The electrical short term load forecasting has been emerged as one of the most essential field of research for efficient and reliable operation of power system in last few decades. It plays very significant role in the field of scheduling, contingency analysis, load flow analysis, planning and maintenance of power system. This paper addresses a review on recently published research work on different variants of artificial neural network in the field of short term load forecasting. In particular, the hybrid networks which is a combination of neural network with stochastic learning techniques such as genetic algorithm(GA), particle swarm optimization (PSO) etc. which has been successfully applied for short term load forecasting (STLF) is discussed thoroughly.
The quality of power that is degrading day by day is an important issue for all the consumers. The important factor for this is harmonics in the voltage and current waveforms which can be resolved by the use of hybrid series active power filter. The combination consists of a series active power filter and a shunt passive filter connected in parallel to the load. The method used in this paper is for the purpose of achieving good harmonic compensation and reduced total harmonic distortion for various types of nonlinear loads as per the standards of IEEE 519. The proposed HSAPF technique uses the synchronous reference frame method for generating the compensating signal with an intelligent PI controller that uses particle swarm optimization (PSO) technique to obtain the required gain values needed to improve the steady state response of the system. The concept of vigorous HSAPF has been authenticated through MATLAB simulation analysis, and the results obtained validate the accuracy of the method for the different load conditions.
In this paper, an intelligent energy management system for the smart home that combines the solar energy as well as the energy from the battery storage devices has been proposed to reduce the dependency on the power grid and make the system to be more economical. The proposed system manages the energy requirement of the smart home by properly rescheduling and arranging the power flow between the energy storage devices, grid power, and the photovoltaics. The power grid can absorb the excess power from the designed system whenever the load requirement is low, and on the other hand, it can supply the power to the load in case of peak demand. Therefore, in the designed system, a user has the flexibility to sell the extra power for the purpose of revenue. A thorough simulation of the system has been carried out, and the results obtained show the effectiveness of the approach in terms of energy management between the different sources.
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