Abstract

Maximum Loadability Limit (MLL) is the margin between the operating point of the system and the maximum loading point. The enhancement of maximum loadability limit of power system can be formulated as an optimization problem, which consists of two steps namely computing MLL and the optimum cost of generation for MLL. This paper proposes a differential evolution particle swarm optimization (DEPSO) algorithm for solving the optimal power flow problem for MLL enhancement with voltage stability constraint. The DEPSO employs features of both differential evolution (DE) and particle swarm optimization (PSO) for the development of hybrid algorithm. The feasibility of the proposed approach was tested on IEEE 30-, 57-bus test systems. Case studies were investigated to test and validate the robustness of the proposed method in finding optimal solution. Simulation results demonstrate that the DEPSO provides very remarkable results compared to original DE, PSO and other methods reported in the literature recently.

Abstract

The photovoltaic system exhibits a non-linear i-v characteristic and its maximum power point varies with solar insolation and temperature. An efficient Maximum Power Point Tracking (MPPT) algorithm is important to increase the output efficiency of a photovoltaic (PV) generate system. The first part of this paper intends to give an overview of the Maximum Power Point Tracking methods for Photovoltaic (PV) generators presently reported in the literature. The most well-known and popular methods, like the Perturb and Observe (P&O), the Incremental Conductance (INC) and the Constant Voltage (CV), are presented. These methods, especially the P&O, have been treated by many works. In the second part of the paper, a MATLAB based model of a MPPT by constant voltage method is developed. The simulation results verify the effective implementation of the constant voltage scheme in Simulink.

Abstract

In this paper, the speed control of induction motor drives using neural networks is presented. A new simulink model for a neural network-controlled bidirectional chopper fed single phase induction motor is developed. The single phase induction motor is modeled using double field revolving theory. The parameters of the controller corresponding to various drive parameter sets are found off-line and stored as the training patterns. Under normal operation, the true drive parameters are real-time identified and they are converted into the controller parameters through multilayer forward computation by neural networks. In which, the connection weights of the multilayer feed forward network are estimated by back propagation learning algorithm. A comparative study has been made between the conventional and neural network controllers. It is observed that the neural network controlled drive system has better dynamic performance, reduced overshoot and faster transient response than the PI controlled system. Here the stator voltage is varied using the neural network controller, according to the non-linear load conditions, and the speed is maintained at the required level.

Abstract

This paper presents a simple method of three phase power flow algorithm for application to unbalanced radial distribution systems (URDS) with inclusion of voltage regulators and transformer modeling. Distribution systems are usually unbalanced due to unbalanced loading at the different phases. In this paper forward - backward propagation algorithm is used to calculate branch currents and node voltages at each node. The proposed method has been tested to analyze several distribution networks. The application of this method is also extended to find optimum location for reactive power compensation and network reconfiguration for planning and day-to-day operation of distribution networks. The proposed method exactly finds out the critical lines as that of the conventional method. The effectiveness of the proposed method was successfully applied to 19 bus  and 25 bus  unbalanced radial test systems.

Abstract

A Power system is said to be operating in a secure state if the system remains in a reliable, normal operating state for every contingency case under consideration. Due to time limitation in real-time situation those contingency cases which are potentially harmful to the system must be picked out and detailed analysis is to be carried out only for these cases. This process of ranking the contingencies according to their severities is referred to as contingency ranking.

In the proposed approach, the post contingent quantities are first expressed in fuzzy set notation. Then the heuristic rules employed by the system operators in contingency ranking are compiled and coded in the form of fuzzy reasoning rules. These reasoning rules form the basis for combining the evidence from each post contingent quantity and reaching the overall system severity index. In the fuzzy logic approach, voltage stability indices at the load buses are used in addition to the real power loadings and load bus voltage violations as the post-contingent quantities to evaluate the network contingency ranking. Simulation Results on IEEE - 14, 30, 39, 57 and 118 bus systems are presented.

Abstract

The planning, operation and control of power system are having great significance governed by security considerations. The estimation of effect of contingencies and planning suitable measures are desirable issues for maintaining security of complex power system. In this paper a novel hybrid method, called Interior Point method embedded with Evolutionary Particle Swarm Optimization is employed to solve optimal power flow problem. IPM-EPSO based methodology considering Series FACTS device like SSSC was proposed for improving the system security and for minimization of fuel cost under consideration of degree of severity of the contingencies. The ranking for the network contingencies is evaluated using composite criteria based fuzzy logic for eliminating masking effects. The proposed hybrid OPF technique using SSSC for deregulated power industry has been tested under simulated conditions on a fewer systems and the results for modified deregulated IEEE 30-bus system were presented for illustration purpose.

Abstract

CMOS based system using complementary logic has dominated other design concept, mainly because of simple fabrication process, low power requirements and good voltage swing response. In modern computing system it requires to have a feature of low power consumption without scarifying the speed. In such case the power requirement is mainly influenced by transition activity at the output node. Literature survey shows that lot of technique has been proposed for static component of power budget, but dynamic component is still to be considered. Encoding methods have been proposed to reduce transition activity. Scheme used to reduce dynamic power is called as Bus Encoding Technique.  Mathematical model have been proposed to get efficiency factors along with probabilistic model and expectation based analysis.  Based upon features of bus encoding scheme, specific application has been proposed. Gray encoding scheme is recommended for generalized application. Many factors were used to find out efficiency of  encoding method. Power requirement and delay are proven to be inversely proportional to each other. To recommend any system, it requires having positive result in terms of overhead. This paper aims to target Grey Encoding scheme for calculating Delay. Work has been extended to consider 0.25-micro meter manufacturing technology. Overhead has been calculated in terms of delay. It has resulted as 661.320075 pico seconds. This is tolerable as compared to integration rate of VLSI system.

Abstract

With the growing needs of energy, the use of renewable resources to serve the purpose has been increased. Our paper is just a light into the solution to the crisis that is looming over us. We have designed a controller in the direction of improving the efficiency of Wind energy systems.

The approach is based on a sliding mode control methodology, i.e., the system under control is driven towards a sliding mode by tuning the parameters of the controller. In this loop, the parameters of the controller are adjusted such that a zero learning error level is reached in one dimensional phase space defined on the output of the controller.