Impact of wind farms on disturbance detection and classification in distributed generation using modified Adaline network and an adaptive neuro-fuzzy information system

• A modified Adaline and ANFIS are used for disturbance detection in distribution generation.
• Training of the Adaline is done using a robust decoupled Gauss–Newton algorithm.
• Impact of wind velocity of the wind farm on islanding and non-islanding cases is studied.
• Power quality indices are used to classify disturbances.
• Comparison with other techniques is shown to validate the superiority.

A new disturbance detection and classification technique based on modified Adaline and adaptive neuro-fuzzy information system (ANFIS) is proposed for a distributed generation system comprising a wind power generating system (DFIG) and a photovoltaic array. The proposed technique is based on a fast Gauss–Newton parameter updating rule rather than the conventional Widrow–Hoff delta rule for the Adaline network. The voltage and current signals near the target distributed generation (DG), particularly the DFIG, whose speed varies from minimum to the maximum cut-off speed, are processed through the modified Adaline network to yield the features like the negative sequence power, harmonic amplification factor (HAF), total harmonic distortion (THD), etc. These features are then used as training sets for the ANFIS, which employs a gradient descent algorithm to update its parameters. The proposed technique distinguishes the islanding condition of the distributed generation system with some other disturbances, such as switching faults, capacitor bank switching, voltage swell, voltage sag, distorted grid voltage, unbalanced load switching, etc. which are referred to as non-islanding cases in this paper.

Representation of non-detection zone in terms of percentage of power mismatch for wind speed of 8 m/s.Figure optionsDownload as PowerPoint slide