PMU Measurement Based Generator Parameter Calibration by Black-Box Optimization with A Stochastic Radial Basis Function Surrogate Model

Khazeiynasab, Seyyed Rashid; Qi, Junjian

doi:10.1109/naps50074.2021.9449684

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…Using two stage optimization algorithms makes the parameter identification process computationally intensive. A radial basis function surrogate model optimization algorithm was used to tune the parameters of the diesel electric generator transient models [28], [29]. The surrogate optimization searches for the best value by evaluating its surrogate on thousands of points and inputs the best approximation to the objective function to minimize the error.…”

Section: Introductionmentioning

confidence: 99%

High-Fidelity Model of Stand-Alone Diesel Electric Generator With Hybrid Turbine-Governor Configuration for Microgrid Studies

et al. 2022

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Diesel electric generators are an inherent part of remote hybrid microgrids found in remote regions of the world that provide primary frequency response (PFR) to restore system frequency during load or generation changes. However, with inverter-based resources (IBR) integration into microgrids, the IBR control provides a fast frequency response (FFR) to restore the system frequency. Hence, supplementing PFR with FFR requires a sophisticated control system and a high fidelity diesel electric generator model to design these control systems. In this work, a high-fidelity model of a diesel electric generator is developed. Its parameters are tuned using a surrogate optimization algorithm by emulating its response during a load change to a 400 kVA Caterpillar C-15 diesel generator, similar to those found in remote microgrids. The diesel electric generator model consists of a synchronous machine, DC4B excitation with V/Hz limiter, and a proposed modified IEEE GGOV1 engine-governor model (GGOV1D). The performance of the GGOV1D is compared with simple, Woodward DEGOV, and a standard IEEE GGOV1 engine-governor model. Results show that error in the diesel electric generator's response to load changes using the GGOV1D model is lower with an improved frequency response during the arresting and rebound period than the other enginegovernor models.INDEX TERMS Diesel generator modeling, diesel governor, power system dynamics, power system stability, frequency stability, model parameterization, microgrid.

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Section: Introductionmentioning

confidence: 99%

High-Fidelity Model of Stand-Alone Diesel Electric Generator With Hybrid Turbine-Governor Configuration for Microgrid Studies

et al. 2022

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“…With the increasing installation of phasor measurement units (PMUs) that provide high-quality online data to monitor the system status, there is an increasing interest in estimating synchronous generator parameters using synchrophasor data. The black-box optimiza-tion based method in [7] has a good estimation accuracy, but for high-dimensional cases the estimation error will increase. Kalman filter (KF) based methods are another popular methods that have been applied to estimate the generator parameters.…”

Section: Introductionmentioning

confidence: 99%

Generator Parameter Estimation by Q-Learning Based on PMU Measurements

Khazeiynasab

Batarseh

2021

2021 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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In this paper, a novel Q-learning based approach is proposed for estimating the parameters of synchronous generators using PMU measurements. Event playback is used to generate model outputs under different parameters for training the agent in Q-learning. We assume that the exact values of some parameters in the model are not known by the agent in Q-learning. Then, an optimal history-dependent policy for the exploration-exploitation trade-off is planned. With given prior knowledge, the parameter vector can be viewed as states with a specific reward, which is a function of the fitting error compared with the measurements. The agent takes an action (either increasing or decreasing the parameter) and the estimated parameter will move to a new state. Based on the reward function, the optimal action policy will move the parameter set to a state with the highest reward. If multiple events are available, they will be used sequentially so that the updated Q-value can be utilized to improve the computational efficiency. The effectiveness of the proposed approach is validated through estimating the parameters of the dynamic model of a synchronous generator.

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“…In general, there are two categories of the system identification technique; online and offline system identification [8]. Since the model parameters depend on the operating conditions, the offline methods can not estimate the exact values of the parameters.…”

Section: Introductionmentioning

confidence: 99%

Measurement-Based Parameter Identification of DC-DC Converters with Adaptive Approximate Bayesian Computation

Khazeiynasab¹,

Batarseh²

2021

Preprint

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The recent advances in power plants and energy resources have extended the applications of DC-DC converters in the power systems (especially in the context of DC microgrids). Parameter identification can extract the parameters of the converters and generate accurate discrete simulation models. In this paper, we propose a measurement-based converter parameter calibration method by an adaptive Approximate Bayesian Computation with sequential Monte Carlo sampler (ABC SMC), which estimates the parameters related to passive and parasitic components. At first, we propose to find suitable prior distribution for the parameter which we don't know the prior information about them. With having prior distributions, we can use the ABC SMC to find the exact values of the parameters of the converter. We chose the distance function carefully and based on the simulations we assigned the best method for the threshold sequencing. For improving the computationally of the algorithm, we propose an adaptive weight that helps the algorithm to find the optimal values with fewer simulations. The effectiveness of the proposed method is validated for a DC-DC buck converter. The results show that the proposed approach can accurately and efficiently estimate the posterior distributions of the buck parameters subject to gross errors in the prior distributions of the parameters. The proposed algorithm can also be applied to other parameter identifications and optimization applications such as rectifiers, filters, or power supplies, among others.

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PMU Measurement Based Generator Parameter Calibration by Black-Box Optimization with A Stochastic Radial Basis Function Surrogate Model

Cited by 5 publications

References 32 publications

High-Fidelity Model of Stand-Alone Diesel Electric Generator With Hybrid Turbine-Governor Configuration for Microgrid Studies

High-Fidelity Model of Stand-Alone Diesel Electric Generator With Hybrid Turbine-Governor Configuration for Microgrid Studies

Generator Parameter Estimation by Q-Learning Based on PMU Measurements

Measurement-Based Parameter Identification of DC-DC Converters with Adaptive Approximate Bayesian Computation

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