Optimization algorithms for adaptive filtering of interferences in corrupted signal

274-281Neural network adaptive filters are mainly used for the interference cancellation techniques. The gradient based design methods are well developed for the design of neural network adaptive filter but they converge to local minima. This paper describes the global optimization interference cancelling techniques for adaptive filtering of interferences in the corrupted signal. The system is designed using the adaptive filtering of the interferences in the corrupted signal using the Back Propagation Neural Network (BPNN) algorithm, Genetic Algorithm (GA), and Bee Colony (BC) algorithm. These optimization algorithms are used for initialization of weights, learning parameters, activation function and selection of network structure of the artificial neural network. The adaptive filtering system is designed using an adaptive learning ability of BPNN algorithm. This paper presents a comparison of evolutionary optimization algorithm such as hybrid GA-BPNN and BC-BPNN algorithm for the interference cancellation in corrupted signals

Optimization algorithms for adaptive filtering of interferences in corrupted signal

Neural network adaptive filters are mainly used for the interference cancellation techniques. The gradient based design methods are well developed for the design of neural network adaptive filter but they converge to local minima. This paper describes the global optimization interference cancelling techniques for adaptive filtering of interferences in the corrupted signal. The system is designed using the adaptive filtering of the interferences in the corrupted signal using the Back Propagation Neural Network (BPNN) algorithm, Genetic Algorithm (GA), and Bee Colony (BC) algorithm. These optimization algorithms are used for initialization of weights, learning parameters, activation function and selection of network structure of the artificial neural network. The adaptive filtering system is designed using an adaptive learning ability of BPNN algorithm. This paper presents a comparison of evolutionary optimization algorithm such as hybrid GA-BPNN and BC-BPNN algorithm for the interference cancellation in corrupted signals

Solution to Optimization Problem Through Evolutionary Algorithm Using Weighting Function Method

The need for reduction in emission of harmful gases such as carbon dioxide, sulfur dioxide and nitrogen oxides from fossil fuel fired power plants has attracted our attention since it pollutes the atmosphere. So an attempt has been made to combine the concept of emission dispatch along with an economic dispatch problem with the help of the ABC algorithm using the weighting function method in power system operation. To apply this algorithm the original problem of optimization has been changed into the problem of identifying the best parameter that optimizes the objective function. This algorithm has shown its effectiveness in solving many real world problems with many constraints in different domains. The designed approach has been tested using Standard IEEE 30 bus system with incremental cost function along with emission coefficients valve opening and closing effects in the power plant. The result obtained shows that the designed approach was identified to be the best and most efficient in identifying the global minimum among the search space compared with other existing techniques

Solution to Optimization Problem Through Evolutionary Algorithm Using Weighting Function Method

The need for reduction in emission of harmful gases such as carbon dioxide, sulfur dioxide and nitrogen oxides from fossil fuel fired power plants has attracted our attention since it pollutes the atmosphere. So an attempt has been made to combine the concept of emission dispatch along with an economic dispatch problem with the help of the ABC algorithm using the weighting function method in power system operation. To apply this algorithm the original problem of optimization has been changed into the problem of identifying the best parameter that optimizes the objective function. This algorithm has shown its effectiveness in solving many real world problems with many constraints in different domains. The designed approach has been tested using Standard IEEE 30 bus system with incremental cost function along with emission coefficients valve opening and closing effects in the power plant. The result obtained shows that the designed approach was identified to be the best and most efficient in identifying the global minimum among the search space compared with other existing techniques

Energy efficient distributed cluster head scheduling scheme for two tiered wireless sensor network

Wireless Sensor Network (WSN) provides a significant contribution in the emerging fields such as ambient intelligence and ubiquitous computing. In WSN, optimization and load balancing of network resources are critical concern to provide the intelligence for long duration. Since clustering the sensor nodes can significantly enhance overall system scalability and energy efficiency this paper presents a distributed cluster head scheduling (DCHS) algorithm to achieve the network longevity in WSN. The major novelty of this work is that the network is divided into primary and secondary tiers based on received signal strength indication of sensor nodes from the base station. The proposed DCHS supports for two tier WSN architecture and gives suggestion to elect the cluster head nodes and gateway nodes for both primary and secondary tiers. The DCHS mechanism satisfies an ideal distribution of the cluster head among the sensor nodes and avoids frequent selection of cluster head, based on Received Signal Strength Indication (RSSI) and residual energy level of the sensor nodes. Since the RSSI is the key parameter for this paper, the practical experiment was conducted to measure RSSI value by using MSP430F149 processor and CC2500 transceiver. The measured RSSI values were given input to the event based simulator to test the DCHS mechanism. The real time experimental study validated the proposed scheme for various scenarios

PSO Based Optimal Location and Sizing of SVC for Novel Multiobjective Voltage Stability Analysis during N – 2 Line Contingency

In this paper voltage stability is analysed based not only on the voltage deviations from the nominal values but also on the number of limit violating buses and severity of voltage limit violations. The expression of the actual state of the system as a numerical index like severity, aids the system operator in taking better security related decisions at control centres both during a period of contingency and also at a highly stressed operating condition. In contrary to conventional N – 1 contingency analysis, Northern Electric Reliability Council (NERC) recommends N – 2 line contingency analysis. The decision of the system operator to overcome the present contingency state of the system must blend harmoniously with the stability of the system. Hence the work presents a novel N – 2 contingency analysis based on the continuous severity function of the system. The study is performed on 4005 possible combinations of N – 2 contingency states for the practical Indian Utility 62 bus system. Static VAr Compensator is used to improve voltage profile during line contingencies. A multi- objective optimization with the objective of minimizing the voltage deviation and also the number of limit violating bus with optimal location and optimal sizing of SVC is achieved by Particle Swarm Optimization algorithm