Voltage control using smart transformer via dynamic optimal setpoints and limit tolerance in a residential distribution network with PV sources

Manojkumar, Rampelli; Kumar, Chandan; Ganguly, Sanjib; Gooi, Hoay Beng; Mekhilef, Saad

doi:10.1049/iet-gtd.2020.0213

Cited by 17 publications

(5 citation statements)

References 22 publications

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“…1(a). For this the power flow solution is obtained considering ST LVAC terminal i.e., bus 1 as slack bus and remaining buses are considered as load buses [28]. The ST based meshed hybrid distribution network is a 16-bus system as shown in Fig.…”

Section: Proposed Power Flow Solution Methodsmentioning

confidence: 99%

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Impact of Optimal Control of Distributed Generation Converters in Smart Transformer Based Meshed Hybrid Distribution Network

Kumar¹,

Manojkumar

Ganguly

et al. 2021

IEEE Access

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Section: Proposed Power Flow Solution Methodsmentioning

confidence: 99%

“…The average voltage deviation index (V ad ) is considered along with the worst maximum and minimum bus voltages (V wmin and V wmax ) [28] to know the voltage profile of the system. The V ad is determined using ( 21)…”

Section: ) Voltage Profilementioning

confidence: 99%

Impact of Optimal Control of Distributed Generation Converters in Smart Transformer Based Meshed Hybrid Distribution Network

Kumar¹,

Manojkumar

Ganguly

et al. 2021

IEEE Access

Self Cite

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“…Its primary winding is connected to the high-voltage power grid, and the secondary winding is connected to protection devices, measuring instruments, metering devices, and other equipment. It is used to convert the high voltage signal of the primary side into the low voltage signal of the secondary side for the safe collection of the secondary side protection, measurement and control, and metering equipment [2][3].…”

Section: Introductionmentioning

confidence: 99%

Voltage transformer metering error state prediction method based on GA-BP algorithm

Gao,

Zhao,

Jiang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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The metering accuracy of the voltage transformer is related to the normal operation of the power system, and the metering results can be optimized through the prediction of the error state. In this paper, according to the generation mechanism of the measurement error of the transformer, the maximum information coefficient is used to extract the error characteristic quantity, and the measurement perturbation model is constructed by combining the ambient temperature and the secondary load factor. Due to the specificity of the ambient temperature, a BP neural network is also used to compensate for the temperature of the perturbation model, which prepares for the improved BP neural network based on a genetic algorithm to recognize the error data. Finally, the simulated operation of the three-phase voltage transformer and the measured data of the wiring substation were utilized for validation, respectively. With the help of three-phase CVT simulation, the error change of A-phase simulated CVT amplitude information at the 4001st sampling point is 0.0962%, and the error change of phase information is -4.572′.GA-BP neural network also has high sensitivity to the difficult-to-detect asymptotic error and is able to realize the error calibration of voltage transformer.

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“…The connection of the PV panel to the main grid impacts the voltage stability of the system and leads to disturbances [ 9 ]. Therefore, several solutions have been introduced to mitigate the impact of penetrating the main grid [ 10 ], such as a meter data management system [ 11 ], smart transformers [ 12 ], smart inverters [ 13 ], an outage management system [ 14 ], and solar irradiance prediction [ 15 ]. Both grid-tied and off-grid PV systems can be improved if information about the future values of solar irradiance is accurately predicted [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data

Alzahrani

2022

Sensors

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Concerns over fossil fuels and depletable energy sources have motivated renewable energy sources utilization, such as solar photovoltaic (PV) power. Utilities have started penetrating the existing primary grid with renewable energy sources. However, penetrating the grid with photovoltaic energy sources degrades the stability of the whole system because photovoltaic power depends on solar irradiance, which is highly intermittent. This paper proposes a prediction method for non-stationary solar irradiance. The proposed method uses an adaptive extreme learning machine. The extreme learning machine method uses approximated sigmoid and hyper-tangent functions to ensure faster computational time and more straightforward microcontroller implementation. The proposed method is analyzed using the hourly weather data from a specific site at Najran University. The data are preprocessed, trained, tested, and validated. Several evaluation metrics, such as the root mean square error, mean square error, and mean absolute error, are used to evaluate and compare the proposed method with other recently introduced approaches. The results show that the proposed method can be used to predict solar irradiance with high accuracy, as the mean square error is 0.1727. The proposed approach is implemented using a solar irradiance sensor made of a PV cell, a temperature sensor, and a low-cost microcontroller.

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Voltage control using smart transformer via dynamic optimal setpoints and limit tolerance in a residential distribution network with PV sources

Cited by 17 publications

References 22 publications

Impact of Optimal Control of Distributed Generation Converters in Smart Transformer Based Meshed Hybrid Distribution Network

Impact of Optimal Control of Distributed Generation Converters in Smart Transformer Based Meshed Hybrid Distribution Network

Voltage transformer metering error state prediction method based on GA-BP algorithm

Short-Term Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data

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