Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples

A Novel Passive Islanding Detection Technique for Converter- Based Distributed Generation Systems

With the increased popularity of distributed generation (DG) in power systems, issues such as unintentional islanding should be efficiently resolved. Islanding condition occurs when part of the electrical power system is disconnected from the rest of the grid and is still energized by a DG unit. Conventional passive islanding detection approaches utilize the power system parameters such as the change of voltage magnitude, frequency deviation, and voltage phase displacement. The main challenge in these approaches is the dependency of these parameters on the islanding conditions which can render some of these approaches ineffective. This paper proposes a novel and a computationally inexpensive passive islanding detection technique for converter-based distributed generation systems. The proposed technique utilizes the converter-induced ripples in the instantaneous voltage amplitude at the point of common coupling to detect islanding. The proposed technique was modeled in a converter-based DG network with photo-voltaic arrays. The proposed technique model was tested using a wide range of islanding and non-islanding conditions and was able to accurately detect islanding under all DG loading conditions