Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers.
Voltage collapse tends to occur due to the voltage instability created during large faults. As a last resort, undervoltage load shedding (UVLS) is performed after all the available power operation and control mechanisms have been exhausted. Load shedding techniques have advanced from the conventional and adaptive methods that are less optimal compared to computational intelligence-based techniques. Recent works have identified hybrid algorithms to give more optimal solutions for UVLS problems with multi-objective functions. In this paper, a novel hybrid ABC-PSO algorithm, adapted from a software estimation project, is used to perform UVLS on a modified IEEE 14-bus system. Eight overload conditions are imposed on the system ranging from 105% to 140% loading, where FVSI ranking is used in identifying weak buses. The load shedding is then performed following decentralized relay settings of 3.5 seconds, 5 seconds and 8 seconds, which gives an overall 99.32% recovery of voltage profiles. The proposed hybrid ABC-PSO algorithm is able to shed optimal amounts of load, giving an 89.56% postcontingency load, compared to GA's 77.04%, ABC-ANN at 84.03% and PSO-ANN at 80.96%. This study has been simulated on MATLAB software, using the Power System Analysis Toolbox (PSAT) graphical user and commandline interfaces.
Distributed generations (DG) are one of the upcoming technologies recently used by many electric utilities in all corners of the world. Most of those DG form the microgrid (MG) to serve local loads and can be connected to the grid. This DG’s technology is enabled by utilizing renewable energy sources (REs) that are ecofriendly; however, these REs are intermittent by their nature, so controlling a power electronic device interfaced with them to be connected to the grid is another challenge. Many researchers have worked on the inverters’ control in MG. This study also elaborates on the control strategy for inverters adapted to REs for proper control of voltage and frequency used in an islanded microgrid. The study proposes a hybrid control strategy made of the virtual impedance droop control with arctan function and model predictive control. Extensive simulations have been carried out to validate the proposed control strategy’s effectiveness in terms of rapid transient response and stabilization of voltage, frequency, and power equitability among the microsources in the islanded microgrid.
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