Real-time ZIP Load Parameter Tracking using Adaptive Window and Variable Elimination with Realistic Synthetic Synchrophasor Data

Rizvi, Safdar; Sadanandan, Sajan K.; Srivastava, Anurag K.

doi:10.1109/ias44978.2020.9334878

Cited by 4 publications

(1 citation statement)

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“…While most power electronics loads are categorized as constant power loads, transducers are classified as constant current loads. The static ZIP load model is simple to use [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Measurement-based ZIP load modelling using opposition based differential evolution optimization

Kumar

Mallik

2023

Eng. Res. Express

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With the large integration of distributed generations, power grids are on the verge of instability if the generation and load side is not synchronized properly. To predict the load, a proper load model should be developed for the purpose of power system monitoring. The ZIP load model is considered in this work due to its simplicity. The parameter of the ZIP load corresponding to constant power, constant impedance, and constant current load must be estimated accurately for predicting the load behavior. In this paper, the measurements are being compared with the estimated value for estimating the load parameters using a suitable parameter tuning method. For ZIP load modeling Measurement-based parameter estimation is applied. In this paper, measurements are simulated by statistically infusing the Gaussian noise into the true value (obtained from the load flow study). The parameter-tuning algorithm iteratively feeds the error discovered from the comparison to determine the optimal solution. In this paper, Opposition-based differential evolution optimization (ODEO) is being proposed as a parameter tuning method for estimating the load parameters from the measurements collected at a given load bus. Measurement of the ZIP load is collected at bus 30 of the NE 39 Bus system after applying step disturbance of voltage at generator 4. The proposed ODEO method is validated and compared with the other tuning methods, like Particle Swarm optimization (PSO) and Grey Wolf optimization (GWO). The proposed ODEO method has a better performance as compared to PSO and GWO.

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