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

Development of a Converter-Based Testing Platform and Battery Energy Storage System (BESS) Emulator for Microgrid Controller Function Evaluation

The microgrid has attracted increasing research attention in the last two decades. Due to the development of renewable energy resources and power electronics technologies, the future microgrid will trend to be smarter and more complicated. The microgrid controller performs a critical role in the microgrid operation, which will also become more and more sophisticated to support the future microgrid. Before final field deployment and test, the evaluation and testing of the controller is an indispensable step in the controller development, which requires a proper testing platform. However, existing simulation-based platforms have issues with potential numerical oscillation and may require huge computation resources for complex microgrid controllers. Meanwhile, field test-based controller evaluation is limited to the test conditions. Existing digital simulation-based platforms and field test-based platforms have limitations for microgrid controller testing. To provide a practical and flexible controller evaluation, a converter-based microgrid hardware testbed is designed and implemented considering the actual microgrid architecture and topology information. Compared with the digital simulation-based platforms, the developed microgrid testing platform can provide a more practical testing environment. Compared to the direct field test, the developed platform is more flexible to emulate different microgrids. As one of the key components, a converter-based battery energy storage system (BESS) emulator is proposed to complete the developed testing platform based on the testing requirements of microgrid controller functions. Meanwhile, the microgrid controller testing under different microgrid conditions is also considered. Two controllers for the microgrid with dynamic boundaries are tested to demonstrate the capability of the developed platform as well as the BESS emulator. Different testing cases are designed and tested to evaluate the controller performance under different microgrid conditions

Efficacy of Smart PV Inverter as a Strategic Mitigator of Network Harmonic Resonance and a Suppressor of Temporary Overvoltage Phenomenon in Distribution Systems

The research work explores the design of Smart PV inverters in terms of modelling and investigates the efficacy of a Smart PV inverter as a strategic mitigator of network harmonic resonance phenomenon and a suppressor of Temporary Overvoltage (TOV) in distribution systems. The new application and the control strategy of Smart PV inverters can also be extended to SmartPark-Plug in Electric Vehicles as the grid becomes smarter. As the grid is becoming smarter, more challenges are encountered with the integration of PV plants in distribution systems. Smart PV inverters nowadays are equipped with specialized controllers for exchanging reactive power with the grid based on the available capacity of the inverter, after the real power generation. Although present investigators are researching on several applications of Smart PV inverters, none of the research-work in real time and in documentation have addressed the benefits of employing Smart PV inverters to mitigate network resonances. U.S based standard IEEE 519 for power quality describes the network resonance as a major contributor that has an impact on the harmonic levels. This dissertation proposes a new application for the first time in utilizing a Smart PV inverter to act as a virtual detuner in mitigating network resonance. As a part of the Smart PV inverter design, the LCL filter plays a vital role on network harmonic resonance and further has a direct impact on the stability of the controller and rest of the distribution system. Temporary Overvoltage (TOV) phenomenon is more pronounced especially during unbalanced faults like single line to ground faults (SLGF) in the presence of PV. Such an abnormal incident can damage the customer loads. IEEE 142-“Effective grounding” technique is employed to design the grounding scheme for synchronous based generators. The utilities have been trying to make a PV system comply with IEEE 142 standard as well. Several utilities are still employing the same grounding schemes even now. The attempt has resulted in diminishing the efficacy of protection schemes. Further, millions of dollars and power has been wasted by the utilities. As a result, the concept of effective grounding for PV system has become a challenge when utilities try to mitigate TOV. With an intention of economical aspects in distribution systems planning, this dissertation also proposes a new application and a novel control scheme for utilizing Smart PV/Smart Park inverters to mitigate TOV in distribution systems for the first time. In other words, this novel application can serve as an effective and supporting schema towards ineffective grounding systems. PSCAD/EMTDC has been used throughout the course of research. The idea of Smart inverters serving as a virtual detuner in mitigating network harmonic resonance and as a TOV suppressor in distribution systems has been devised based on the basic principle of VAR injection and absorption with a new control strategy respectively. This research would further serve as a pioneering approach for researchers and planning engineers working in distribution systems

Islanding Detection and Power Quality Analysis in Microgrid

芝浦工業大学2019年