Adaptive neuro-fuzzy inference system based total demand distortion factor for power quality evaluation
An adaptive neuro-fuzzy inference system based total demand distortion factor (ANFIS TDDF) is proposed in this paper. When considering a single range of short circuit level, the values of total demand distortion (TDD) are enough to quantify harmonic distortion in a certain current waveform. When considering multiple ranges of short circuit levels the TDD is unable to determine whether the distortion is within the acceptable limits or not. The ANFIS TDDF indicates the level of distortion in the current waveform or how close is the waveform to a pure sinusoidal wave shape and also allows deciding whether the distortion contained in the current is within the acceptable limit or not. Moreover, the use of an adaptive neuro-fuzzy inference system (ANFIS) has the advantages of being simple, easy to implement and contains its knowledge base. The proposed ANFIS TDDF is sensitive to the TDD and short circuit level changes in all distortion cases in sinusoidal and non-sinusoidal situations. Therefore it will be very useful for many applications such as power-quality (PQ) evaluation, cost-benefit analysis of PQ mitigation techniques and setting penalty tariffs for customers generating harmonics.
anfis total demand distortion (tdd) harmonics fuzzy logic power quality (pq)