Adaptive protection technique for controllable series compensated EHV transmission systems using neural networks

As is well known, flexible AC transmission systems (FACTS) provide opportunities of better utilizing existing transmission systems by using power electronics based controllers. One of the main FACTS devices is controllable series compensation (CSC), which has an ability to control the compensated impedance by changing the firing angle of thyristors. However, the implementation of this technology will pose new problems to conventional line protection schemes. This paper proposes a novel adaptive protection scheme for CSC transmission systems by using a neural network approach. It places emphasis on the feature extraction, the topology and training of neural networks. Some preliminary test results clearly show the trained neural network is able to make correct trip decisions from abnormal voltage waveforms using associations learned from previous experiences. In addition, the scheme also has the ability to identify faulted phases. The test results successfully demonstrate the feasibility of neural networks based adaptive protection for CSC transmission systems