Ordinal Optimization Technique for Three-Phase Distribution Network State Estimation Including Discrete Variables

Abstract

This paper has discussed transformer tap position estimation with continuous and discrete variables in the context of three phase distribution state estimation (SE). Ordinal optimization (OO) technique has been applied to estimate the transformer tap position for the first time in unbalanced three phase distribution network model. The results on 129 bus system model have demonstrated that OO method can generate a reliable estimate for transformer exact tap position with discrete variables in distribution system state estimation (DSSE) and also in short period of time. In this paper the node voltages and power losses are calculated for 129 bus network. It is also demonstrated that OO is much faster than other accurate methods such HPSO. The losses obtained with OO are much accurate. In view of this OO performs better than WLS as it provides higher accuracy of the loss calculation. In a distribution network where about 5-6% of electricity generated is lost, accurate estimation of this loss has significant technical and commercial value. The authors believe the technique proposed will help realize those benefits.

Topology-Based Estimation of Missing Smart Meter Readings

“…In these studies, the voltage of nodes containing the nodes at which the pole-transformer is located is estimated with various error rates. The assumed estimation error rate was found to be less than 1% when the aggregated power flow has a 3% measurement error [30]. Other researchers evaluated the voltage estimation error in a more realistic network along with load fluctuations [31].…”

“…In this situation, state estimation in a high-voltage distribution network, which is on the primary side of a pole-transformer, can contribute to estimating the RMS voltage of the pole-transformer . Distribution system state estimators were proposed [30,31] and their robustness against the measurement error was evaluated. …”

A Medium-/Low-Voltage Joint State Estimator Through Linear Uncertainty Propagation

“…The authors in [3] proposed an artificial neural network-based solution to offer error covariance estimation of the pseudo measurements, which are commonly applied in DSSE to address the low-observability issue. An ordinal optimization-based state estimator was introduced in [4] to jointly estimate the three-phase states along with transformer tap positions. The authors in [5] proposed a Bayesian estimator to deal with measurements or power flows in distribution systems with non-Gaussian behaviors.…”

Screening of optimal structure among large-scale multi-state weighted k-out-of-n systems considering reliability evaluation

“…For example, reference [16] incorporated the OO into automation learning algorithm to improve the learning efficiency. The most important characteristics of OO is the consideration of "order" rather than the "value" during optimization [17], [18]. Therefore, an efficient performance approximation method to achieve a performance order of structures is inevitable.…”