Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning

Recent years have seen a rich literature of data-driven approaches designed for power grid applications. However, insufficient consideration of domain knowledge can impose a high risk to the practicality of the methods. Specifically, ignoring the grid-specific spatiotemporal patterns (in load, generation, and topology, etc.) can lead to outputting infeasible, unrealizable, or completely meaningless predictions on new inputs. To address this concern, this paper investigates real-world operational data to provide insights into power grid behavioral patterns, including the time-varying topology, load, and generation, as well as the spatial differences (in peak hours, diverse styles) between individual loads and generations. Then based on these observations, we evaluate the generalization risks in some existing ML works causedby ignoring these grid-specific patterns in model design and training

GPU-Resident Sparse Direct Linear Solvers for Alternating Current Optimal Power Flow Analysis

Integrating renewable resources within the transmission grid at a wide scale poses significant challenges for economic dispatch as it requires analysis with more optimization parameters, constraints, and sources of uncertainty. This motivates the investigation of more efficient computational methods, especially those for solving the underlying linear systems, which typically take more than half of the overall computation time. In this paper, we present our work on sparse linear solvers that take advantage of hardware accelerators, such as graphical processing units (GPUs), and improve the overall performance when used within economic dispatch computations. We treat the problems as sparse, which allows for faster execution but also makes the implementation of numerical methods more challenging. We present the first GPU-native sparse direct solver that can execute on both AMD and NVIDIA GPUs. We demonstrate significant performance improvements when using high-performance linear solvers within alternating current optimal power flow (ACOPF) analysis. Furthermore, we demonstrate the feasibility of getting significant performance improvements by executing the entire computation on GPU-based hardware. Finally, we identify outstanding research issues and opportunities for even better utilization of heterogeneous systems, including those equipped with GPUs

GPU-resident sparse direct linear solvers for alternating current optimal power flow analysis

Integrating renewable resources within the transmission grid at a wide scale poses significant challenges for economic dispatch as it requires analysis with more optimization parameters, constraints, and sources of uncertainty. This motivates the investigation of more efficient computational methods, especially those for solving the underlying linear systems, which typically take more than half of the overall computation time. In this paper, we present our work on sparse linear solvers that take advantage of hardware accelerators, such as graphical processing units (GPUs), and improve the overall performance when used within economic dispatch computations. We treat the problems as sparse, which allows for faster execution but also makes the implementation of numerical methods more challenging. We present the first GPU-native sparse direct solver that can execute on both AMD and NVIDIA GPUs. We demonstrate significant performance improvements when using high-performance linear solvers within alternating current optimal power flow (ACOPF) analysis. Furthermore, we demonstrate the feasibility of getting significant performance improvements by executing the entire computation on GPU-based hardware. Finally, we identify outstanding research issues and opportunities for even better utilization of heterogeneous systems, including those equipped with GPUs

DEVELOPMENT OF AN IMPLICITLY COUPLED ELECTROMECHANICAL AND ELECTROMAGNETIC TRANSIENTS SIMULATOR FOR POWER SYSTEMS

The simulation of electrical power system dynamic behavior is done using tran- sient stability simulators (TS) and electromagnetic transient simulators (EMT). A Transient Stability simulator, running at large time steps, is used for studying rela- tively slower dynamics e.g. electromechanical interactions among generators and can be used for simulating large-scale power systems. In contrast, an electromagnetic transient simulator models the same components in finer detail and uses a smaller time step for studying fast dynamics e.g. electromagnetic interactions among power electronics devices. Simulating large-scale power systems with an electromagnetic transient simulator is computationally inefficient due to the small time step size in- volved. A hybrid simulator attempts to interface the TS and EMT simulators which are running at different time steps. By modeling the bulk of the large-scale power system in a transient stability simulator and a small portion of the system in an electromagnetic transient simulator, the fast dynamics of the smaller area could be studied in detail, while providing a global picture of the slower dynamics for the rest of power system. In the existing hybrid simulation interaction protocols, the two simulators run independently, exchanging solutions at regular intervals. However, the exchanged data is accepted without any evaluation, so errors may be introduced. While such an explicit approach may be a good strategy for systems in steady state or having slow variations, it is not an optimal or robust strategy if the voltages and currents are varying rapidly, like in the case of a voltage collapse scenario. This research work proposes an implicitly coupled solution approach for the combined transient stability and electromagnetic transient simulation. To combine the two sets of equations with their different time steps, and ensure that the TS and EMT solutions are consistent, the equations for TS and coupled-in-time EMT equations are solved simultaneously. While computing a single time step of the TS equations, a simultaneous calculation of several time steps of the EMT equations is proposed. Along with the implicitly coupled solution approach, this research work also proposes to use a three phase representation of the TS network instead of using a positive-sequence balanced representation as done in the existing transient stability simulators. Furthermore a parallel implementation of the three phase transient stability simulator and the implicitly coupled electromechanical and electromagnetic transients simulator, using the high performance computing library PETSc, is presented. Re- sults of experimentation with different reordering strategies, linear solution schemes, and preconditioners are discussed for both sequential and parallel implementation.Ph.D. in Electrical Engineering, December 201

Towards Efficient Alternating Current Optimal Power Flow Analysis on Graphical Processing Units

We present a solution of sparse alternating current optimal power flow (ACOPF) analysis on graphical processing unit (GPU). In particular, we discuss the performance bottlenecks and detail our efforts to accelerate the linear solver, a core component of ACOPF that dominates the computational time. ACOPF analyses of two large-scale systems, synthetic Northeast (25,000 buses) and Eastern (70,000 buses) U.S. grids [1], on GPU show promising speed-up compared to analyses on central processing unit (CPU) using a state-of-the-art solver. To our knowledge, this is the first result demonstrating a significant acceleration of sparse ACOPF on GPUs

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time