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The aim of the paper is to simultaneously clear the energy and spinning reserve market for deregulated system involving uncertainties in wind generation. The volatility of wind is represented by Weibull density function (WDF). The objective in the paper is to minimize the total cost which involves cost of energy supplied by thermal and wind generating units, cost of reserve procured by thermal units, and cost owing to overestimation and underestimation of wind generation. The optimization is carried out using gravitational search algorithm (GSA). The efficacy of the presented model is investigated on IEEE 30-bus power system.

In this paper the forward market clearing of wind integrated power system considering the randomness in wind power generation is presented. The intermittent nature of wind power is characterized by Weibull probability density function (PDF). The aim is to minimize the cost of energy supplied by thermal and wind generators. The cost of energy also incorporates overestimation and underestimation cost of available wind power. Gravitational search algorithm (GSA) based scheduling is utilized for the market clearing purpose. The results have been obtained on IEEE 30-bus and IEEE 57-bus test systems to investigate the effectiveness of the presented algorithm for optimal scheduling.

In this work the optimal allocation of distinct types of Dispersed Generators is determined using both single objective and multi-objective optimization. The voltage deviation index and active power loss index are taken as objectives for the optimization. For the multi-objective optimization, fuzzy memberships are assigned to these indices and the subsequent membership is optimized. The above formulation is implemented under Particle Swarm Optimization to attain the optimum capacity and location of Dispersed Generators. The potency of the proposed approach is investigated on 33-bus radial distribution system considering practical voltage dependent loads. It is found that the optimum allocation of Dispersed Generators improves the voltage profile of the system as well as reduces the losses.

This paper deals with a multi-objective solution for power dispatch complexity. This complexity is represented in terms of a nonlinear constraints based optimization considering real power losses, operational cost (Fuel Cost) of generation, and emissions (like NOx, COx and SOx) are to be minimized simultaneously. An approach based on Non-Dominated Sorting Genetic Algorithm (NSGA-II) has been presented in this paper to tackle the problem. It serves as a true multi-objective solution for complex and precise objective. Moreover solution to such problem has been solved by fuzzy set theory approach extorting one of the Pareto optimal solution as the optimal solution. This multi-objective algorithm is applied on the six generators test system. The obtained results are viable and have potential to solve multi-objective power dispatch problem by providing well distributed Pareto optimal front.

In this paper, the optimum operation of single winding single phase self-excited induction generator has been investigated with series and shunt capacitor configurations. The optimum capacitors values for different configurations are obtained using the genetic algorithm for optimum voltage regulation. The optimization is carried out under MATLAB where fsolve is used to find the magnetizing reactance and generated frequency for optimum capacitances and given prime mover speed. With such optimal values of capacitors, the steady state performance of single winding self-excited induction generator found satisfactory and similar to two winding self-excited induction generator.

In this paper, the optimum operation of two winding single phase self-excited induction generator has been investigated with different capacitor topologies. The optimum values of the capacitors for different configurations are obtained using the genetic algorithm for optimum voltage regulation. The optimization is carried out under MATLAB where fsolve is used to find the magnetizing reactance and generated frequency for optimum capacitances and given prime mover speed. With such optimal values of capacitors, the performance of different configurations becomes comparable.

This paper proposes Z (Impedance)-source inverter for single phase grid connected photovoltaic system. Unlike traditional voltage source inverter, Z-source inverter uses shoot-through (ST) duty cycle and modulation index to control output voltage. By modifying the traditional pulse width modulation (PWM) techniques, shoot through duty is accommodated to buck or boost the input voltage. Z-source network provides single stage power conversion. This paper summarizes brief calculation for circuit capacitors and inductors. The design parameter are validated by simulating the performance of an example photovoltaic (PV) module under MATLAB/Simulink environment.

Owing to the continuous growth of nonlinear loads and penetration of distributed generation (DG) plants in the power system, the harmonics are increasing at an alarming rate. The harmonics generated at one location can affect the performance of other components because of the interconnected nature of power system and even affecting the performance of harmonic generating sources. Therefore, it is important to locate non linear loads for placing the harmonic mitigation devices. In this paper, the locations of harmonic sources are identified by the direction of power flow. The required harmonic current injection data is obtained by the phasor measurement units (PMUs). The placement of PMUs is costly and needs communication system at every substation. The optimal placement of PMUs is carried out using genetic algorithm (GA) and effectiveness of the method is studied on IEEE 69-bus system.

In restructured power market, congestion arises due to inadequate transmission capability to hold every physical or operational constraints of the transmission line, simultaneously. In order to relieve congestion, series FACTS devices like thyristor controlled series compensator (TCSC) may be used as an effective tool for reducing the power flows in overloaded lines. In this paper, two methods are used for the installation of TCSC to alleviate congestion namely, locational marginal pricing difference method and congestion rent contribution method. Congestion rent is given by the product of LMP difference and power flows. LMP mechanism is one of the most commonly employed tools for the market settlement in deregulated power system environment. It has become a popular method to identify the nodal prices and regulate transmission congestion. The analysis of LMP by an iterative DC optimal power flow with the fictitious nodal demand model is presented in the paper. Here, LMPs at all buses considering generator bids as linear, are calculated in MATLAB software. The methods are tested to place TCSC in IEEE 14 and IEEE 30-bus test systems. Results illustrate that the methods are competent in finding the best location for the placement of TCSC, that satisfies the overall objective i.e. to minimize the total congestion rent.

The inclusion and modeling of uncertainty in conventional load flow is required with the enhancement in the penetration of intermittent generation. As a result, the multi-modality is there in output distribution functions. In this paper, a probabilistic load flow method is used with two wind generator models with multimodal loadings and a spline based reconstruction technique is introduced for the construction of multimodal distribution functions. The proposed load flow method is tested in the IEEE-118 bus test system and accuracy of the results have been validated by comparing these results with those obtained by Monte Carlo simulation studies.

The power transferred physically over already committed usage is important and needs to be accurately calculated. It will promote the commercial transaction opportunities when power transmitted is above the actually committed, however, the commercial transactions will be limited if it is lower. This can be accurately predicted using available transfer capability calculation. The birds' intelligence technique is proposed to accurately calculate the transfer capability for the thyristor controlled series compensated (TCSC) system and the obtained results are compared and validated with the results obtained using conventional techniques on the six-bus system.

DFIG based wind farm with Type III wind turbines are the most popular choice of wind power generation industry. Mostly, these wind farms are connected to load centers by series compensated long transmission lines. Series compensated lines offer cost effective way to evacuate large power from these wind farms. But these wind farms connected through series compensated lines may be affected by Sub Synchronous Resonance (SSR). Induction generator effect is the predominant cause of SSR in series compensated transmission lines or feeders as suggested by earlier researchers. Fuzzy logic control offers an effective way to mitigate the effect of SSR. In this paper application of fuzzy controllers instead of PI controllers in partially rated converter has been proposed to damp out SSR oscillations. First fuzzy controller parameters like number of membership functions and shape of membership functions have been studied for effectiveness of fuzzy controller for SSR control. Then fuzzy controllers for RSC, GSC and DC Link Voltage Controller have been studied and compared for their effectiveness to mitigate SSR phenomena.

Impedance source converters, are very fast growing technology for electrical energy conversion and particularly appropriate for distributed energy generation. These network avoid limitation associated with traditional voltage and current source converter by adding additional state, called as shoot-through state. These shoot-through states can be controlled by various control methods and pulse width modulation (PWM) techniques to control output voltage and current at required level. This paper provides broad review on various modulation techniques used in Z-source network for ac-dc and dc-dc power conversion. This paper also provides comparison between various parameter related to PWM techniques.

In this paper the integration of Distributed Generation (DG) in radial distribution system is investigated by computing the optimal site and size of DG to be placed using Particle Swarm Optimization (PSO). The Type-I and Type-II DGs, which are generating real and reactive power respectively, are considered for placement. The investigations are carried out on IEEE 33-bus and 69-bus radial distribution systems. The optimal site and size is also found by using analytical formulas and results of two techniques are compared. The results show the considerable improvement in voltage profile and reduction in losses is seen with optimal placement of DG. Further, the type of DGs affects these parameters.

The integration of Distributed Generation (DG) in distribution system is growing very fast since last decade. The integration of DG is beneficial if DG of optimal size is placed at optimal site. The optimal placement of DG (OPDG) helps the distribution system planners to conquer maximum benefits of DG integration. Many techniques are reported in literature to solve OPDG problem. This paper presents the state of art review of the OPDG techniques.

The loading of lines indicates the quality and reliability of the system. If it increases beyond 100%, the reliability of power system is diminished. This factor is taken care of while comparing results of different cases using power world simulator software. Sometimes these factors become contradictory to each other so much that if the minimization of one factor is attempted, the other factor maximizes so only the optimal solution will win in these cases. In this paper, three cases are taken namely 3 bus, 7 bus and 10 bus system and the values of the mentioned factors are calculated by considering various transactions. These systems are further divided into distinct areas. Also the economic cost of each combination is determined to visualize the impact of change of transactions between areas on the cost factor. The results of these transactions are then compared to obtain the reliable operation with the help of power world simulator 19 software.

Rib cage structure and morphology is important for anatomical analysis of chest CT scans. A fundamental challenge in rib cage extraction is varying intensity levels and connection with adjacent bone structures including shoulder blade and sternum. In this study, we present a fully automated 3-D algorithm to segment the rib cage by detection and separation of other bone structures. The proposed approach consists of four steps. First, all high-intensity bone structures are segmented. Second, multi-scale Hessian analysis is performed to capture plateness and vesselness information. Third, with the plate/vessel features, bone structures other than rib cage are detected. Last, the detected bones are separated from rib cage with iterative relative fuzzy connectedness method. The algorithm was evaluated using 400 human CT scans and 100 small animal images with various resolution. The results suggested that the percent accuracy of rib cage extraction is over 95% with the proposed algorithm.

Distributed inflammation in infectious diseases cause variable uptake regions in positron emission tomography (PET) images. Due to this distributed nature of immuno-pathology and associated PET uptake, intensity based methods are much better suited over region based methods for segmentation. The most commonly used intensity based segmentation is thresholding, but it has a major drawback of a lack of consensus on the selection of the thresholding value. We propose a method to select an optimal thresholding value by utilizing a novel similarity metric between the data points along the gray-level histogram of the image then using Affinity Propagation (AP) to cluster the intensities based on this metric. This method is tested against the PET images of rabbits infected with tuberculosis with distributed uptakes with promising results.

We present a simple and elegant method to incorporate user input in a template-based segmentation method for diseased organs. The user provides a partial segmentation of the organ of interest, which is used to guide the template towards its target. The user also highlights some elements of the background that should be excluded from the final segmentation. We derive by likelihood maximization a registration algorithm from a simple statistical image model in which the user labels are modeled as Bernoulli random variables. The resulting registration algorithm minimizes the sum of square differences between the binary template and the user labels, while preventing the template from shrinking, and penalizing for the inclusion of background elements into the final segmentation. We assess the performance of the proposed algorithm on synthetic images in which the amount of user annotation is controlled. We demonstrate our algorithm on the segmentation of the lungs of Mycobacterium tuberculosis infected mice from μCT images.

The performance of three-phase Self-Excited Induction Generator (SEIG) with Static Synchronous Series Compensator (SSSC) is investigated to feed various static loads. The employed SSSC is a three-phase, Insulated Gate Bipolar Transistor (IGBT) based, PWM operated, Current-Control Voltage source converter (CC-VSC). The reference current to generate the gating signals is obtained by employing only one PI control loop. With the designed parameters and the proposed control scheme, the SSSC is found effective to regulate load voltage during change of the loads.

Many techniques have been proposed to segment organs from images, however the segmentation of diseased organs remains challenging and frequently requires lots of user interaction. The challenge consists of segmenting an organ while its appearance and its shape vary due to the presence of the disease in addition to individual variations. We propose a template registration technique that can be used to recover the complete segmentation of a diseased organ from a partial segmentation. The usual template registration method is modified in such a way that it is robust to missing parts. The proposed method is used to segment Mycobacterium tuberculosis infected lungs in CT images of experimentally infected mice. Using synthetic data, we evaluate and compare the performance of the proposed algorithm with the usual sum of squared difference cost function.

The transient performance of 3-phase 4-wire self-excited induction generator (SEIG) feeding various types of linear and non-linear loads is analyzed in detail. The reactive power of SEIG and the load are being controlled by a 3-phase 4-wire configuration of static compensator (STATCOM). The STATCOM is an insulated gate bipolar transistor (IGBT) based, pulse width modulation (PWM) operated, four-leg voltage source inverter (VSI) with suitable self supporting DC link capacitor. The study is carried out for static R-L load, a motor load and non-linear rectifier fed load. Various problems of 3-phase 4-wire isolated generating system such as voltage regulation, unbalancing of loads, harmonics currents and neutral current are compensated by the STATCOM.

The combinatorial problem of the economic load dispatch is solved to find the generation levels that minimize the cost or minimize the emission level while satisfying the power balance equation. The environmental emission levels are also taken as the constraints in cost optimization problem. The solutions to the above problems is attempted using modified Hopfield neural network (MHNN), which has the flexibility of handling objective function and the constraints separately and works on the principal of minimizing the energy function and therefore ensure convergence. The study is carried out for cost optimization, NOx emission optimization, SOx emission optimization and cost optimization with SOx and NOx emissions as constraint. The simulation results are presented for standard 3-generator data while considering the losses and neglecting them.