Energie NB Power (Canada)

Geomagnetic disturbances trigger quasi-dc currents that flow through grounding circuits into power systems, mostly through power transformers. Geomagnetically-induced currents (GICs) can cause severe harmonic distortions, disruption in reactive power flow, and/or thermal damage to power transformers. These adverse effects of GIC events on power transformers depend on the GIC magnitude, transformer core design, grounding circuit, and winding configuration. This paper analyzes the effects of grounding circuit designs on the adverse impacts of GIC flows in power transformers. These effects are analyzed through experimental tests carried out using laboratory 3ϕ, multicore power transformers. Experimental tests are performed for several grounding circuit designs for the test power transformers. Normal, magnetizing inrush, line-to-line, and line-to-neutral fault conditions are conducted for each tested grounding circuit design. Results of experimental tests show that grounding circuits with low impedance have minor effects on the GIC flows, while grounding circuits with high impedance tend to reduce the magnitude of the GIC. In addition, test results demonstrate that grounding circuits have little influence on the harmonic distortion (mainly the 2nd harmonic component) created by a GIC flow.

Accurate long-term load forecasting (LTLF) is essential for strategic resource allocation, infrastructure development, and resilient power system planning, particularly in today’s dynamic power grid landscape shaped by high penetration of Behind-the-Meter (BTM) generation, Distributed Energy Resources (DERs), Electric Vehicles (EVs), and Demand Side Management (DSM). This study proposes a hybrid approach combining a Random Forest (RF) algorithm for trend extraction and an Artificial Neural Network (ANN) for residual modeling to forecast provincial electricity demand over a 20 year horizon. The methodology includes a comprehensive data preprocessing pipeline capable of harmonizing diverse data resolutions and synthesizing future projections under varying development scenarios. Both point forecasts and probabilistic forecasts are produced, where the latter employs Monte Carlo simulations with time-varying noise injection to explicitly capture and communicate long-term uncertainty. The proposed method is practically implemented using NB Power as a case study, achieving a Root Mean Squared Error (RMSE) of 162.80 MW, a Mean Absolute Error (MAE) of $\mathbf{1 1 2. 7 0 ~ M W}$, and a nearzero mean error of -5.36 MW on test data, with $68.5 \%$ overestimations and $31.5 \%$ underestimations. Provided interpretation of results enhances the understanding of future load trajectories and supports evidence-based decisions for long-term utility planning and risk management.

Geomagnetically induced currents (GICs) are currents dominated by dc components that flow through grounding circuits into power systems. These currents are typically initiated by geomagnetic disturbances, and flow into power systems through power transformers with grounded windings. The flow of a GIC through a power transformer creates adverse impacts, including high levels of harmonic distortion in the currents flowing through the grounded windings, overheating of transformer windings, and significant disruptions in the reactive power flow through the affected transformer. Adverse impacts of GICs on power transformers depend on various factors, among which are the core design (multi-cores, 3-limb, or 5-limb), configuration of primary and secondary windings, and loading levels. This paper presents and discusses the effects of loading levels on the harmonic distortion due to GIC flows in power transformers. Tests are carried out using a laboratory power transformer with multi-core (three single phase transformers). Various values of GIC flows are tested for different loading levels. Test results conclude that the loading levels have minor ifluence on the harmonic distortion created by the GIC flow. In addition, test results show that the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> harmonic component remains the dominant harmonic component due to the GIC flow in a power transformer, regardless of the loading level.

High-dimensional transport equations frequently occur in science and engineering. Computing their numerical solution, however, is challenging due to its high dimensionality. In this work we develop an algorithm to efficiently solve the transport equation in moderately complex geometrical domains using a Galerkin method stabilized by streamline diffusion. The ansatz spaces are a tensor product of a sparse grid in space and discontinuous piecewise polynomials in time. Here, the sparse grid is constructed upon nested multilevel finite element spaces to provide geometric flexibility. This results in an implicit time-stepping scheme which we prove to be stable and convergent. If the solution has additional mixed regularity, the convergence of a 2d-dimensional problem equals that of a d-dimensional one up to logarithmic factors. For the implementation, we rely on the representation of sparse grids as a sum of anisotropic full grid spaces. This enables us to store the functions and to carry out the computations on a sequence regular full grids exploiting the tensor product structure of the ansatz spaces. In this way existing finite element libraries and GPU acceleration can be used. The combination technique is used as a preconditioner for an iterative scheme to solve the transport equation on the sequence of time strips. Numerical tests show that the method works well for problems in up to six dimensions. Finally, the method is also used as a building block to solve nonlinear Vlasov-Poisson equations.

Maximum power point tracking (MPPT) is a technique employed for with variable-power sources, such as solar, wind, and ocean, to maximize energy extraction under all conditions. The commonly used perturb and observe (P&O) and incremental conductance (INC) methods have advantages such as ease of implementation, but they also have the challenge of selecting the most optimized perturbation step or increment size while considering the trade-off between convergence time and oscillation. To address these issues, an MPPT solution for grid-connected photovoltaic (PV) systems is proposed that combines the golden section search (GSS), P&O, and INC methods to simultaneously achieve faster convergence and smaller oscillation, converging to the MPP by repeatedly narrowing the width of the interval at the rate of the golden ratio. The proposed MPPT technique was applied to a PV system consisting of a PV array, boost chopper, and inverter. Simulation and experimental results verify the feasibility and effectiveness of the proposed MPPT technique, by which the system is able to locate the MPP in 36 ms and regain a drifting MPP in approximately 30 ms under transient performance. The overall MPPT efficiency is 98.99%.

The classical control mechanisms of the wind turbines are generally based on precise modelling approaches to ensure robust and effective interplay between the wind turbines and the main power grids in both autonomous and grid‐connected modes. This study presents an innovative intelligent control system for the doubly fed induction generator wind turbines. The proposed system uses model‐free control polices. The online controller is based on a policy iteration reinforcement learning paradigm along with an adaptive actor‐critic technique. It is shown to be robust against the turbine's high non‐linearities and stochastic variations in the input–output conditions. These are associated with single and double rotor doubly fed large‐scale induction generators driven by wind turbines in the range of 5–7 MW. The performance of the controller is validated against challenging scenarios of coexisting undesired situations like severe wind changes with load excursions and abrupt shifts in the loads.

A Probabilistic Safety Assessment (PSA) for Point Lepreau Generating Station has been completed as part of the plant Refurbishment Project. The main objective of this PSA is to provide insights into plant safety design and performance, including the identification of dominant risk contributors and assessing options for reducing risk. The scope of this assessment covers Level 1 and 2 PSA and includes internal events for full power and shutdown, internal fires and internal floods as well as PSA-based seismic margin assessment (SMA) for full power operation. Following the accident sequence quantification for internal events, fire and flood, the results were integrated to provide an overall estimation of the Severe Core Damage Frequency (SCDF) and the Large Release Frequency (LRF) for the refurbished Point Lepreau plant. Importance analysis was performed on the integrated results to identify risk-significant failures, using Fussell-Vesely and Risk Achievement Worth indices, and risk-contributors using Risk Reduction Worth indices. Based on the importance measures, analysis was performed to evaluate the sensitivity of the SCDF and LRF results to the dominant contributors. Uncertainty analysis was also performed to provide qualitative discussions and quantitative measures of the uncertainties in the results of the PSA, namely the frequency of severe core damage or external releases. Based on the results, recommendations were made to improve maintenance, testing, training procedures as well as housekeeping. Also, results of the Level 1 and 2 PSA have been used to determine the safety important systems and components. This paper discusses the key results and recommendations of the Level 2 PSA as well as the methodology used to determine the safety important systems.

The application of optimal reactive power flow (ORPF) to the New Brunswick (NB) power system is presented. The potential benefits and the real-time implementation problems of ORPF are discussed. Some important issues of real-time implementation on ORPF, such as frequency of running, the number of activated control variables, and the order of adjustment of different controls, are discussed. The application of ORPF on the NB power network has shown two major benefits: (1) an improvement in the voltage profile and voltage stability, and (2) a saving in active power loss. The improvement in the voltage profile can cause fewer violations and a more stable system from the voltage point of view. A reduction in active power loss gained from ORPF can save a significant amount of money. The total ideal savings for the year 1997 predicted in the study was in excess of $900000, however, only 10 to 30% of this amount is realistically obtainable due to operational and other constraints. These savings can be gained simultaneously with the improvement of the voltage profiles.

The paper presents the development of a fuzzy logic inference technique for the hierarchical level one (HL1) reliability for a power system. The inference process is rule based where fuzzy values are assigned to the fuzzy variables. The average unit size, system ratio, forced outage rate and the load factor constitute the left hand side of the fuzzy rule whereas the loss of load expectation is the right hand side consequent. The suggested technique for inferring rather than calculating the loss of load expectation has the advantage of handling the uncertainty that may be associated with the system data. This approach shows satisfactory results. >