Informatics Publishing Limited

K. Selvakumar ¹, C. S. Boopathi ¹, T. Venkatesan ²

Affiliations
1 Department of EEE, SRM University, Kattankulathur - 603203, Tamil Nadu, IN
2 Department of EEE, K. S. Rangasamy College of Technology, Tiruchengode - 637215, Tamil Nadu, IN

Abstract

Objectives: The main objective function is to increase the Generation Companies profit and reduce the GHG gas emission of the thermal generating units. Methods/Analysis: During the most recent few centuries, emission control has become a very big problem of worlds concern due to the frequently increasing pollution of earth’s atmosphere. In order to reach the emission control in this paper the Improved Bacterial Foraging Algorithm (IBFA) is proposed. The Bacterial Foraging Algorithm is formed by foraging behavior of E-coli Bacteria in the human intestine. But the BF algorithm leads to some convergence problem while solving the large problems. So for improving the performance of the large problems the new integer coded Improved Bacterial Foraging Algorithm is proposed. Findings: The proposed method is implemented to the IEEE 39 bus10 unit system with one day time period. This proposed algorithm is simulated using MATLAB software and the output results are compared with traditional Unit Commitment Method. Novelty/Improvement: The restructuring of electric power industry is used to reform the electric supply industry. The generation scheduling of thermal generating units in deregulated environment is named as Profit Based Unit Commitment. In PBUC problem the normal Demand constraint is changed to modified power demand constraint to increase the GENeration Companies (GENCO) profit.

Keywords

Emission Limitation, GENeration Companies (GENCO), Improved Bacterial Foraging Algorithm (IBFA), Profit Based Unit Commitment (PBUC), Unit Commitment (UC).

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A. Venkadesan ¹, K. Sedhu Raman ², K. Chandrasekaran ¹, C. S. Boopathi ³

Affiliations
1 Department of EEE, NIT Puducherry, Karaikal - 609609, Puducherry, IN
2 Department of EEE, MVIT, Kalitheerthalkuppam - 605107, Puducherry, IN
3 Department of EEE, SRM University, Chennai - 603203, Tamil Nadu, IN

Abstract

Objectives: This paper presents harmonics estimation using Artificial Neural Network (ANN) for a 2 pulse Un-controlled power electronics converter. Methods/Analysis: Feed-forward architecture is chosen to model ANN-based Harmonics Estimator. The Feedforward architecture trained with various Learning algorithms is investigated. The suitable ANN model is identified. The performance of ANN based harmonics estimator is compared with conventional Fourier series method. Findings: The feed-forward architecture trained with LM algorithm is identified to be suitable for harmonics estimation in 2-pulse uncontrolled rectifier. Novelty/Improvement: The suitability of feed-forward architecture with different learning algorithms is investigated which is novel in this paper.

Keywords

Artificial Neural Networks, Estimator, Feed-Forward Neural Architectures, Harmonics, Learning Algorithms, Power Electronics Converters, 2-Pulse Controlled Rectifier.

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K. Selvakumar ¹, K. Vijayakumar ¹, D. Sattianadan ¹, C. S. Boopathi ¹

Affiliations
1 Department of EEE, SRM University, Potheri – 603203,Chennai,Tamil Nadu, IN

Abstract

Objectives: The objective of this paper is to decide the startup and shut down status of thermal generating units to meet the fluctuating load over a limited period at a lowest cost and also with lowest emission. Methods/Analysis: This paper presents Shuffled Frog Leaping Algorithm to explain short term Unit Commitment Problem solution with regard to Emission limitation and Prohibited Operating Zone (POZ) constraint. Findings: Fuel cost savings can be obtained by proper commitment of the available generating units. The total operating cost includes both the fuel cost and cost associated with the startup, shut down and maintenance of units. A variety of constraints like spinning reserve, generation limit constraint, minimum up time, minimum down time, system power balance and response rate constraints like ramp up constraint, ramp down constraint and prohibited operating zone constraints are considered for investigation. Novelty /Improvement: The problem is solved using an integer coded Shuffled Frog Leaping Algorithm which offers a practical unit commitment problem. A MATLAB code has been developed to explain the unit commitment problem using SFLA. The results are extensively validated for standard IEEE 39 bus with 10 units system. The results obtained are compared with existing method.

Keywords

Economic Dispatch (ED), Emission Limitation, Prohibited Operating Zone (POZ), Shuffled Frog Leaping Algorithm (SFLA), Unit Commitment (UC).

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D. Ragul ¹, K. Selvakumar ², C. S. Boopathi ², K. Raja ¹

Affiliations
1 Department of EEE, Knowledge Institute of Technology, Salem – 637504, Tamil Nadu, IN
2 Department of EEE, SRM University, Chennai – 603203, Tamil Nadu, IN

Abstract

Objectives: The main objective of this paper is smoothening of the power fluctuation of a variable speed PMSG wind generator with the help of Energy Capacitor System (ECS). ECS consists of a voltage source inverter, dc-dc converter and an ultra-capacitor bank including their control strategies. Methods/Analysis: In this paper two back-to-back voltage source Insulated Gate Biopolar Transistor (IGBT) converters controlled through highly efficient variable frequency drive control method is considered. By using this method, efficiency of wind farm can be improved and voltage regulation for interconnecting the wind farm with grid can be achieved. Findings: Recent application of power electronic devices and scaling up of MegaWatt (MW) wind turbines, Low-speed high torque Permanent Magnet Synchronous Generator (PMSG) has become the ultimate choice for wind power extraction. At present, a commercial PMSG based wind generator primarily makes use of a rectifier followed by an IGBT converter. Novelty /Improvement: The real power reference for ECS is generated accurately. Simulation is carried out through MATLAB/SIMULINK.

Keywords

ECS, Grid Voltage Support Control, Maximum Power Extraction, PMSG Wind Generator, Reactive Power Control, Variable Frequency Drive Control, Voltage Source Converter.

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K. Selvakumar ¹, C. S. Boopathi ¹, M. Sri Harsha ¹

Affiliations
1 Department of EEE, SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, IN

Abstract

Objectives: To calculate the load flow analysis by using Artificial Neural Networks (ANN) and the Cascade Architecture (CC) with Levenberg-Marquardt (LM) algorithm is used for this proposed system. Methods/Statistical Analysis: Many conventional methods such as Newton-Raphson method, Gauss-Seidel method, AC load flow analysis etc., are used to estimate the load flow analysis of a power system. The major backdrops in using these methods are, using complex non-linear equations, iterative methods and time consuming. To overcome these problems, this paper discusses using Artificial Neural Networks (ANN) which reduces the time consumption in calculating load flow analysis. Findings: In the real-time planning and operation of a power system the major consideration is voltage stability assessment. The voltage instability in a power system will lead to a blackout condition. The continuous increase in load demand, changes in system conditions causes voltage collapse. So the on-line monitoring of voltage stability is a necessary condition. Application/Improvements: The output of the load flow analysis is used to calculate the Index that is used to maintain the system in stable limits.

Keywords

Artificial Neural Networks (ANN), Cascade Architecture (CC), Levenberg-Marquardt (LM), Stability Index, Voltage Stability.

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