Conference Agenda

Non-intrusive load monitoring is one of the key tools in demand-side management (DSM). Recent advancements in the computational power of processors have accentuated the role of machine learning algorithms e.g., clustering, as a key function in the NILM solutions applied on power grids. In event-based NILM methods, the algorithm detects the transient states (load events) and clusters them based on the similarity of different features of the transient state. In this study, the performances of eight clustering algorithms are comprehensively investigated and the impact of choosing different input signals, e.g., P , Q, and I, on transient states clustering is analyzed. Various input signals from the BLUED dataset are fed to the clustering algorithms. By comparing the evaluation metrics including shape-based and ground-truth-based metrics, it is observed that the OPTICS algorithm fed by dual-stream input streams outperformed the rest of the investigated clustering algorithms and input sets. OPTICS algorithm groups load events based on their density in multi-dimensional space, using a dynamic radius. The OPTICS algorithm, as the best-performing transient state clustering algorithm for the low-frequency NILM purpose, is then tested with the downsampled input data in a wide frequency range, to observe the impact of the data-sampling frequency on the results, which simplifies the use of clustering algorithms in future studies.

Offline electromagnetic transient (EMT) simulation is a very time-consuming activity for large-scale and complex power systems. Hydro-Québec (HQ) is involved in the development of EMT simulation tools, one of which can operate both in real-time (RT) and offline. This software heavily relies on parallel processing to achieve high-level performance. However, the offline mode is currently limited as it targets only single system image computers. As the offline mode uses the Message Passing Interface (MPI) standard to implement its parallel processing, porting the offline mode to PC clusters is the logical step to increase the offline simulation capabilities of HQ EMT simulation software. This paper evaluates the performance of different communication fabrics for the execution of offline EMT simulation operating in parallel with MPI. The performance metrics used for this evaluation are first discussed. The evaluated communication fabrics are then presented and tested with an offline simulation of the HQ power transmission system.

This paper introduces new techniques for efficientuse of electromagnetic transient simulators combined withoptimization algorithms to optimize power systems withconverter-tied renewable resources. This work is motivated byseveral challenges that must be overcome for simulation-basedoptimal design, including high computational burden ofsimulating large switching systems, repetitive nature of thedesign cycle, and large number of variables that need to behandled. Two screening methods are proposed in this paper toidentify the parameters that do not influence the optimal solutionsignificantly and hence can be ignored. Moreover, hybridizationof optimization algorithms and parallel processing techniquesare explored to achieve additional computational benefits. Casestudies of systems with different complexity and number ofvariables are used to demonstrate the effectiveness of theproposed techniques.

This paper presents a new tool for the computation of per-unit-length parameters for transmission line and cable models used for simulating electromagnetic transients (EMT). The proposed methodology is based on the MoM-SO theory and state-of-the-art formulations for the computation of the series impedance and shunt admittance parameters. The new tool has major advantages compared to traditional approaches available in EMT-type software. These advantages include accurate skin and proximity effect modeling, above-ground cable modeling, modeling of stranded wires in cables, representation of multilayer soil, coupled overhead lines and underground cables, etc. This paper presents the new tool together with demonstrations of transient simulations for practical examples.

Modern power grids incorporating inverter-based resources (IBRs) may be liable to persistent oscillations and instability incidents, which jeopardize the reliable operation of the power system. Due to the high number and the complexity of the involved components, an analytical stability assessment of modern power grids is infeasible. A viable approach is the impedancebased stability analysis (ISBA) using impedances extracted from manufacturer-specific electro-magnetic transient (EMT) models via frequency scanning. This paper reviews the EMT-level positive-sequence, dq-frame and αβ-frame frequency scanning techniques and compares their computational efficiencies. The impact of the model reference frame on IBSA precision is also examined on two test cases: I - a full-size converter (FSC)-based wind park (WP) interacts with transmission grid in the supersynchronous frequency range; II - a doubly-fed induction generator (DFIG)-based WP interacts with transmission grid in the sub-synchronous frequency range. Among the compared techniques, ISBA using dq-frame impedance models features the highest accuracy. However, IBSA using positive-sequence or αβ-frame impedance models is sufficiently accurate. The computational speed of the ps-scan is fastest among the presented
techniques. Using αβ-frame models has the slowest computational speed and is, therefore, is not recommended.

Electrical systems have been facing transformations, such as distributed generation insertion, system expansion and regulatory standards in order to increase reliability and quality of
the power supply. Thus, fault location methods must be updated to ensure accuracy in estimating the location of electrical faults. The delay in restoring the system causes damage to utilities and
consumers. Considering this, the current work presents an approach capable of locating faults accurately in radial distribution systems. At first, the distance is estimated using the
travelling wave theory with data from measurements from two terminals. Next, due to the radial characteristic of the system, the proposal aims to mitigate the problem of multiple estimation of
faults. Thus, features are extracted from the voltage and current signals, which are used as inputs of decision trees to identify the fault region. The proposed approach was validated in a medium voltage distribution system, in which the results presented an average error of 0.79% (with a standard deviation of 0.4%) in estimating the fault distances and an average accuracy above 88.7% in identifying the region under fault. Thus, it was demonstrated that the proposed methodology is efficient to locate faults, mitigating the problem of multiple estimation.