6533b7dcfe1ef96bd127359b

RESEARCH PRODUCT

An Improved Load Flow Method for MV Networks Based on LV Load Measurements and Estimations

Valentina CosentinoGiuseppe MarsalaDario Di CaraAntonio CataliottiMauro GaggeroGiovanni TineAntonella RagusaCristiano CervelleraDanilo Maccio

subject

Artificial neural networksBusbarComputer sciencepower system measurement020208 electrical & electronic engineeringArtificial neural networks (ANNs)power system managementpower measurementFlow method02 engineering and technologypower system measurementsload flow (LF)Power (physics)Control theoryload flowsmart grids0202 electrical engineering electronic engineering information engineeringstate estimationElectrical and Electronic Engineeringsmart gridInstrumentationSettore ING-INF/07 - Misure Elettriche E ElettronicheVoltage

description

A novel measurement approach for power-flow analysis in medium-voltage (MV) networks, based on load power measurements at low-voltage level in each secondary substation (SS) and only one voltage measurement at the MV level at primary substation busbars, was proposed by the authors in previous works. In this paper, the method is improved to cover the case of temporary unavailability of load power measurements in some SSs. In particular, a new load power estimation method based on artificial neural networks (ANNs) is proposed. The method uses historical data to train the ANNs and the real-time available measurements to obtain the load estimations. The load-flow algorithm is applied with the estimated load powers, and the MV network state variables are obtained. The proposed method is validated for the real MV distribution network of the island of Ustica. The loads of selected SSs are estimated for two full days of different seasons. In comparison with previous works, satisfactory results are obtained in terms of uncertainty in the calculated power flows, thus suggesting the applicability of the proposed method for real-time monitoring of MV distribution networks.

yearjournalcountryeditionlanguage
2019-02-01
10.1109/tim.2018.2847818http://hdl.handle.net/10447/339937