Conference Agenda

Numerous factors, including sudden load reductions, switching transient loads, lightning strikes, and malfunctions of control devices, can result in overvoltage. Overvoltage can harm associated power supply components and result in insulation failure, electronic component damage, flashovers, etc. A machine learning technique called a "neural network" estimates computation results that depend on a lot of inputs. For a variety of reasons, neural networks have recently been used to manage and optimize the power system. This paper presents an artificial neural network (ANN)-based approach to determining overvoltages in power systems. To simulate overvoltages, many simulations were performed in Electromagnetic Transient Program (EMTP). Variations of parameters ofinterest that have an influence on overvoltages were made using JavaScript that was connected to EMTP models. The extraction of characteristic parameters from overvoltage waveshape is a demanding task, and it was conducted in MATLAB, as was a overvoltage classification methodology based on neural networks. Results were presented and discussed.

As more power electronics are introduced into the power system, its stability is impacted, e.g., through undesired interactions. One such interaction is called sub-synchronous control interaction (SSCI), an example being an interaction between a DFIG wind farm and a series compensated line. In this paper, two methods are used to assess the risk of SSCI: the reactance crossover method, and the Nyquist criterion. The analysis is performed on three case studies: one system based on the IEEE First Benchmark System, and the other system is modelled as a typical Swedish transmission system with two different degrees of series compensation. Both methods predict SSCI in all three case studies, with the Nyquist criterion being able to predict the oscillation frequency more accurately. To mitigate the sub-synchronous oscillations, a PV farm is implemented and placed in parallel to the wind farm. The performed simulations show that it is able to damp the oscillations successfully in all case studies.

This paper presents an interpolation-based solutionthat allows the application of traveling wave (TW)-basedalgorithms even using sampling rates lower than those typicallyconsidered as a requirement for classical approaches. Twophasor (PH)-based and one TW-based fault location methodsare compared with the proposed technique and applied throughAlternative Transients Program (ATP) fault simulations ina 500 kV/60 Hz Brazilian double circuit series-compensatedtransmission lines with high-voltage direct current lines (HVDCs)and static var compensators (SVCs) in the vicinity. The resultsshow that the proposed interpolation-based technique is reliableand suitable for TW-based fault location using data records withsampling rates lower than those used in commercially availableequipment. Moreover, its accuracy is comparable to classicalTW-based fault location solutions, revealing its usefulness forpractical application when only traditional digital fault recorders(DFRs) are available.

This paper presents an interpolation with Backward Euler (BE) method that enhances the switching simulations accuracy in an electromagnetic transient (EMT) simulation with a fixed time-step. Because of the switching operations on discrete instants, the simulations can show artificial voltage spikes and numerical oscillations (chatter), compromising the accuracy and producing misleading results. We thus present a method that eliminates the spurious switching losses and improves the accuracy by combining interpolation and extrapolation with a half-time-step BE solution over existing interpolation-based approaches to resolve the issues above. In addition, this improved method requires the same calculation steps as the industry-accepted instantaneous interpolation. Analytical discussion reinforced by the simulation studies for a simple and complex power electronic circuits demonstrates the accuracy and efficacy of the proposed interpolation with BE method.

The impedance-based stability analysis (IBSA) is an effective method for identifying instability issues caused by gridconnected inverter-based resources (IBRs). The electromagnetic transient (EMT)-level positive sequence and dq-frame frequency scanning methods (p-scan and dq-scan, respectively) are widely used to measure the impedance models of IBRs. The p-scan is easier to implement but has inaccuracy issue because it ignores the mirror frequency effect (MFE). The dq-scan is accurate but cannot differentiate the resonance and mirror frequencies. This paper proposes a new EMT-level coupled sequence domain (CSD) multi-input multi-output (MIMO) frequency scanning method (CSD-scan) in stationary frame. The CSD-scan usage in IBSA 1) accounts for the MFE; 2) differentiates the resonance and mirror frequencies; 3) requires no coordinates transformation for perturbation and measurement signals; 4) significantly reduces computational burden compared to the existing coupled sequence scanning method. This paper also demonstrates the impedance transformation relation between dq-domain and stationary frame CSD. The accuracy and time efficiency of the proposed CSD-scan is validated in both IBSA and EMT simulations on a weak grid test case incorporating full-size converter (FSC)-based wind park (WP) by comparing with p- and dq-scans.

This paper presents an accuracy analysis from the application of techniques for the extraction of characteristic data from current and voltage signals in transmission line fault location based on traveling wave theory. The techniques used were the empirical mode decomposition and the variational mode decomposition associated with Teager energy operator, which helps to identify the instantaneous energy of the first intrinsic mode function. The simulation of the power system was carried out using the MATLAB/Simulink® software, for a test system consisting of a 200 km long transmission line between two Thevenin equivalents. Data is obtained from both terminals. The
fault resistance was fixed at 100 Ω, but the incidence angle, the sampling rate and the fault location were varied for all fault types. The numerical and graphical results proved that the techniques can extract the characteristic data of the current and voltage signals and estimate the fault distance to the terminal with high accuracy, depending only on the sampling rate adopted.