieve self-sufficiency. The station integrates various components, including photovoltaic panels, fuel cells, desalination units, natural gas and power-to-hydrogen conversion systems, and storage facilities for water and hydrogen. The primary goals are to achieve an independent power supply from renewable sources and an autonomous water supply through seawater desalination. To determine the most cost-effective configuration, a Mixed Integer Linear Programming (MILP) model is developed, taking into account the water and power consumption of each component. The objective is to minimize the Net Present Cost (NPC) of investment, maintenance, and operation. The model is implemented and solved using the CBC solver within the PYOMO environment. The study's findings reveal that converting natural gas to hydrogen is more economically viable than power-to-hydrogen conversion, with the former accounting for more than 95% of the hydrogen produced. The power demand is effectively met by a combination of photovoltaic systems, fuel cells, and hydrogen storage. Moreover, the study highlights the benefits of integrating water and hydrogen storage systems, which optimizes the utilization of photovoltaic energy. Excess energy generated by the photovoltaic panels is utilized for seawater desalination and the production of green hydrogen
Optimization
Seyed-Saeid Moosavi-Anchehpoli; Mahmood Moghaddasian; Maryam Golpour
Abstract
In an electric vehicle, energy storage systems (ESSs) are critical for sinking and sourcing power as well as ensuring operational protection. Because of their high power density, quick charging or discharging, and low internal loss, supercapacitors (SCs) are a recent addition to the types of energy storage ...
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In an electric vehicle, energy storage systems (ESSs) are critical for sinking and sourcing power as well as ensuring operational protection. Because of their high power density, quick charging or discharging, and low internal loss, supercapacitors (SCs) are a recent addition to the types of energy storage units that can be used in an electric vehicle as an energy storage systems. They can be used in conjunction with batteries or fuel cells to create a hybrid energy storage device that maximizes the benefits of each component while minimizing the disadvantages. This paper presents a multilayer perceptrons (MLP) feedforward artificial neural network for supercapacitor state-of-charge diagnosis in vehicular applications. The proposed approach is tested using a supercapacitor Maxwel model that is subjected to complex charge and discharge current profiles as well as temperature changes. The proposed wavelet neural network and the validation results significantly improves state-of-charge estimation accuracy in different current discharge profiles.
Optimization
Sadegh Etedali
Abstract
This paper proposes three techniques aimed at enhancing the seismic performance of base-isolated tall buildings through uniform deformation of the superstructure. The first and second methods focus on generating a uniform modal shape and an even distribution of seismic loads across the floors, while ...
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This paper proposes three techniques aimed at enhancing the seismic performance of base-isolated tall buildings through uniform deformation of the superstructure. The first and second methods focus on generating a uniform modal shape and an even distribution of seismic loads across the floors, while the third method seeks to minimize the standard deviation of story drifts. For these purposes, an optimization procedure based on a gas Brownian motion optimization (GBMO) algorithm is defined. Simulation results, compared to those for a 20-story reference base-isolated structure, demonstrate that these techniques effectively reduce maximum floor displacement, particularly in the upper levels of the studied buildings. The proposed methods show clear advantages in lowering maximum floor drift, a critical factor in seismic damage. Specifically, methods 1 and 3 resulted in significant reductions in maximum floor drift, ranging from 30% to 80% in the upper floors. Additionally, these methods led to a reduction of 10% to 15% in maximum acceleration and seismic forces on the upper floors, while a slight increase was observed in the lower floors. Among the methods, method 1 exhibited the best overall performance, yielding average reductions of 6.65%, 32.65%, and 0.88% in maximum floor displacement, drift, and acceleration, respectively, when compared to the reference base-isolated structure. While methods 2 and 3 resulted in only modest reductions in displacement and acceleration, they were effective in significantly lowering maximum floor drift.
Optimization
Faezeh Gholipour Zarandi; Masoud Rashidinejad; Amir Abdollahi; Ali Yazhari Kermani
Abstract
The proliferation of renewable energy sources, with their inherent uncertainty in smart microgrids, necessitates the use of flexible resources to maintain grid stability. However, implementing these flexibility-based approaches can have a multidimensional impact, including economic, technical, social, ...
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The proliferation of renewable energy sources, with their inherent uncertainty in smart microgrids, necessitates the use of flexible resources to maintain grid stability. However, implementing these flexibility-based approaches can have a multidimensional impact, including economic, technical, social, and environmental considerations. This study investigates these effects, with a particular focus on how flexibility provision influences battery aging, which is a critical aspect since batteries are the primary source of flexibility in microgrids. Here, a Lexicographic approach is used to optimize the multi-objective operation problem by minimizing costs while maximizing flexibility. Batteries act as the main source of flexibility and compensate for the uncertainty associated with solar energy production; therefore, it is important to investigate the battery's aging upon flexibility provision. The analysis shows a trade-off between flexibility and economic efficiency. Hence, from an economic point of view, increasing reliance on batteries and micro turbine production to improve flexibility leads to higher operating costs. From a social perspective, the proposed approach increases microgrid reliability by minimizing the cost of energy not supplied. Considering the technical aspect, the results indicate that increasing the use of batteries in order to increase microgrids' flexibility accelerates their aging, hence decreasing their corresponding state of health. Further, the simulation results show that flexibility comes with an environmental cost. Therefore, increasing reliance on micro turbine production and the possibility of purchasing energy from sources with more emissions to provide the required flexibility can lead to an increase in the cost of pollution.
Optimization
Asieh Ghanbarpour; Soheil Zaremotlagh; Fahimeh Dabaghi-Zarandi
Abstract
Optimization algorithms are widely used in various fields to find the best solution to a problem by minimizing or maximizing an objective function, subject to certain constraints. This paper introduces the development and application of an innovative optimization algorithm (WOADD) designed to address ...
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Optimization algorithms are widely used in various fields to find the best solution to a problem by minimizing or maximizing an objective function, subject to certain constraints. This paper introduces the development and application of an innovative optimization algorithm (WOADD) designed to address the challenges posed by constrained optimization problems with dependent data. Unlike traditional algorithms that struggle with data dependencies and valid range constraints, WOADD incorporates a novel normalization process and a dynamic updating mechanism that accurately considers the interdependencies among features. Specifically, it adjusts the search strategy by calculating a scaling parameter to maneuver within feasible regions, ensuring the preservation of data dependencies and adherence to constraints, thus leading to more efficient and precise optimization outcomes. Our extensive experimental analysis, comparing WOADD against other swarm-based optimization methods on a suite of benchmark functions, illustrates its superior performance in terms of faster convergence rates, improved solution quality, and enhanced determinism in outcomes.
Optimization
Zobeir Raisi; John Zelek
Abstract
Scene text detection frameworks heavily rely on optimization methods for their successful operation. Choosing an appropriate optimizer is essential to performing recent scene text detection models. However, recent deep learning methods often employ various optimization algorithms and loss functions without ...
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Scene text detection frameworks heavily rely on optimization methods for their successful operation. Choosing an appropriate optimizer is essential to performing recent scene text detection models. However, recent deep learning methods often employ various optimization algorithms and loss functions without explicitly explaining their selections. This paper presents a segmentation-based text detection pipeline capable of handling arbitrary-shaped text instances in wild images. We explore the effectiveness of well-known deep-learning optimizers to enhance the pipeline's capabilities. Additionally, we introduce a novel Segmentation-based Attention Module (SAM) that enables the model to capture long-range dependencies of multi-scale feature maps and focus more accurately on regions likely to contain text instances.The performance of the proposed architecture is extensively evaluated through ablation experiments, exploring the impact of different optimization algorithms and the introduced SAM block. Furthermore, we compare the final model against state-of-the-art scene text detection techniques on three publicly available benchmark datasets, namely ICDAR15, MSRA-TD500, and Total-Text. Our experimental results demonstrate that the focal loss combined with the Stochastic Gradient Descent (SGD) + Momentum optimizer with poly learning-rate policy achieves a more robust and generalized detection performance than other optimization strategies. Moreover, our utilized architecture, empowered by the proposed SAM block, significantly enhances the overall detection performance, achieving competitive H-mean detection scores while maintaining superior efficiency in terms of Frames Per Second (FPS) compared to recent techniques. Our findings shed light on the importance of selecting appropriate optimization strategies and demonstrate the effectiveness of our proposed Segmentation-based Attention Module in scene text detection tasks.
Optimization
Maedeh Abedini Bagha; Kambiz MajidZadeh; Mohammad Masdari; Yousef Farhang
Abstract
Software-defined networking is a new network model proposed to solve the complexity of traditional network problems and facilitate dynamic network operation and management. The separation of the control plane from the data plane is the main idea of software-defined networks. Controllers are the operating ...
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Software-defined networking is a new network model proposed to solve the complexity of traditional network problems and facilitate dynamic network operation and management. The separation of the control plane from the data plane is the main idea of software-defined networks. Controllers are the operating system of software-defined networks and are responsible for managing the entire network. It is essential to locate controllers appropriately to have a balanced topology while guaranteeing low latency. In this work, a metaheuristic algorithm is used for controller placement. First, the problem is formulated, and the network is partitioned by a clustering algorithm. Then, the seagull optimization algorithm is used to determine a suitable place for the controller in each network partition dynamically. Simulations are performed on the standard network topology from the internet topology zoo dataset to evaluate the proposed method. Simulation results reveal that the proposed method performs well in case of delay and load balancing compared with the state-of-the-art optimization algorithms.
Optimization
Vahid Kiani; Azadeh Soltani
Abstract
Sensor placement is a critical issue in wireless sensor networks that affects the quality of wireless sensor network coverage. In this paper, we propose an improved virtual force algorithm based on the states of matter (IVFASM) for relocating sensors of a mobile wireless sensor network. IVFASM simulates ...
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Sensor placement is a critical issue in wireless sensor networks that affects the quality of wireless sensor network coverage. In this paper, we propose an improved virtual force algorithm based on the states of matter (IVFASM) for relocating sensors of a mobile wireless sensor network. IVFASM simulates the behavior of molecules in different states of matter to improve the coverage of sensors. In the proposed IVASM algorithm, the strength of repulsive forces, the kinetic energy of the matter molecules, and attraction radius are dynamically adjusted over time according to different states of matter. As a result, in the gaseous state, sensors move rapidly apart; in the liquid state, sensors absorb each other to fill small holes gradually; in the solid state, sensors stabilize their final position. In the simulation, different states of matter led to improved coverage and fewer holes. Evaluation of the proposed method on 14 sample problems with the different numbers of sensors and comparison of the results with state of the art revealed that the proposed method can achieve a higher coverage rate in almost all sample problems. For a sample problem of 30 sensors, genetic algorithm (GA) and particle swarm optimization (PSO) achieved a coverage ratio of 69%, fuzzy redeployment algorithm (FRED) achieved a coverage ratio of 72%, classical virtual force algorithm (VFA) obtained a coverage ratio of 79%, improved virtual force algorithm based on area intensity (IVFAI) achieved a coverage ratio of 82%, and our proposed method IVFASM achieved a coverage ratio of 83%.
Optimization
Davoud Mirzaei; Saeid Amini
Abstract
In this paper, an ultrasonic horn based on the PSO algorithm for emulsion homogenization is optimized and fabricated. The application of various ultrasonic instruments such as horns in different industrial procedures is increasingly expanding and developing. Horn is a tool that has played a crucial role ...
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In this paper, an ultrasonic horn based on the PSO algorithm for emulsion homogenization is optimized and fabricated. The application of various ultrasonic instruments such as horns in different industrial procedures is increasingly expanding and developing. Horn is a tool that has played a crucial role in the energy transfer to fluid. Longitudinal frequency, vibration amplitude, length-to-diameter ratio, a distance of frequency from other frequency modes, wide distribution of cavitation along with the horn’s length, and increasing the area of acoustic energy transfer are the main characteristics of the horn design procedure. Therefore considering these important features with using the PSO algorithm and electro-mechanical circuit method to finding resonance frequency, the design procedure of the optimized horn is performed. The definition of the objective function is based on the horn’s amplification factor, and the rest of the other characteristics are defined as design constraints. The simulation results show a 15% improvement in the natural frequency compared to the target frequency and a suitable frequency distance of 2.5 kHz between the previous and next modes. According to the barbell part of the horn, the amplification factor of 14 was obtained for the proposed horn, the frequency and amplitude of the vibration were evaluated. The experimental results were very close in terms of amplification factor and frequency to the simulation results with reasonable accuracy.
Optimization
Amin Moradbeiky; Vahid Khatibi; Mehdi Jafari Shahbazzadeh
Abstract
Managing software projects due to its intangible nature is full of challenges when predicting the effort needed for development. Accordingly, there exist many studies with the attempt to devise models to estimate efforts necessary in developing software. According to the literature, the accuracy of estimator ...
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Managing software projects due to its intangible nature is full of challenges when predicting the effort needed for development. Accordingly, there exist many studies with the attempt to devise models to estimate efforts necessary in developing software. According to the literature, the accuracy of estimator models or methods can be improved by correct application of data filtering or feature weighting techniques. Numerous models have also been proposed based on machine learning methods for data modeling. This study proposes a new model consisted of data filtering and feature weighting techniques to improve the estimation accuracy in the final step of data modeling. The model proposed in this study consists of three layers. Tools and techniques in the first and second layers of the proposed model select the most effective features and weight features with the help of LSA (Lightning Search Algorithm). By combining LSA and an artificial neural network in the third layer of the model, an estimator model is developed from the first and second layers, significantly improving the final estimation accuracy. The upper layers of this model filter out and analyze data of lower layers. This arrangement significantly increased the accuracy of final estimation. Three datasets of real projects were used to evaluate the accuracy of proposed model, and the results were compared with those obtained from different methods. The results were compared based on performance criteria, indicating that the proposed model effectively improved the estimation accuracy.
Optimization
Nastaran Darjani; Hesam Omranpour
Abstract
Nowadays time series analysis is an important challenge in engineering problems. In this paper, we proposed the Comprehensive Learning Polynomial Autoregressive Model (CLPAR) predict linear and nonlinear time series. The presented model is based on the autoregressive (AR) model but developed in a polynomial ...
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Nowadays time series analysis is an important challenge in engineering problems. In this paper, we proposed the Comprehensive Learning Polynomial Autoregressive Model (CLPAR) predict linear and nonlinear time series. The presented model is based on the autoregressive (AR) model but developed in a polynomial aspect to make it more robust and accurate. This model predicts future values by learning the weights of the weighted sum of the polynomial combination of previous data. The learning process for the hyperparameters and properties of the model in the training phase is performed by the metaheuristic optimization method. Using this model, we can predict nonlinear time series as well as linear time series. The intended method was implemented on eight standard stationary and non-stationary large-scale real-world datasets. This method outperforms the state-of-the-art methods that use deep learning in seven time series and has better results compared to all other methods in six datasets. Experimental results show the advantage of the model accuracy over other compared methods on the various prediction tasks based on root mean square error (RMSE).
Optimization
Hassan Ghaedi; Seyed Reza Kamel Tabbakh Farizani; Reza Gaemi
Abstract
In this paper, a two-level stacking technique with feature selection is used to detect power theft. The first level of this technique uses base classifiers such as support vector machine (SVM), naïve Bayes (NB), and AdaBoost selected by evaluating the F-score and diversity criteria. The appropriate ...
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In this paper, a two-level stacking technique with feature selection is used to detect power theft. The first level of this technique uses base classifiers such as support vector machine (SVM), naïve Bayes (NB), and AdaBoost selected by evaluating the F-score and diversity criteria. The appropriate features of the base classifiers are selected using a new feature selection algorithm based on the cheetah optimization algorithm (CHOA). This algorithm ensures diversification and intensification in each step of running by adjusting the Attention parameter of the cheetahs. In the second level, a single-layer perceptron (SLP) model is used to obtain the weight of the base classifiers and combine their predictions. The proposed framework is evaluated on the Irish Social Science Data Archive (ISSDA) dataset, and MATLAB R2020b is used for simulation and evaluation. The results of the accuracy, recall, precision, and F-score, specificity, and receiver operating characteristic (ROC) criteria indicated the high efficiency of the proposed framework in detecting power theft.
Optimization
Alireza HossienPour; Ahmad Khajeh
Abstract
In this paper, the effects of magnetization patterns on the performance of Hybrid Electrical Vehicle (HEV) are investigated. HEVs have three magnetic field sources: armature winding, permanent magnets, and field winding. To initiate the investigation, the magnetic field distributions produced by these ...
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In this paper, the effects of magnetization patterns on the performance of Hybrid Electrical Vehicle (HEV) are investigated. HEVs have three magnetic field sources: armature winding, permanent magnets, and field winding. To initiate the investigation, the magnetic field distributions produced by these three sources are obtained. By using the magnetic field distributions, the machine is analyzed under no-load and on-load conditions, and the operational indices, such as self and mutual inductance, cogging-, reluctance- and instantaneous torque, and unbalance magnetic force (UMF) in x- and y direction are calculated. Various magnetization patterns are considered to investigate their influences on the performance of the machine. This step was done with Maxwell software. Furthermore, instantaneous torque and magnitude of UMF are expressed in term of pole arc to pole pitch ratio by using artificial intelligence. The optimal of the pole arc to pole pitch ratio to maximize the average of instantaneous torque and minimize the magnitude of UMF by some multi-objective algorithms is also computed. The modeling and optimization are performed by Matlab Software.
Optimization
Seyed Mehdi Shafiof; Javad Askari Marnani; Maryam Shamssolary
Abstract
This article aims to introduce a modern numerical method based on the hybrid functions, consisting of the Bernoulli polynomials and Block-Pulse functions. An indirect approach is proposed for solving the fractional optimal control problems (FOCPs). Firstly, the two-point boundary value problem (TPBVP) ...
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This article aims to introduce a modern numerical method based on the hybrid functions, consisting of the Bernoulli polynomials and Block-Pulse functions. An indirect approach is proposed for solving the fractional optimal control problems (FOCPs). Firstly, the two-point boundary value problem (TPBVP) is calculated for a class of FOCPs, including integer-fractional derivatives, leading to a system of fractional differential equations (FDEs), which have the left and right-sided Caputo fractional derivatives (CFD). Therefore, a new approach is proposing to achieve the left Riemann-Liouville fractional integral (LRLFI) and right Riemann-Liouville fractional integral (RRLFI) operators for Bernoulli hybrid functions. Then, hybrid functions approximation, LRLFI, and RRLFI operators, and the collocation method are used to solve the TPBVP. The error bounds for the hybrid function and LRLFI and RRLFI operators are also presented. Moreover, the convergence of the proposed method is proved. Finally, the simplicity and accuracy of the method are illustrated using some numerical examples.
Optimization
Elham Khoshbakht Sangcar; Farhad Namdari; Mysam Doostizadeh
Abstract
This paper studies the reciprocal impact of distribution network protection and reconfiguration of active Nowadays, with increasing energy consumption in distribution networks and the demand of consumers to buy highly reliable power, it is of high importance to establish adapt between protection and ...
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This paper studies the reciprocal impact of distribution network protection and reconfiguration of active Nowadays, with increasing energy consumption in distribution networks and the demand of consumers to buy highly reliable power, it is of high importance to establish adapt between protection and operation of the power system. distribution system. Accordingly, a distribution network reconfiguration is carried out to find the optimal switching operations in an economic way. Since the switching operations will change network topology as well as short circuit level of buses, the protection coordination may be invalid. To address this issue, constraints of the coordination of protection relays and fuses is being formulated and added to the reconfiguration problem. Moreover, the nonlinear equations of the problem are linearized and are transformed the reconfiguration problem into Mixed-Integer Linear Programming (MILP) one to achieve the global optimal solution. The proposed method has been implemented on 33 bus distribution network. The results clearly show effectiveness of the active and reactive power management in an intelligent distribution network considering protection concepts.
Optimization
Saman Hosseini-Hemati; Shahram Karimi; Gholam Hossein Sheisi
Abstract
Secure and economical operation of distribution networks needs the management of reactive power resources. Optimal Reactive Power Dispatch (ORPD) optimally manages the reactive power scheduling of generators and distribution generations as well as the Reactive Power Compensation (RPC) devices. This paper ...
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Secure and economical operation of distribution networks needs the management of reactive power resources. Optimal Reactive Power Dispatch (ORPD) optimally manages the reactive power scheduling of generators and distribution generations as well as the Reactive Power Compensation (RPC) devices. This paper investigates the effect of load models on the multi-objective ORPD problem in active distribution networks. Moreover, a modified Grey Wolf Optimizer (GWO), which is called in this paper as Civilized GWO (CGWO), is introduced to solve the ORPD problem. The proposed strategy including multi-objective function, various load models, DG’s reactive power, RPCs and the introduced CGWO, is tested on standard IEEE 33- and 69-bus distribution systems. The obtained results indicate the load models have significant impact on the cost function amount. Moreover, the performance of the proposed algorithm is evaluated using ten standard benchmark functions. The optimization results demonstrate the robustness of the introduced optimization algorithm and its ability in finding the better solutions compared to the Particle Swarm Optimization (PSO), Exchange Market Algorithm (EMA), and original GWO.
Optimization
Morteza Karimzadeh Parizi; Farshid Keynia; Amid Khatibi Bardsiri
Abstract
The Economic Dispatch (ED) is one of the most important optimization problems in power systems the ultimate goal of the ED is to minimize the cost of operations in a power generation. In this paper, the Woodpecker Mating Algorithm (WMA) is used to solve the ED problem considering the nonlinear properties ...
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The Economic Dispatch (ED) is one of the most important optimization problems in power systems the ultimate goal of the ED is to minimize the cost of operations in a power generation. In this paper, the Woodpecker Mating Algorithm (WMA) is used to solve the ED problem considering the nonlinear properties of generators such as valve point effects (VPE), prohibited operating zones (POZ), ramp rate limits, multiple fuel options, and transmission loss. The WMA algorithm is a novel metaheuristic algorithm inspired by the mating behavior of woodpeckers and sound intensity (a physical quantity). The WMA is implemented on six test systems of different operational dimensions and characteristics to show its capacity for solving the ED problem. The results are compared with the latest and most efficient methods introduced in the literature. Proving the efficiency of the WMA to solve the ED problem, simulation results are promising and offer the optimal fuel cost of production.
Optimization
Mina Salarian; Milad Niaz Azari; Mostafa Haji aghai
Abstract
One of the important issues in designing high-performance brushless direct current (BLDC) motors is reducing the cogging torque since it results in mechanical vibration, audible noises, and torque ripples, which adversely impact the performance of the motor, which is awkward high-accuracy applications. ...
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One of the important issues in designing high-performance brushless direct current (BLDC) motors is reducing the cogging torque since it results in mechanical vibration, audible noises, and torque ripples, which adversely impact the performance of the motor, which is awkward high-accuracy applications. This paper proposes an optimum design for BLDC motors aimed at reducing the cogging torque based on the capability of metaheuristics algorithms in finding the optimal solution. For this purpose, a simplified cogging torque equation is used as the objective function whose design variables include air gap length, magnet height, slot height, slot opening, and motor axial length. These are the five most influential parameters of cogging torque. On the other hand, we employ not only the old metaheuristics algorithms like the Genetic Algorithm (GA) and Simulated Annealing (SA) but also more recent algorithms such as Keshtel Algorithm (KA) along with the hybrid ones to benefit from their strength. The simulation is performed in the Matlab package. First, five selected optimization algorithms are applied and the results are investigated. The results of all the algorithms show a significant reduction in the cogging torque. Eventually, the proposed algorithms are compared to one another in terms of their value of cogging torque. The results show the superiority of the KASA algorithm in comparison with the others.
Optimization
Ehsan Jafari
Abstract
a new algorithm is presented to reduce the uncertainty effects of wind farms power generation (WFPG) and photo-voltaic generation (PVG) in both day-ahead energy and ancillary services markets. Firstly, this research tries to predict the uncertainty of short-term WFPG with acceptable accuracy. Indeed, ...
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a new algorithm is presented to reduce the uncertainty effects of wind farms power generation (WFPG) and photo-voltaic generation (PVG) in both day-ahead energy and ancillary services markets. Firstly, this research tries to predict the uncertainty of short-term WFPG with acceptable accuracy. Indeed, it uses the hybrid method of wavelet transform (WT) in order to reduce the fluctuations in the input historical data along with the improved artificial neural network (ANN) based on the nonlinear structure for better training and learning. In addition, regarding the high-level penetration of wind farms (WFs) on the power system, cascaded hydro units (CHUs) and pump-storage units (PSUs) are taken for the first time as supplementary units. Therefore, they are coordinated with WFs and photo-voltaic (PV) operations. Considering uncertainties of energy price, spinning and non-spinning reserves in the electricity market, WFPG, PVG and the availability of WFs, PV, CHUs and PSUs along with their effects on energy supply reliability lead to a scenario-based stochastic optimization problem. The aim of this problem is to increase the profit and decrease the financial risk (FR) of all of the units. The proposed method is implemented on WFs, PV, CHUs and PSUs of IEEE 118-bus standard system. Studying the results of profit and FR in the coordinated operation (CO) and the independent operation (IO) confirms that the profit is increased and the FR is reduced in the CO. Hence, the ability and merit of hybrid method of WT-ANN-ICA is verified.
Optimization
Javad Farzaneh; Ali Karsaz
Abstract
Maximum Power Point Tracking (MPPT) is an important concept for both uniform solar irradiance and Partial Shading Conditions (PSCs). The paper presents an Improved Salp Swarm Algorithm (ISSA) for MPPT under PSCs. The proposed method benefits a fast convergence speed in tracking the Maximum Power Point ...
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Maximum Power Point Tracking (MPPT) is an important concept for both uniform solar irradiance and Partial Shading Conditions (PSCs). The paper presents an Improved Salp Swarm Algorithm (ISSA) for MPPT under PSCs. The proposed method benefits a fast convergence speed in tracking the Maximum Power Point (MPP), in addition to overcoming the problems of conventional MPPT methods, such as failure to detect the Global MPP (GMPP) under PSCs, getting trapped in the local optima, and oscillations around the MPP. The proposed method is compared with original algorithms such as Perturbation and Observation (P&O) method (which is widely employed in MPPT applications), Differential Evolutionary (DE) algorithm, Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The obtained results show that the proposed method can detect and track the MPP in a very short time, and its accuracy outperforms the other methods in terms of detecting the GMPP. The proposed ISSA algorithm has a higher speed and the convergence rate than the other traditional algorithms.
Optimization
Hossein Sharifzadeh
Abstract
This paper presents a new solution method to efficiently handle non-convexity stemmed from valve points in the economic load dispatch problem. The proposed solution technique integrates both the advantage of fast solution algorithms of linear programming and powerful solution techniques of nonlinear ...
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This paper presents a new solution method to efficiently handle non-convexity stemmed from valve points in the economic load dispatch problem. The proposed solution technique integrates both the advantage of fast solution algorithms of linear programming and powerful solution techniques of nonlinear programming to find the global solution. In the first step of the proposed solution framework, non-convex terms are replaced by some linear segments and the new linear model solved by modern fats algorithms. In the second step, a nonlinear programming algorithm as a powerful local search algorithm solves the original non-convex model to improve the solution obtained in the previous step. By exploiting the main strength of linear and nonlinear programming algorithms, the proposed solution approach can quickly converge to nearly the global solution method. By experimental results on three test cases with different sizes, we show that the presented method outperforms the other algorithms published in the literature in the quality of the solution.
Optimization
Seyed Mahdi Hadad Baygi; Javad Farzaneh
Abstract
The idea of this paper is behind the development of sizing optimization model based on a new optimization algorithm to optimize the size of different stand-alone hybrid photovoltaic (PV)/wind turbine (WT)/battery system components to electrify a remote location including ten residential buildings located ...
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The idea of this paper is behind the development of sizing optimization model based on a new optimization algorithm to optimize the size of different stand-alone hybrid photovoltaic (PV)/wind turbine (WT)/battery system components to electrify a remote location including ten residential buildings located in Rafsanjan, Kerman, Iran. Then, the optimal system is estimated on the basis of various inconstant parameters related to the renewable energy system units: the number of batteries, occupied region by the turbine blades rotation, and occupied space by the group of solar panels. The solar radiation, ambient temperature, and wind velocity data are achieved from the website of renewable energy and energy efficiency organization of Iran. The ant lion optimizer is suggested to find the optimal values of the parameters for satisfying the electrical load demand in the most cost-effective way. The results obtained from the simulation illustrate that the off-grid PV/WT/battery hybrid power system is the more promising method to provide the electricity consumption of an urban location. To evaluate the performance of the proposed method, the simulation results are compared with other hybrid energy systems, which optimized by particle swarm optimization (PSO), harmony search (HS), firefly algorithm (FA), and differential evolutionary (DE) algorithm. The results obtained by the investigated algorithms show that the PV/WT/battery system that is optimized by ALO method is more economical in compared with PV/battery and WT/battery hybrid systems.
