S. Padmini ¹, V. S. Rajesh ¹, N. Chandrakanth ¹, V. Rajesh ¹

Affiliations
1 Department of Electrical and Electronics Engineering, SRM University, SRM Nagar, Kattankulathur − 603 203, Kancheepuram, Chennai, Tamil Nadu, IN

Abstract

Background/Objectives: In this paper a clear focus on pricing of the GENeration COmpanies (GENCO) has been dealt with. GENCO participate in day-ahead power pool trading to maximize their profit in the energy market. Methods/Statistical Analysis: Since the self-scheduling problem is a highly non-linear, non-convex mixed-integer optimization problem, conventional methods for optimizing may suffer excessive computational burden. This paper incorporates the method for determining the Locational Marginal Pricing with and without congestion due to over load and gives an observation of the ways to overcome this critical situation in the deregulated energy market using Power World Simulator (PWS) software for 3-bus system and IEEE 9-bus system Findings: In the case of Locational Marginal Pricing (LMP) forecasting, the main challenge is to forecast the volatile prices accurately in a day-ahead market. The PWS software used for test cases considered indicates that the output information is obtained at the short time frame which ultimately reduces the computation burden existed earlier in the conventional method Applications: The proposed methodology will be helpful for the generating company to forecast the Locational Marginal Pricing for both with and without congestion due to over load and rescheduling of generators will be carried out accordingly in the deregulated energy market within a very short time frame.

Keywords

Congestion, Deregulated Market, GENeration COmpanies (GENCO), Locational Marginal Pricing (LMP).

Full Text

   

D. Bhavana ¹, V. Rajesh ¹

Affiliations
1 Department of Electronics and Communications Engineering, K L University, Vaddeswaram, Guntur - 522502, Andhra Pradesh, IN

Abstract

Background/Objectives: To propose a new fusion technique for combining optical and IR images and validate the proposed technique with the existing techniques using entropy as an evaluating measure. Methods/Statistical Analysis: In this paper we propose a new pixel level fusion method using Continuous Genetic Algorithm (CGA) using Heuristiccrossover for reproduction. Findings: Pixel level Fusion methods are computationally less complex and converge quickly. The proposed approach is applied on multispectral images which are used in applications like multispectral face recognition, Medical imaging, Remote Sensing etc. The proposed algorithm requires less memory space and has less computational complexity. Conclusion/Improvements: An increase in the entropy of the fused image indicates that there is an increase in the overall information content. The proposed technique is implemented on a set of visual and thermal images and an increase in the entropy value of the fused image is observed.

Keywords

Continuous Genetic Algorithm, Entropy, Heuristic Crossover, Image Fusion, Pixel Level Fusion.

Full Text

   

D. Bhavana ¹, V. Rajesh ¹, K. Kishore Kumar ²

Affiliations
1 Department of ECE, KL University, Guntur - 522502, Andhra Pradesh, IN
2 Department of Mechanical Engineering, KL University, Guntur - 522502, Andhra Pradesh, IN

Abstract

Background/Objectives: Contrast enhancement is an important stage in uncooled thermal images as uncooled thermal images are low-contrast images. Histogram equalization is the widely used technique for contrast enhancement of images. Methods/Statistical Analysis: The proposed method for contrast enhancement is based on plateau histogram equalisation. It preserves all the benefits of classical histogram equalisation but does not extend the contrast of background enormously²⁴. Plateau histogram equalization technique is one of the contrast enhancement techniques where the pels count is limited to an inception value defined based on histogram properties. Findings: In comparing the results of proposed contrast enhancement system, the image has been significantly enhanced without enhancing the background gray levels and the noise and thereby increasing the brightness preservation and harvested a more natural enhancement. Applications/Improvements: The proposed technique on thermal images provides a good enhancement, which is difficult to achieve with traditional Histogram equalization technique on thermal images. The proposed algorithm is tested on raw images, gain corrected images, and Gain and Offset corrected images and achieved satisfactory results.

Keywords

Contrast Enhancement, Histogram Equalization, Low-contrast images, Plateau Histogram Equalization, Thermal Images.

Full Text