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1

GA based optimal planning of VAR sources using Fast Voltage Stability Index method

Bhattacharyya B., Rani S., Vais I. R., Bharti P. I.

Archives of Electrical Engineering

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2016

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Vol. 65, nr 4

789--802

EN

This paper presents a novel approach for reactive power planning of a connected power network. Reactive power planning is nothing but the optimal usage of all reactive power sources i.e., transformer tap setting arrangements, reactive generations of generators and shunt VAR compensators installed at weak nodes. Shunt VAR compensator placement positions are determined by a FVSI (Fast Voltage Stability Index) method. Optimal setting of all reactive power reserves are determined by a GA (genetic algorithm) based optimization method. The effectiveness of the detection of the weak nodes by the FVSI method is validated by comparing the result with two other wellknown methods of weak node detection like Modal analysis and the L-index method. Finally, FVSI based allocation of VAR sources emerges as the most suitable method for reactive power planning.

2

Comparative study of GA & DE algorithm for the economic operation of a power system using FACTS devices

Bhattacharyya B., Gupta V. K., Kumar S.

Archives of Electrical Engineering

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2013

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Vol. 62, nr 4

541--552

EN

The problem of improving the voltage profile and reducing power loss in electrical networks must be solved in an optimal manner. This paper deals with comparative study of Genetic Algorithm (GA) and Differential Evolution (DE) based algorithm for the optimal allocation of multiple FACTS (Flexible AC Transmission System) devices in an interconnected power system for the economic operation as well as to enhance loadability of lines. Proper placement of FACTS devices like Static VAr Compensator (SVC), Thyristor Controlled Switched Capacitor (TCSC) and controlling reactive generations of the generators and transformer tap settings simultaneously improves the system performance greatly using the proposed approach. These GA & DE based methods are applied on standard IEEE 30 bus system. The system is reactively loaded starting from base to 200% of base load. FACTS devices are installed in the different locations of the power system and system performance is observed with and without FACTS devices. First, the locations, where the FACTS devices to be placed is determined by calculating active and reactive power flows in the lines. GA and DE based algorithm is then applied to find the amount of magnitudes of the FACTS devices. Finally the comparison between these two techniques for the placement of FACTS devices are presented.

3

Encrypted prefix tree for pattern mining

Bhattacharyya R., Bhattacharyya B., Chaudhuri A.

Foundations of Computing and Decision Sciences

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2009

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Vol. 34, No. 1

3-22

EN

Data influx at large volumes is welcome for quality outcome in knowledge discovery, but it causes concern for scalability of mining algorithms. We introduce three measures for scalable mining - bit-vector coding, data-partitioning and Transaction Prefix (TP)-tree. Following encryption with bit-vector coding, transaction records are partitioned with notion of common prefixes. A TP-tree structure is devised for arranging the data parts such that multiple records share common storage. Advantage is two-fold: additional storage reduction over bit-vector coding and mining common prefixes together. These altogether improve space-time requirement in frequent pattern mining. Experiments on dense datasets show significant improvements in performance and scalability of both candidate generation and pattern-growth algorithms.

4

Stochastic finite element method for sensitivity analysis of structures

Bhattacharyya B., Chakraborty S.

Applied Mechanics and Engineering

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2000

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Vol. 5, no 4

867-882

EN

The conventional sensitivity analysis of structures is based on the assumption of complete certainty of design parameters. However, occurrence of uncertainty is unavoidable in structures. In the present work, an attempt has been made to study the response sensitivity, considering the effect of uncertainty in structural design parameters. The random parameters are modeled as Gaussian stochastic process and simulated through covariance matrix decomposition. The advantages of Neumann expansion technique has been utilized in deriving the finite element solution of the response sensitivity within the framework of Monte Carlo simulation. Numerical examples are presented to explain the accuracy and efficacy of Neumann expansion method over direct simulation the process.