Transmission voltage unbalance evaluation

Rossman, James B.; Laughner, Theo; Murphy, Anthony M.; Marler, David E.; Kobet, Gary

doi:10.1109/fiiw.2012.6378338

Cited by 6 publications

(5 citation statements)

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“…The voltage or current unbalance for each bus as defined in NEMA MG-1, 1998 (Rossman et al , 2012) for non-grounded three-phase loads in terms of negative-sequence component of voltage or current is calculated as follows: …”

Section: Problem Formulationmentioning

confidence: 99%

Rooftop photovoltaic system allocation to improve the distribution transformers life span using golden ratio optimization algorithm

Sobouti

Bigdeli

Azizian

2021

WJE

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Purpose This paper aims to evaluate the effect of optimal use of rooftop photovoltaic (PV) systems on improving the loss of life (LOL) of distribution transformers, reducing power losses as well as the unbalance rate of the 69-bus distribution network. Design/methodology/approach The problem is studied in three scenarios, considering different objective functions as multi-objective optimization in balanced and unbalanced operations. Meta-heuristic golden ratio optimization method (GROM) is used to determine the optimal size of the rooftop PV in the network. Findings The simulation results show that in all scenarios, the GROM by optimally installing the rooftop PV is significantly capable to reduce the transformer distribution loss of loss, unbalance rate and power loss as well as reduce the temperature of the oil and transformer winding. Also, the lowest %LOL, power loss and unbalance rate occurred in the second scenario for the balanced network and first scenario, respectively. In addition, the results showed that the unbalance of the network results in increased power losses and LOL of the distribution transformer. Originality/value The better capability of GROM is proved compared with the grey wolf optimization algorithm with better objective function and by achieving better values of LOL, unbalance rate and power loss. The results also showed that the %LOL, unbalance and power losses are weakened compared to without considering the PV cost but the achieved results are realistic and cost-effective.

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Section: Problem Formulationmentioning

confidence: 99%

Rooftop photovoltaic system allocation to improve the distribution transformers life span using golden ratio optimization algorithm

Sobouti

Bigdeli

Azizian

2021

WJE

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“…However, it is observed that the off-diagonal elements are small relative to the diagonal terms. The motivation to study the behavior of the DSRs with different impedance models is that the transmission lines in many power systems are not transposed and as a result have unbalanced immittances [18]- [21]. One aim of this study is to investigate the differences in DSR design results that occur between using the balanced immittance model and using the unbalanced immittance model.…”

Section: Case Study Characteristics and Technologymentioning

confidence: 99%

“…No previous papers in the literature other than the work in [16] and [17] studied the application of DSRs in unbalanced three phase transmission systems, and many of the transmission lines in the US are unbalanced [18][19][20][21]. This work is devoted to investigating the application of DSRs in unbalanced transmission for load growth.…”

Section: Introductionmentioning

confidence: 99%

Load growth and power flow control with DSRs: Balanced vs unbalanced transmission networks

Omran

Broadwater

Hambrick

et al. 2017

Electric Power Systems Research

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An important issue in today's power system is the need to analyze and determine the adequacy of transmission capacity. There is a need for approaches to increase transmission system capacity without construction of new transmission facilities, all while assuring secure operation of the grid. Distributed Series Reactors (DSRs) are a new smart grid technology that can be applied to control flows in transmission or distribution systems. DSRs can be used to balance phase flows in a single line as well as to control the distribution of flow in a meshed system. This paper investigates DSRs to control power flow to alleviate overloads due to increased power transfer. The IEEE 39 bus standard model is modified to a 3-phase unbalanced transmission model with 345 kV lines that accounts for tower geometry. Using the symmetrical components transformation, a balanced, 3-phase model is then derived from the unbalanced, 3-phase model. DSR designs based on the unbalanced, 3-phase model and the balanced, 3-phase model are compared and used to demonstrate the effectiveness of DSR control in handling load growth. Only unbalanced impedances are addressed i.e., non-transposed lines, but the effects of impedance unbalance are shown to be significant on the resulting DSR design.

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“…The authors of [3] determined the average voltage unbalance within an operational power transmission system using four seasonal periods for twenty-two sites and determined that line configuration (e.g., transposed lines) and line length do not significantly influence voltage unbalance. The authors calculated voltage unbalance using Supervisory Control And Data Acquisition (SCADA) and Digital Fault Recorder (DFR) data collection methods.…”

Section: Introductionmentioning

confidence: 99%

“…The average voltage unbalance is calculated as 0.59% and 0.64% using the SCADA and DFR data collection methods, respectively. The authors of [3] highlight that the SCADA system did not detect a 1.5% voltage unbalance condition reported by a large industrial customer; thus, the work in [3] is not predictive and highlights the need for a voltage unbalance prediction method within operational power systems.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques to Predict Voltage Unbalance in a Power Transmission System

Boyd,

Reising,

Murphy

et al. 2024

IEEE Open J. Ind. Applicat.

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Voltage unbalance is a growing issue that, among other things, can impact three-phase motor and drive loads, result in nuisance tripping of generation units and capacitor banks, and prevent optimization of conservative voltage regulation strategies. This difference between the three phases of voltage delivered to customers can damage the equipment of these customers as well as negatively impact the power system itself. This work presents an approach for predicting voltage unbalance using machine learning. Historical megawatt and megavar data-obtained through a Supervisory Control And Data Acquisition (SCADA) system-are used to train an Artificial Neural Network model as a binary classifier with a portion of the data serving to validate the trained model. Voltage unbalance is predicted at an accuracy above 95% for eight substations within the power utility's Extra-High Voltage transmission network and over 91% for all forty-two substations. The trained model is tested in a manner that would be employed using simulated data generated by state estimation software. This simulated data validates the model's capacity to predict the substation buses that would experience voltage unbalance.

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Transmission voltage unbalance evaluation

Cited by 6 publications

References 0 publications

Rooftop photovoltaic system allocation to improve the distribution transformers life span using golden ratio optimization algorithm

Rooftop photovoltaic system allocation to improve the distribution transformers life span using golden ratio optimization algorithm

Load growth and power flow control with DSRs: Balanced vs unbalanced transmission networks

Machine Learning Techniques to Predict Voltage Unbalance in a Power Transmission System

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