Environmental concerns and rising energy consumption has offered new opportunities for public use of renewable energy sources. In the current power and energy scenario, distributed generation (DG) has created a lot of interest across the globe due to the growing concerns about environmental pollution and global warming caused by greenhouse gas emissions. Renewable DGs such as wind generators and solar photovoltaic are well recognized now-a-days as sources of clean energy. Instability in power system has always been a serious problem in electricity networks accounting for the disruption, poor power quality and affecting the cost and productivity of power utilities and the consumers. One of the most exciting and potentially profitable recent developments is increasing usage of artificial intelligence techniques. The intention of this paper is to give an overview of using neural network (NN) techniques in power systems. The number of ANN applications has increased drastically in the last few years, fired by both theoretical and application successes in a variety of disciplines including power system. In case of Micro Grid, without control strategy the system would be pushed back leading system towards instability can be avoided implementing ANN.  Micro grid advantages can be cited as follows: The need for additional suppliers felt due to the rapid growth of load and fossil fuels reduction. Establishing new power generating sources will reduce environmental pollution and global warming. Distributed generation (DG) sources make it easy to combined heat and power (CHP) which increases its efficiency by reducing losses. The resources are suitable for consumers with low capacity. DG Resource, can back up and thus improves power quality and network reliability due to both possible performances, Islanding and Grid Connected. Also The artificial neural network used in short-time load forecasting can grasp interior rule in factors and complete complex mathematic mapping. Therefore, it is worldwide applied effectively for power system short-term load forecastin