Load modeling and calibration techniques for power system studies

Chassin, Forrest S.; Mayhorn, Ebony T.; Elizondo, Marcelo; Lu, Shuai

doi:10.1109/naps.2011.6024878

Cited by 17 publications

(7 citation statements)

References 44 publications

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“…Therefore, the standard inertia of the system can be calculated by the input torque and the measured velocity curve. In practical experiments, the formula is modified to T t − T d = Jα because of the resistance of the system, which is mainly related to the rotation speed [25,28]. When the rotation speed of the system is at a proper level, the resistance of the system is basically stable.…”

Section: Calibration Of Standard Inertiamentioning

confidence: 99%

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Research on the Modeling, Control, and Calibration Technology of a Tracked Vehicle Load Simulation Test Bench

Zhou

Yang

et al. 2019

Applied Sciences

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The load simulation test bench plays an important role in tracked vehicle development. The stability and accuracy of the system have a vital impact on the experimental results. To accurately reproduce the power performance of a tracked vehicle on the test platform, this paper aims to investigate the model, control, and calibration method of the test bench. Firstly, the dynamic model of a tracked vehicle under complex driving conditions is analyzed and established, which takes driving torque as the input and driving wheel speed as the output. Then, considering the uncertainties and disturbances in the system model, a 2-degree-of-freedom (2-DOF) control method combined with a disturbance observer is proposed to solve the stability problem of the system. Furthermore, in order to investigate the accuracy of the simulation on the test bed, a method of calibrating the system by a flywheel set with standard inertia is proposed. In the calibration process, the influence of the system resistance torque and the original mechanical inertia on the results is considered, and the response time of the inertia simulation is analyzed in both a steady and dynamic state. Finally, the load simulation test is carried out with the corrected system. The test results show that the system has a high load simulation accuracy under various load simulation tests.

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Section: Calibration Of Standard Inertiamentioning

confidence: 99%

“…Although many researchers have conducted a lot of work on the simulation of electrical inertia, the existing methods at present are limited to the calibration of the accuracy of a single sensor, and there is no system-level calibration method for a large inertia load simulator [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Research on the Modeling, Control, and Calibration Technology of a Tracked Vehicle Load Simulation Test Bench

Zhou

Yang

et al. 2019

Applied Sciences

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“…A constant power load model is utilized in the OID (1). However, the OID formulation can be broadened to account for constant-impedance, constantcurrent, and constant-power load components (i.e., the socalled ZIP model [33]) by following the method in [34]. For constant impedance loads, the demands are proportional to the voltage magnitude squared; thus, they can be easily incorporated in (3).…”

Section: Remark (Zip Load Model)mentioning

confidence: 99%

Optimal Dispatch of Residential Photovoltaic Inverters Under Forecasting Uncertainties

Dall’Anese

Dhople

Johnson

et al. 2015

IEEE J. Photovoltaics

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Abstract-Efforts to ensure reliable operation of existing lowvoltage distribution systems with high photovoltaic (PV) generation have focused on the possibility of inverters providing ancillary services such as active power curtailment and reactive power compensation. Major benefits include the possibility of averting overvoltages, which may otherwise be experienced when PV generation exceeds the demand. This paper deals with ancillary service procurement in the face of solar irradiance forecasting errors. In particular, assuming that the forecasted PV irradiance can be described by a random variable with known (empirical) distribution, the proposed uncertainty-aware optimal inverter dispatch (OID) framework indicates which inverters should provide ancillary services with a guaranteed a-priori risk level of PV generation surplus. To capture forecasting errors, and strike a balance between risk of overvoltages and (re)active power reserves, the concept of conditional value-at-risk is advocated. Due to AC power balance equations and binary inverter selection variables, the formulated OID involves the solution of a nonconvex mixed-integer nonlinear program. However, a computationally-affordable convex relaxation is derived by leveraging sparsity-promoting regularization approaches and semidefinite relaxation techniques.

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“…The load control law was designed in a hierarchically decentralized manner consisting of two interactive decision layers. In the first layer, a supervisory controller is responsible to gather system level information (e.g., power flow, system topology, generation and load forecast, available responsive loads, among others), and determine the optimal gains for the responsive loads on each bus every few (e.g., [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] minutes. In the second layer, each decentralized load switched ON/OFF probabilistically in real time based on local frequency measurement so that the aggregated load response under each bus matches the desired power determined by the first layer.…”

Section: Executive Summarymentioning

confidence: 99%