Optimization Based Parameter And State Estimation Framework For Remote Microgrid Frequency Dynamics Modeling Using Probing Signals From Energy Storage Systems
Bibliographic record
Abstract
The primary aim of this thesis is to deliver an efficient design and selection of probing signal needed to estimate state and parameters representing the power system frequency dynamics with the proposed estimation technique in real-time with minimum computational time and cost. These test cases are designed for power system researchers that need to estimate and control analysis at the remote microgrid level. Case studies are presented that can be simulated at the transmission and distribution level in power grids, and in remote isolated microgrids where the independent system operator (ISO) has control. Increasing utilization of renewable energy sources and their different dynamics has created unknowns in time-varying system inertia and damping constants. Thus, it is difficult to know these parameters at any given time in converter-dominated microgrids. The first part of the work investigates existing probing signals for accurate estimation of inertia and damping constants in microgrids, and describes the design characteristics, considerations, methodology, and accuracy level of different probing signals in determining unknown parameters of a system. The main goal of the first part of this research is to find an effective probing signal with a simple implementation and minimal impacts on power system operation and energy storage systems. The test-case model in this work analyzes non-intrusive excitation signals to perturb a power system model (i.e., square wave, multisine wave, filtered white Gaussian noise, and pseudo-random binary sequence). A moving horizon estimation (MHE) based approach proposed in this work is then implemented with an isolated power system model, and energy storage system (ESS) in MATLAB/Simulink for estimation of inertia and damping constants of a system based on frequency measurements from a local phase-locked-loop (PLL) . The accuracy of parameter estimates alters depending on the chosen probing signal; when estimating inertia and damping constants using MHE with the different probing signals, square waves yielded the lowest error. Remote microgrids such as in Canada and Alaska having diesel generators as the primary energy source are growing to be integrated with renewable energy sources (RESs) for clean and sustainable energy development. However, inverter-based generation shows faster and more stochastic dynamics. It is necessary to develop accurate models of the diesel genset system components to ensure the stability of these systems and proper controller design. The second part of this work presents a simplified linear model developed to represent the frequency dynamics of the detailed diesel generator system and estimated the model using MHE approach. The proposed optimization-based MHE algorithm is employed to accurately provide an estimation of multiple parameters of a simplified diesel generator model. The proposed algorithm uses a developed linearized diesel model and extracts the unknown parameters based on the frequency and power measurements while minimizing cost function for given set constraints on the estimates. The proposed estimation technique could be further applied in system dynamic studies (e.g. stability analysis) in systems with high penetration of converters or for predictive controllers. This work was further validated with the experimental test performed in the power system integration laboratory (PSI) of University of Alaska Fairbanks (UAF) . With the growing distributed energy resources, the complexity in the detailed model representing the large power system network increases and computationally, it becomes intractable to extract the exact dynamics of the system. To tackle this issue, a part of this work presents an idea of designing effective chirp signals that can provide wide range of system dynamics for the noisy measurements without impacting system balance. Based on the data, different methods has been proposed to obtain frequency response analysis to identify the zeros, poles, and eigenvalues of the system. The work has been carried in large multi-area power system network to extract the states and parameters of the system assuming it as a gray-box model with a high accuracy using MHE. A robust dynamic state and parameter estimation technique will be required for adaptive protection and control of power grids with the increasing uncertain resources which includes renewable (photovoltaic,wind), electrical vehicle charging, and demand responses. This work presents a real-time combined state and parameter estimation technique along with the detailed mathematical modeling of system frequency dynamics with an effective design of probing signals. The proposed approach has been successfully verified with experimental and simulation validation steps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".