University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 6 2 2015 08 23 Bidirectional Layer-by-layer Pre-training Method Bidirectional Layer-by-layer Pre-training Method 1 10 15420 FA Seyyede Zohreh Seyyedsalehi Faculty of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran 0000-0003-3413-398X Seyyed Ali Seyyedsalehi Faculty of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran Journal Article 2016 06 14 In this paper, a bidirectional pre-training method for initializing weights of hetero-associative deep neural network was presented. Training of deep neural networks, because of confrontation with a large number of local minima, is not often converged. This is while through proper initializing weights instead of random values at the beginning of the training; it is possible to avoid many local minima. The bidirectional layer-by-layer pre-training method pre-train weights in forward and backward manners in parallel. Afterwards, the weight values resulted from their training are applied in the deep neural network. The bidirectional layer-by-layer pre-training was applied for pre-training of the classifier deep neural network weights, and revealed that both the training speed and the recognition rate were improved in Bosphorus and CK+ databases. In this paper, a bidirectional pre-training method for initializing weights of hetero-associative deep neural network was presented. Training of deep neural networks, because of confrontation with a large number of local minima, is not often converged. This is while through proper initializing weights instead of random values at the beginning of the training; it is possible to avoid many local minima. The bidirectional layer-by-layer pre-training method pre-train weights in forward and backward manners in parallel. Afterwards, the weight values resulted from their training are applied in the deep neural network. The bidirectional layer-by-layer pre-training was applied for pre-training of the classifier deep neural network weights, and revealed that both the training speed and the recognition rate were improved in Bosphorus and CK+ databases. https://isee.ui.ac.ir/article_15420_1339bf81326fc7f4473bf62a547c94cc.pdf

University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 6 2 2015 08 23 Modeling of Smart Energy Management Systems in Microgrids using Analytical Hierarchy Process (AHP) Modeling of Smart Energy Management Systems in Microgrids using Analytical Hierarchy Process (AHP) 11 24 15419 FA Mojtaba Ahanch Shahram Jadid iran university of science and technology Journal Article 2016 06 14 Nowadays, Microgrids are considered as the basis of smart grids. Therefore, they are under the focus of researchers and specialists in the area of electrical engineering. If the operation of Microgrids is defined and implemented very well, rapid development of smart grids will be obtainable in the near future. In this paper, a new model of smart economical operation of Microgrids is presented which includes; forecasting toolbox for photovoltaic systems, energy storage system, distributed generation management and smart optimization center. With regard to the uncertainty of photovoltaic output, the forecasting toolbox informs the Microgrid’s operator of the power output of the photovoltaic for the short-term planning horizon (next 24 hours in this paper). Energy storage system is also employed during the planning horizon in order to help decrease operation cost of the Microgrid. Then, the smart optimization center, by utilizing a new decision making strategy (Analytical Hierarchy Process (AHP)) and by using Imperialist Competitive Algorithm (ICA), begins solving the unit commitment and economic dispatch problems for the purpose of declining operation cost of distributed resources and reducing the recourses’ emissions. Obtained results of different scenarios simulated on a test system approve efficacy of the proposed model.   Nowadays, Microgrids are considered as the basis of smart grids. Therefore, they are under the focus of researchers and specialists in the area of electrical engineering. If the operation of Microgrids is defined and implemented very well, rapid development of smart grids will be obtainable in the near future. In this paper, a new model of smart economical operation of Microgrids is presented which includes; forecasting toolbox for photovoltaic systems, energy storage system, distributed generation management and smart optimization center. With regard to the uncertainty of photovoltaic output, the forecasting toolbox informs the Microgrid’s operator of the power output of the photovoltaic for the short-term planning horizon (next 24 hours in this paper). Energy storage system is also employed during the planning horizon in order to help decrease operation cost of the Microgrid. Then, the smart optimization center, by utilizing a new decision making strategy (Analytical Hierarchy Process (AHP)) and by using Imperialist Competitive Algorithm (ICA), begins solving the unit commitment and economic dispatch problems for the purpose of declining operation cost of distributed resources and reducing the recourses’ emissions. Obtained results of different scenarios simulated on a test system approve efficacy of the proposed model.   https://isee.ui.ac.ir/article_15419_516111314ad6c0889cf18dc0aa005db9.pdf

University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 6 2 2015 08 23 Optimum Participation of DFIG-Based Wind Turbine in Short-Term Frequency Control Optimum Participation of DFIG-Based Wind Turbine in Short-Term Frequency Control 25 38 15423 FA Mostafa Abedini Dept. of Electrical Engineering, University of Isfahan, Isfahan, Iran Seyed Mohammad Madani Dept. of Electrical Engineering, University of Isfahan, Isfahan, Iran Amin Khodabakhshian Dept. of Electrical Engineering, University of Isfahan, Isfahan, Iran Journal Article 2016 06 14 Due to the increasing penetration of wind energy in power systems, various researches are performed on the effects of wind plants on power system operation.  This article briefly explains: the frequency control principles of traditional power plants, the structure and the connection of wind turbines to the power grid and the operation of wind turbines after frequency disturbances. Several methods have been suggested for the contribution of wind turbines in short-term frequency control. Then, one the latest methods in this field is modified and used. The presented method applies an intelligent algorithm and achieves a beetr frequency response.   Due to the increasing penetration of wind energy in power systems, various researches are performed on the effects of wind plants on power system operation.  This article briefly explains: the frequency control principles of traditional power plants, the structure and the connection of wind turbines to the power grid and the operation of wind turbines after frequency disturbances. Several methods have been suggested for the contribution of wind turbines in short-term frequency control. Then, one the latest methods in this field is modified and used. The presented method applies an intelligent algorithm and achieves a beetr frequency response.   https://isee.ui.ac.ir/article_15423_f63550cceb80ffb5296555f6c79ee313.pdf

University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 6 2 2015 08 23 Boost converter control by using of FOPID optimized by ICA Boost converter control by using of FOPID optimized by ICA 39 48 15418 FA Saber Falahati Ali Abadi Dept. of Electrical and Computer Engineering, University of Kashan, Kashan, Iran Abbas Ketabi Dept. of Electrical and Computer Engineering, University of Kashan, Kashan, Iran Masoud Haji Akbari Fini Dept. of Electrical Engineering, Isfahan University of Technology Journal Article 2016 06 14 This paper proposes a new method for designing fractional order PID to control boost converter. FOPID is a PID controller where the integration and derivation orders are of fractional order rather than integer. In this paper FOPID controller is used for controlling the boost converter, and Imperialist Competitive Algorithm is employed to determine FOPID parameters because of its good performance and high accuracy. To illustrate the performance of the proposed controller, some simulations have been carried out in MATLAB and the results have been compared with Genetic algorithm.  Moreover, FOPID controller has been compared with PID and PI controllers optimized by ICA. The Simulation results illustrate the good performance of the proposed controller. This paper proposes a new method for designing fractional order PID to control boost converter. FOPID is a PID controller where the integration and derivation orders are of fractional order rather than integer. In this paper FOPID controller is used for controlling the boost converter, and Imperialist Competitive Algorithm is employed to determine FOPID parameters because of its good performance and high accuracy. To illustrate the performance of the proposed controller, some simulations have been carried out in MATLAB and the results have been compared with Genetic algorithm.  Moreover, FOPID controller has been compared with PID and PI controllers optimized by ICA. The Simulation results illustrate the good performance of the proposed controller. https://isee.ui.ac.ir/article_15418_b48bc32b9585c915d3111bc26f1696fd.pdf

University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 6 2 2015 08 23 Reducing the Static Energy Consumption using Heterogeneous Multi-FPGU System Reducing the Static Energy Consumption using Heterogeneous Multi-FPGU System 49 58 15425 FA Mohsen Kiani 2 Dept. of Computer Engineering, University of Razi, Kermanshah, Iran Abdollah Chalechale Dept. of Computer Engineering, University of Razi, Kermanshah, Iran Journal Article 2016 06 14 Multi-FPGA systems are an alternative to the reconfiguration limitation of a single FPGA, when a number of real-time tasks arrive together and must be scheduled and executed before a specified deadline. Energy consumption, consisting of static and dynamic, is an important factor in such systems, especially when used as battery powered applications. In this paper, heterogeneous multi-FPGA system is proposed to reduce the static energy consumption of a multi-FPGA system. Ant colony optimization (ACO) is used to schedule the real-time tasks that periodically enter the system. The number of these tasks, as the case of many applications, supposed to be different from one period to another. The consumption energy is estimated for both the homogeneous and heterogeneous systems. Results show that the heterogeneous system saves 6.44 percent of energy, compared with the homogeneous system. If the number of tasks, and so the required number of blocks are small, this amount could be much higher. Multi-FPGA systems are an alternative to the reconfiguration limitation of a single FPGA, when a number of real-time tasks arrive together and must be scheduled and executed before a specified deadline. Energy consumption, consisting of static and dynamic, is an important factor in such systems, especially when used as battery powered applications. In this paper, heterogeneous multi-FPGA system is proposed to reduce the static energy consumption of a multi-FPGA system. Ant colony optimization (ACO) is used to schedule the real-time tasks that periodically enter the system. The number of these tasks, as the case of many applications, supposed to be different from one period to another. The consumption energy is estimated for both the homogeneous and heterogeneous systems. Results show that the heterogeneous system saves 6.44 percent of energy, compared with the homogeneous system. If the number of tasks, and so the required number of blocks are small, this amount could be much higher. https://isee.ui.ac.ir/article_15425_57bb38ed738be62ed4df0e10edea1fa4.pdf

University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 6 2 2015 08 23 Using Imperialist Competitve Algorithm for Secured loadability enhancement with TCSC in transmission system for pool and hybrid model Using Imperialist Competitve Algorithm for Secured loadability enhancement with TCSC in transmission system for pool and hybrid model 59 74 15421 FA Ehsan Afzalan 2 Dep. of Electrical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran Mahmood Joorabian 2 Dep. of Electrical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran Journal Article 2016 06 14 This paper proposes an algorithm for the optimal location and control of Flexible AC Transmission System (FACTS) devices for enhancing the loadability in transmission system using particle swarm optimization (PSO) and Imperialist Competitve Algorithm (ICA) for pool and hybrid model in deregulated electricity market. This approach uses AC load flow equations with the constraints on power generation, transmission line flow, magnitude of bus voltages and FACTS device settings. The bilateral transactions are modeled using secured bilateral transaction matrix utilizing AC distribution factor with the slack bus contribution. In this proposed approach, Thyristor Controlled Series Compensator (TCSC) is used. To validate the proposed approach, simulations are performed on 39-bus New England test system and IEEE 118-bus system. Comparisons are made in terms of maximum loadability, computation time. The simulation results obtained indicate that by optimal location and control of TCSC, using ICA, the loadability in transmission system is enhanced and a less computation time is achieved. The comparative study concludes that by the optimal location and control of TCSC using ICA method the secured loadability enhancement is obtained in the transmission system in deregulated electricity market. This paper proposes an algorithm for the optimal location and control of Flexible AC Transmission System (FACTS) devices for enhancing the loadability in transmission system using particle swarm optimization (PSO) and Imperialist Competitve Algorithm (ICA) for pool and hybrid model in deregulated electricity market. This approach uses AC load flow equations with the constraints on power generation, transmission line flow, magnitude of bus voltages and FACTS device settings. The bilateral transactions are modeled using secured bilateral transaction matrix utilizing AC distribution factor with the slack bus contribution. In this proposed approach, Thyristor Controlled Series Compensator (TCSC) is used. To validate the proposed approach, simulations are performed on 39-bus New England test system and IEEE 118-bus system. Comparisons are made in terms of maximum loadability, computation time. The simulation results obtained indicate that by optimal location and control of TCSC, using ICA, the loadability in transmission system is enhanced and a less computation time is achieved. The comparative study concludes that by the optimal location and control of TCSC using ICA method the secured loadability enhancement is obtained in the transmission system in deregulated electricity market. https://isee.ui.ac.ir/article_15421_699dd2387154e1d7f00f02f0722a87e9.pdf

University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 6 2 2015 08 23 Nonlinear Adaptive Neural Identifier Filter with Optimal Learning Rate for Converging of Parameters based on Gradient Descent Nonlinear Adaptive Neural Identifier Filter with Optimal Learning Rate for Converging of Parameters based on Gradient Descent 75 86 15424 FA Fatemeh Alibakhshi Dept. of Electrical Engineering, Islamic Azad University of Tehran, Tehran, Iran Mohammad Teshnehlab Faculty of Electrical Engineering, K.N.T University of technology,Tehran, Iran Mehdi Alibakhshi Dept. of Electrical Engineering, Islamic Azad University of Tehran, Tehran, Iran Mohammad Mansouri Faculty of Electrical Engineering, K.N.T University of technology,Tehran, Iran Journal Article 2016 06 14 The convergence of learning rate in neural networks identifier and controller is one of challenging issues which attracts great interest from researchers.  This paper suggests the adaptive gradient descent algorithm with learning laws which assures the convergence of multi-layer perceptron neural network based on Taylor series expansion of output error. In the proposed method the learning rate can be calculated online. To increase the accuracy and the speed of convergence, the second and higher order terms of the Taylor series expansion are not considered constant and are updated during the algorithm. Simulating the suggested algorithm on two examples reveals that with considering the bounds in the proposed method, the aims for learning rate, convergence of learning algorithm are guaranteed and the speed of convergence of training algorithm is increased. The convergence of learning rate in neural networks identifier and controller is one of challenging issues which attracts great interest from researchers.  This paper suggests the adaptive gradient descent algorithm with learning laws which assures the convergence of multi-layer perceptron neural network based on Taylor series expansion of output error. In the proposed method the learning rate can be calculated online. To increase the accuracy and the speed of convergence, the second and higher order terms of the Taylor series expansion are not considered constant and are updated during the algorithm. Simulating the suggested algorithm on two examples reveals that with considering the bounds in the proposed method, the aims for learning rate, convergence of learning algorithm are guaranteed and the speed of convergence of training algorithm is increased. https://isee.ui.ac.ir/article_15424_132fe7cd24bd9b488a52e3a42c0cd513.pdf

University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 6 2 2015 08 23 An Applied Method to Online Recognition of Farsi Handwritten Isolated Characters Using Knowledge of Main Body and Tiny Movements Simultaneously An Applied Method to Online Recognition of Farsi Handwritten Isolated Characters Using Knowledge of Main Body and Tiny Movements Simultaneously 87 100 15422 FA Majid Marzani , Dept. of Electrical and Computer Engineering, University of Birjand, Birjand, Iran Mohammad Razavi , Dept. of Electrical and Computer Engineering, University of Birjand, Birjand, Iran Mehran Taghipour Gorjikolaie , Dept. of Electrical and Computer Engineering, University of Birjand, Birjand, Iran Journal Article 2016 06 14 In this paper, a method is presented to online Farsi handwritten isolated characters. In the proposed method, the information of main body and tiny movements are simultaneously used to improve the validation of output class recognition. Farsi handwritten isolated characters are categorized in 18 groups based on similarity in main body and also 11 groups based on tiny movement. According to the proposed method in this paper, the main body and tiny movements are recognized to identify unknown input characters. If detected groups from main body and tiny movements are corresponded, the unknown character is recognized; otherwise this mistake will be corrected by correction algorithm, as much as possible. In this paper, point features and global features are extracted from main body.  Principle Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are applied to reduce computational burden and to increase the quality of features. Using PCA and LDA, feature dimension is reduced from 102 to 17 for main body. One Versus One (OVO) approach of Support Vector Machine (SVM) classifier is used to classify the main body of characters and also tiny movements. The obtained results show that by using the proposed method; about 98 percent of online Farsi handwritten isolated characters are correctly recognized. In this paper, a method is presented to online Farsi handwritten isolated characters. In the proposed method, the information of main body and tiny movements are simultaneously used to improve the validation of output class recognition. Farsi handwritten isolated characters are categorized in 18 groups based on similarity in main body and also 11 groups based on tiny movement. According to the proposed method in this paper, the main body and tiny movements are recognized to identify unknown input characters. If detected groups from main body and tiny movements are corresponded, the unknown character is recognized; otherwise this mistake will be corrected by correction algorithm, as much as possible. In this paper, point features and global features are extracted from main body.  Principle Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are applied to reduce computational burden and to increase the quality of features. Using PCA and LDA, feature dimension is reduced from 102 to 17 for main body. One Versus One (OVO) approach of Support Vector Machine (SVM) classifier is used to classify the main body of characters and also tiny movements. The obtained results show that by using the proposed method; about 98 percent of online Farsi handwritten isolated characters are correctly recognized. https://isee.ui.ac.ir/article_15422_fa954ee4f3ad0ab6a584475da7df4b95.pdf