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An intelligent load-shedding strategy was designed and embedded in the special protection system (SPS) to enhance the system stability for an offshore island with high penetration of photovoltaic (PV) systems. To prepare the training dataset for the artificial neural network (ANN), the transient stability analysis of the isolated power system was executed to determine the minimum amount of load to be interrupted to prevent the tripping of diesel generators for the emergency shutdown of PV systems. By selecting various combinations of PV penetration levels, total system load demand, and the frequency decay rate at the instant of PV system tripping as the input neurons of the ANN, the proper load-shedding scheme is derived and stored in the decision knowledge base of the SPS. When the intelligent energy management system detects the tripping of a PV system, the SPS will be triggered to determine the amount of loss to be disconnected and executes the corresponding load interruption. By applying the proposed ANN-based load-shedding scheme in SPS, the amount of customer loading to be interrupted has been reduced dramatically for the restoration of system stability after the emergency shutdown of high-penetration PV system. [ABSTRACT FROM PUBLISHER]

Voltage flicker is one of many serious power quality disturbances, which would deteriorate the stability and operation efficiency of a power system. Effective evaluation of flicker severity plays an important role in the monitoring and control of developing advanced metering infrastructure. Since the evaluation process for voltage flicker is complicated, some inaccurate intermediate results may give rise to the propagation of estimation errors. Therefore, an accurate solution procedure for the calculation of voltage flicker components is proposed in this paper. With the extraction technique for envelope signal and high-frequency resolution mechanism for flicker spectral analysis, the superior performance to achieve the instantaneous flicker severity evaluation can be obtained. [ABSTRACT FROM PUBLISHER]

This study is focused on integration and operation of a single-phase bidirectional inverter with two buck/boost maximum power point trackers (MPPTs) for dc-distribution applications. In a dc-distribution system, a bidirectional inverter is required to control the power flow between dc bus and ac grid, and to regulate the dc bus to a certain range of voltages. A droop regulation mechanism according to the inverter inductor current levels to reduce capacitor size, balance power flow, and accommodate load variation is proposed. Since the photovoltaic (PV) array voltage can vary from 0 to 600 V, especially with thin-film PV panels, the MPPT topology is formed with buck and boost converters to operate at the dc-bus voltage around 380 V, reducing the voltage stress of its followed inverter. Additionally, the controller can online check the input configuration of the two MPPTs, equally distribute the PV-array output current to the two MPPTs in parallel operation, and switch control laws to smooth out mode transition. A comparison between the conventional boost MPPT and the proposed buck/boost MPPT integrated with a PV inverter is also presented. Experimental results obtained from a 5-kW system have verified the discussion and feasibility. [ABSTRACT FROM AUTHOR]

Underfrequency load shedding maintains the power system in a stable condition when a severe disturbance occurs. The underfrequency relay protects the power system against a blackout when the system frequency declines to predetermined settings. This paper determines the shed load at each stage for the underfrequency relay (81L) in a stand-alone microgrid considering multiple scenarios. In addition, the probabilities of all scenarios are defined based on historical meteorological data and the Markov two-state model. The shed load is minimized and the lowest swing frequency is maximized using the genetic algorithms. Due to many inequality constraints in the studied problem, an efficient tree-encoding chromosome and the penalty functions are presented as well. Moreover, applicability of the proposed method is demonstrated using a microgrid test bed with the gas-turbine, wind-turbine generator, and solar photovoltaic. [ABSTRACT FROM PUBLISHER]