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Photovoltaic (PV) panel produces electricity depending on a variety of characteristics, including the PV module model, design specifications, and ambient circumstances such as temperature and sun irradiation. To analyze and model the effect of these factors on PV performance, a PV model is significant to be studied and modeled in advance. It is desirable to be compatible with the real-physical behavior of the PV panel. This paper presents mathematical modeling, design, and simulation of the three-diode model (3DM) MPPT controller instead of using conventional single/double diode PV models. The proposed PV model is analyzed, verified, and simulated at various temperature and irradiance levels. Furthermore, Particle Swarm Optimization (PSO) as a multi-objective algorithm is used for the Maximum Power Point Tracking MPPT controller to enhance the performance of the module and PV array system. A DC/DC boost converter is combined with the proposed 3DM model and connected through a resistive load. Results show that adopting PSO-based MPPT improves the performance of the PV panel compared to the traditional MPPT and verified the theoretical background. [ABSTRACT FROM AUTHOR]

Nowadays, sea wave energy is widely regarded as an energy source that is clean, renewable and highly available for power extraction. The subject of extracting the maximum power from sea waves in Iran is of great importance due to access to the Caspian Sea and the Persian Gulf. Moreover, there is a need for resources with no air pollution besides providing a part of the country's power demand without costly infrastructure, so this research field is highly interesting although has rarely been addressed so far. The main purpose of this paper is to use an appropriate control strategy to improve the performance of point absorbers. In this scheme, to consider high uncertainty in the parameters of the power take-off system in different atmospheric conditions and improve controller performance, a new improved black hole algorithm is introduced to tune fuzzy controller parameters. The proposed method is then implemented for tuning the fuzzy controller parameters in order to obtain the maximum power capture of wave energy converters. Compared to particle swarm optimization and conventional black hole algorithm, the results of the proposed method indicate enhancements in reference velocity tracking and absorbed power. Finally, some simulations are performed and the proposed controller is implemented for the wave spectrum of the Persian Gulf waters, so the performance of the proposed controller is evaluated. [ABSTRACT FROM AUTHOR]

Penetration of distributed generation resources including wind power and solar photovoltaic units in distribution system has been increased, and it is important to examine their impact on the distribution systems' operation in term of reliability. In this paper, the multi-objective dynamic feeder reconfiguration is introduced as an efficient approach for providing an energy management schedule in the distribution grid considering energy loss and energy not supplied as the objective functions in the presence of renewable energy sources and capacitor units. In addition, the effect of uncertainty related to power demand is considered in the evaluations. To this end, an enhanced particle swarm optimization algorithm is provided in this paper, the proposed approach is applied to the 33-node testing system. [ABSTRACT FROM AUTHOR]

DGs and capacitor banks are installed to optimize the performance of many distribution networks. Typically, the problem of optimizing the overall performance of the distribution network is examined with multi-objective purposes. Network optimization purposes are usually varied and sometimes contradictory. Therefore, the problem search space is very large due to the variety of purposes. This paper presents a modified Pareto local search function for optimal placement of DGs and capacitor banks. To limit the search space and find Pareto points, a new combination method including Pareto chart and a weight function has been used. The optimal operation of the distribution network is performed by three single objective functions related to the voltage stability index, voltage profile of buses and power loss. In this method, a modified per-unit system is presented to align single objective functions and their weighting coefficients. The network is studied with three different loads. So that, the network is examined in the final stage by increasing the load and reaching bus voltage stability margins. The particle swarm optimization method is applied to solving placement problems. In addition, locating and sizing DG and capacitor banks, tap setting of on load tap changer transformer is adjusted by the proposed method. To show the effectiveness of the purposed method, simulations are applied to 69 bus radial system. The results indicated the favorable advantage of the proposed method to improve the overall performance of the distribution network. [ABSTRACT FROM AUTHOR]

This paper objective presents static synchronous series compensation FACTS device with biogeography based optimization (BBO) to deal for obtaining worthwhile power flow control. The biogeography based optimization method is utilized to find the optimal fitted child sets by surviving parents with the help of migration and mutation Jaipur process. The present BBO technique from the evolutionary strategy with static synchronous series compensator provides improved outcomes in comparison to other optimization methods. The simulation outcomes illustrate that the proposed BBO algorithm is efficacious, secure and correct to search the optimized values with SSSC based FACTS devices. The proposed method is considering the solution quality an optimistic substitute method for extricating the OPF problems. The simplification and effectiveness of this method are validated on the IEEE 57 bus and 75 bus Systems. In this paper, from the outcome results, it is clearly shown that the proposed technique execution can be properly and effectively applied to the optimal position of multiple OPF problems. i.e. BBO based algorithm with SSSC FACTS device is found better results when compared to without SSSC device in all aspects. [ABSTRACT FROM AUTHOR]

In this paper Fuzzy-FOPID controller is used in order to control automatic voltage regulator (AVR) system. This controller has six parameters. In this paper optimization method is employed to determine the controller parameters. For this purpose, imperialist competitive algorithm (ICA) is used for its high accuracy and good performance. To illustrate performance of proposed controller, some simulations are carried out in MATALB and the results are compared with FOPID controller results. Good performance of proposed controller is validated by results of simulations. [ABSTRACT FROM AUTHOR]

This paper presents an AC Optimal Power flow (AC-OPF) problem of a power system, considering wind energy. Wind energy is an environmental-friendly energy source to produce electrical power and it includes less operating costs compared with other sources of electrical power production. Wind generators also affect the operation cost of a power system as well as transmission losses, based on generators locations and speed of wind. In addition, wind speed is a parameter with uncertainty and considering this uncertainty is an important issue in operation of wind generators in the AC-OPF problem. The proposed AC-OPF formulation includes the integer variables in addition to continuous variables and studies the effects of wind energy, transformer tap settings, and shunt capacitors on fuel cost, transmission losses as well as up and down spinning reserves. To solve the AC-OPF model, an Improved Particle Swarm Optimization (IPSO) is presented. The IPSO algorithm in this work includes velocity mirror effect that causes improvement in the quality of the results. The proposed method is applied on modified IEEE 30 bus test system, and obtained results approve the validity and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, robotic manipulators are the key industry requirement. These have find the importance to enhance the productivity as well as accuracy. Furthermore, industries are also moving towards the use of Flexible Link Manipulator (FLM) owing to their unique characteristics i.e. light weight, high speed operations, and the larger workspace. The FLM system has flexibility of link that causes vibrations and oscillations which affect adversary to the performance of robotic arm. The performance of FLM system is measured w.r.t. minimum error and oscillations in trajectory tracking. In this research paper, an attempt has been made to overcome the complications of FLM system. A full state feedback Linear Quadratic Regulator (LQR), is designed for FLM. It is observed that the designed controller can enhance the accuracy of the robotic arm, while reducing oscillations and vibrations. In addition, to enhance the performance of controller and to reduce the hassle in terms of selecting the parameter of Q matrix in LQR, modified particle swarm optimization (m- PSO) is used. The effectiveness of designed controller is simulated in MATLAB. Further, the validation of designed controller is tested on hardware FLM device. The results obtained from the simulation and hardware are compared. [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological disorder occurs at the central nervous system, Electroencephalography (EEG) is the reliable tool for analysing the human brain activity with the help of the signals, moreover, it plays a significant role in the detection of epileptic seizures. The abnormal electrical discharge leads to loss of memory; from the recent survey over five crore people are affected by epilepsy. An effective detection system is a vital solution for detecting the epileptic disease in the initial stage. In this paper, an improved epilepsy seizure detecting system is developed with a better accuracy; the EEG signal in both time and frequency domain with the use of Discrete Stationary wavelet-based Stockwell transform (DSWST) is proposed. The feature extraction is processed by a temporal feature, spectral feature and Amplitude Distribution Estimation (ADE) from EEG signals in which the normal EEG signals will have various spectral and temporal centroids. Also, a modified filter bank based particle swarm optimization (MF-PSO) helps for the feature selection; it significantly improves the classifier accuracy. Finally, a Hybrid K nearest support vector machine (Kn-SVM) is employed for classification to investigate the performance of feature to classify the brain signals into three groups of normal (healthy), seizure free (inter-ictal) and during a seizure (ictal) groups. [ABSTRACT FROM AUTHOR]

Designing nonlinear optimal controllers such as Minimum Variance Controller (MVC) has many difficulties. Main difficulties are 1) in order to design controller; the explicit relations between outputs and inputs must be executable. This relation is defined as implicitly in the nonlinear models; 2) learning controller is a high dimensional-multimodal optimization task and search space can be extremely rugged and has many local minima. In this paper, in order to overcome these disadvantages, the model-free optimal controller scheme is utilized. In a model-free controller, as the system model is not available, the gradient of the cost function cannot be executed. Instead, in this paper, a relation between gradient of the controller with gradient of the system model is derived by inverse lemma. The controller structure is selected to be neural network. Then, the gradient based PSO (GPSO) is proposed to be a learning controller. GPSO has both advantages of global searching and convergence properties. The application of the methodology to the empirical CSTR model indicates that this approach gives very credible estimates of the controller. The simulation results indicate that the proposed method can be more accurate than existing methods. [ABSTRACT FROM AUTHOR]

In this paper, thin dipole is investigated in the 3.1-10.6 GHz Ultra-Wide Band frequency range. The study includes the design of half wave dipole antenna with different dipole radii to improve the antenna characteristics. Antenna parameters including return loss, Voltage Standing Wave Ratio, -10 dB bandwidth and radiation pattern are examined according to Federal Communication Commission regulations and Ultra-Wide Band system requirements in this frequency range. Additionally, two optimization techniques, Genetic Algorithm and Particle Swarm Optimization are applied to obtain the best dipole radius. CST Microwave Studio, an electromagnetic simulation program, is used for the design of the antenna structure and the simulation purposes. The simulation is performed in transient mode. The results show that the antenna performance can be improved by selecting the suitable antenna dimensions. Additionally, Genetic Algorithm and Particle Swarm Optimization techniques are compared in terms of time consumption to obtain the best VSWR and return loss values. The results also depict that Genetic Algorithm yields better results for the proposed structure. [ABSTRACT FROM AUTHOR]

Interline power flow controller (IPFC) is a concept of AC flexible control of the transmission system (FACTS), with the ability to series compensation and power flow management in multi lines of a post power. Out of all FACTS devices, interline power flow converter (IPFC) is considered to be most flexible, powerful and versatile. IPFC has the capability of compensating multi-transmission line. The proper placement of interline power flow controller (IPFC) can improve the transmission line congestion problem to a great extent. This paper proposes the optimal location of the IPFC in electrical power systems, using the particle swarm optimization algorithm. Expression of sample figure and analysis of the sample system show that IPFC is effective to minimize the power losses in the power system. [ABSTRACT FROM AUTHOR]

This article presents a network feeder reconfiguration in balanced distribution networks using Multi Verse Optimization (MVO) to optimize the total network power losses and reduce emissions by means of step by step switching. Reconfiguration is a considerable manner of altering the power flows through the lines from the main substation to load ends, while maintaining radial structure. The main objective of this paper is to solve feeder reconfiguration problem to reduce the total line losses and emission reduction for an open loop distribution system. MVO is a population based method to resolve the network reconfiguration problem. A precise power flow solution is applied and the objective is formulated. A nature inspired Multi Verse Optimization is utilized to restructure the power distribution system and identify the optimal tie switches for lower line losses in the distribution network. The reduction of resistive losses leads to reduction of emissions. The suggested MVO method has carried out on two standard 16-node and 69-node distribution systems for normal load and overload conditions and results show the performance of the anticipated MVO method. The final outcomes prove a significant reduction in real power losses and emissions. [ABSTRACT FROM AUTHOR]

In this paper optimum size and location of distributed generators (DGs) are determined for maximizing voltage profile and minimizing line losses in distribution systems. For this purpose, Particle Swarm Optimization algorithm (PSO) approach is proposed. The significant innovation of this research paper is using new coding in (PSO) which includes both active and reactive powers of DGs to achieve better voltage profile improvement and line loss reduction. Furthermore, four sets of weighting factors are chosen based on the importance and criticality of the different loads. The effectiveness of the proposed method is examined in the 33 bus distribution systems. The results show that determination of optimum size and location of DGs has a considerable effect on voltage profile improvement and line loss reduction. [ABSTRACT FROM AUTHOR]

The topic of power allocation in multiple-input-multiple-output )MIMO( systems for wireless communication in order to reach high capacity or low bit error rate has gained some attention. In this paper, heuristic algorithms, including genetic and particle swarm optimization algorithms, are applied to find the optimal power allocation for achieving best capacity benefit. Two cases of un-polarized and cross-polarized antennas of spatial MIMO channel modeling are studied. We demonstrate that the performance of genetic and particle swarm optimization algorithms is optimal compared with a perfect search at a reduced computational complexity. These algorithms have fast convergence and can handle large number of sub-channels without performance degradation. Both simulation and numerical results confirm that, compared to other mathematical methods, the proposed algorithms are more efficient in terms of complexity and power assignment to antennas in a MIMO system. [ABSTRACT FROM AUTHOR]

In restructured electricity markets, the consumers have various procurement strategies to supply their electricity demand from alternative resources such as self-generating facility, bilateral contracts and pool market purchase. A hybrid approach based on binary particle swarm optimization (BPSO) and binary imperialist competitive algorithm (BICA) is proposed in this paper to find optimal procurement for large consumers with multiple procurement options. The solution of these problems provides adequate information to obtain an electricity procurement problem for large consumers. Also, the results are compared with ICA and PSO methods. Test results show that the proposed hybrid approach is more effective and has higher capability in finding the optimum solutions in comparison with ICA and PSO methods. A case study is used to illustrate the efficiency of the proposed hybrid approach. [ABSTRACT FROM AUTHOR]

In this paper a locomotion control system for bipedal robot is proposed to provide desirable walking on a terrain and skipping over a pitfall preventing the robot from falling in it. The proposed strategy is a combination of motion optimization based on particle swarm optimization algorithm and utilization of mode switching at the higher level controller. The model for bipedal robot is a compass gait model but the presented method is general and could be appropriately extended and generalized for other complicated models. Principles of minimalistic designs are also respected and simple central pattern generator and simple mechanical feedback control are used to produce and maintain desirable motion patterns of the robot. [ABSTRACT FROM AUTHOR]

This paper presents a new method for optimal multi-objective reconfiguration of distribution system based on the novel Intelligent Water Drops (IWD) algorithm in order to mitigation of losses, improving the voltage profile and equalizing the feeder load balancing. The proposed method is validated using the IEEE33-bus test system and a Tai-Power 11.4 KV distribution system as a real distribution network. Also to assess the performance of the proposed method at different load levels, the two test networks are simulated at three load levels: light, nominal, and heavy. The results show that multi objective reconfiguration using Intelligent Water Drops leads to improvement in all objectives at various load levels. The obtained results prove that the proposed technique can be more accurate than other well known methods such as Genetic Algorithm, Particle Swarm Optimization, and Harmony Search algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the multi-machine power system is simulated and in addition, VURPSO optimization algorithm is used to optimize the parameters of the simultaneous controllers of the Static Var Compensator (SVC) and power system stabilizer (PSS) to increase the power system stability. By SVC, the transmittable power in the steady state can be increased and the voltage profile can be controlled along the line. The main role of the PSS is to damp the generator oscillations controlling its stimulation by the use of auxiliary supplementary signals. If it is assumed that a three phase short circuit fault with the ground has occurred in a four-machine power system, SVC and PSS simultaneous controllers are used to damp the oscillations and stabilize the system. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid of Neural Network (NN) and Fast Traversal Filter (FTF) based controller in each area is used to determine the optimal parameters of Load Frequency Control (LFC) of a realistic two area power system. The two area power system is modeled considering the various non -linearities like governor dead band, generation rate constraint (GRC) and boiler dynamics. Input to the controller i.e. the error signal is divided into two parts- linear and non- linear. The linear part of the input signal is minimized by the FTF algorithm, whereas the non- linear part is minimized by the NN algorithm. The output of the controller is the sum of the outputs of NN and FTF networks. The proposed hybrid controller requires less number of samples for training of weights, thus making the system fast. This is highly desirable in power quality problems. The various components of power system are reduced to transfer functions and the system performance is analyzed for 1% step load perturbation in area1 with different controllersproportional and integral (PI), neural network (NN) and NN+FTF based controllers. The simulations demonstrate the fast and smooth performance of the power system with the proposed controller. Simulated results evince the superiority of the proposed hybrid controller. [ABSTRACT FROM AUTHOR]

Since the search process of the particle swarm optimization (PSO) technique is non-linear and very complicated, it is hard if not impossible, to mathematically model the search process and dynamically adjust the PSO parameters. Thus, already some fuzzy systems proposed to control the important structural parameters of basic PSO. However, in those researches no effort were reported for optimizing the structural parameters of the designed fuzzy controller. In this paper, a new algorithm called Fuzzy Optimum PSO (FOPSO) has been introduced. FOPSO utilizes two optimized fuzzy systems for optimal controlling the main parameters of basic PSO. Extensive experimental results on many benchmark functions with different dimensions show that the powerfulness and effectiveness of the proposed FOPSO outperforms other versions of PSO. [ABSTRACT FROM AUTHOR]

This paper incorporates the SSSC FACTS device in optimal power flow solutions to enhance the performance of the power systems. The particle swarm optimization is used for solving the optimal power flow problem for steady-state studies. The effectiveness of the proposed approach was tested on IEEE 14-bus and IEEE 30-bus systems with SSSC FACTS device. Results show that the proposed PSO algorithm gives better solution to enhance the system performance with SSSC device compared to without SSSC device. [ABSTRACT FROM AUTHOR]

Electricity market plays an important role in improving the economics of electrical power system. Transmission network is vital entity in an open access restructured electricity market. Whenever transmission network congestion occurs in an electricity market, it divides the market in different zones and the trading price of electricity will no longer remains the same for the whole system. Bidding strategies in an electricity market, where by changing the bid, market player changes the revenue of every participant of the market. In this paper, the bidding strategy problem with congestion management is modeled as an optimization problem and solved using Particle Swarm Optimization (PSO). Search procedure of PSO is based on the concept of combined effect of cognitive and social learning of the members in a group. The effectiveness of the proposed method is tested with a numerical example and the results are compared with Genetic Algorithm (GA) approach. The results shows that PSO takes less computational time and maximizing the social welfare compared to GA approach. [ABSTRACT FROM AUTHOR]

Relevance feedback (RF) approaches have been used to improve the performance of content-based image retrieval (CBIR) systems. In this paper, an RF approach based on modification of similarity measure using particle swarm optimization (PSO) in a medical X-ray image retrieval system is proposed. In this algorithm, using PSO, the significance of each feature in the similarity measure is modified for image retrieval. This modification causes that good features have major effect in relevant image retrieval. The defined fitness function in PSO uses relevant and irrelevant retrieved images with different strategies, simultaneously. The relevant and irrelevant images are used to exhort and penalize similarity measure, respectively. The proposed RF is integrated to a CBIR system based on semantic classification for evaluation. In this system, using merging scheme in a hierarchical structure, the overlapped classes are merged together and determined search space for each query image. The proposed RF evaluated on a database consisting of 10000 medical X-ray images of 57 classes. The proposed algorithm provides the improvement, effectiveness more than those reported before. [ABSTRACT FROM AUTHOR]