In the present day scenario, the electric power demand is increasing, so power systems have become larger and more complex. In order to keep reliability, fast and reliable transient stability assessment schemes are desired in power system operations. In this article, a quick and unfailing transient stability assessment algorithm is proposed, where support vector machines are employed as pattern classifiers so as to build fast relation mappings between the stability results and selected input attributes. Support vector machines with different kernel functions and kernel parameters are constructed and trained to calculate hyperplanes that separate the stable and unstable states of power system for .n 1/ faults. The outputs of three randomly selected support vector machines are combined by fuzzifying the distance of a given operating state from their optimal hyperplanes and their validation accuracies. By combining the outputs of a team of classifiers, a more accurate decision than that of a single best member has been achieved. Each fuzzy combined support vector machine is designed to deal with a single contingency scenario. Feature extraction through discrete wavelet transform alleviates the problem of high input dimensionality. The simulation results obtained using three bench mark systems demonstrate the good potential for fuzzy combined support vector machine classifiers in transient stability assessment.

The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit's allocation with minimum fuel cost and also considers the emission cost. In this paper we have intended to propose a hybrid technique to optimize the economic and emission dispatch problem in power system. The hybrid technique is used to minimize the cost function of generating units and emission cost by balancing the total load demand and to decrease the power loss. This proposed technique employs Particle Swarm Optimization (PSO) and Neural Network (NN). PSO is one of the computational techniques that use a searching process to obtain an optimal solution and neural network is used to predict the load demand. Prior to performing this, the neural network training method is used to train all the generating power with respect to the load demand. The economic and emission dispatch problem will be solved by the optimized generating power and predicted load demand. The proposed hybrid intelligent technique is implemented in MATLAB platform and its performance is evaluated.

No abstract

No abstract

No abstract