Ashish Mani

Simultaneous Placement and Sizing of DG and Capacitor to Minimize the Power Losses in Radial Distribution Network

In the present power system scenario, minimization of power losses in the distribution network is one of the interesting areas of research and modern challenges in the research community. Distribution network has large and complex structure; it produces more power losses as compared to transmission system. High power losses and poor voltage regulation are occurring at each bus when it moves away from the substation node to end node. Many methods have been implemented to minimize the power losses in the distribution system. Sitting and sizing of Distributed Generation (DG) and capacitors are new approaches used in the distribution system to minimize the power losses. However, capacity and location of DG and capacitors in the distribution system are considered independently. Improved voltage profile, increased overall energy efficiency, and reduced environmental impacts are some benefits produced by DG and capacitors. Consumer is also benefited from DG and capacitors optimization in terms of improved quality of power supply at lower cost. Sitting and sizing of DG and capacitor is a combinatorial optimization problem, and hence metaheuristics are used. An Adaptive Quantum-inspired Evolutionary Algorithm (AQiEA) approach is used for the optimization of DG and capacitors. In this paper, a new approach is considered by simultaneous placement and sizing of Distributed Generation (DG) and capacitor to minimize power losses. The effectiveness of the proposed algorithm is tested on 85-bus system. The experimental results show that AQiEA has better performance as compared with some existing algorithms.

An all-pair quantum SVM approach for big data multiclass classification

In this paper, we discuss a quantum approach for the all-pair multiclass classification problem. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are k(k − 1)/2 classifiers for a k-class classification problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speedup due to quantum computing. The quantum all-pair algorithm can also be used with other classification algorithms, and a speedup gain can be achieved as compared to their classical counterparts.In this paper we have discussed a quantum approach for the all-pair multiclass classification problem. We have shown that the multiclass support vector machine for big data classification with a quantum all-pair approach can be implemented in logarithm time complexity on a quantum computer. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are k (k-1)/2 classifiers for a k-class problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial time complexity, our approach exhibits exponential speed up in the quantum version. The quantum all-pair algorithm can be used with other classification algorithms, and a speed up gain can be achieved as compared to their classical counterparts.

Big data classification with quantum multiclass SVM and quantum one-against-all approach

In this paper, we have proposed a quantum approach for multiclass support vector machines to handle big data classification. To achieve this goal, we have also developed and implemented a quantum version of the one-against-all algorithm. The proposed approach demonstrates that the big data multiclass classification can be implemented with quantum multiclass support vector machine in logarithmic time complexity on a quantum computer, compared to the classical multiclass support vector machines that can be implemented with polynomial time complexity. Hence, our proposed approach exhibits an exponential speed up in time complexity for big data multiclass classification.

Solving Economic Load Dispatch problem with valve loading effect using adaptive real coded quantum-inspired evolutionary algorithm

Economical dispatch of power is a significant real time optimization problem in the operation of power system, which allocates the loads on the committed generating units to reduce the generation cost, while meeting all the inequality and equality constraints. Traditionally, economic load dispatch problems have been formulated as convex optimization problem and various classical methods such as gradient methods, base-point participation factor method, etc. have been used for solving them. The traditional formulation is a quadratic input-output curve or piece wise linear cost curve, which are continuous and convex, however, real generating units have valve loading effects, which make the cost curve discontinuous and non-convex and hence the economic dispatch problem is no longer simple to handle. Thus, the classical methods cannot be employed for non-convex problems. The non-convex, non-linear and discontinuous economic dispatch (ED) problem can be better solved by metaheuristic approaches. Such approaches often require tuning of their respective evolutionary parameters. Although, solution to such type of problem have already been solved by different metaheuristic optimization techniques in previous studies as well, however, the algorithm employed in this paper to solve Power Economic Load Dispatch (PELD) problem is adaptive real coded quantum-inspired evolutionary algorithm, which does not require tuning of evolutionary parameters and performs better than some of the existing techniques on standard 6-bus 3 machine system and IEEE 14 bus 5 unit system.