RT journal article
T1 Site Characterization Index for Continuous Power Quality Monitoring Based on Higher-order Statistics
A1 Florencias Oliveros, Olivia
A1 González de la Rosa, Juan José
A1 Sierra Fernández, José María
A1 Agüera Pérez, Agustín
A1 Espinosa Gavira, Manuel Jesús
A1 Palomares Salas, José Carlos
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 Continuous statistical monitoring
K1 big data
K1 data compression
K1 higher-order statistics (HOSs)
K1 power quality (PQ)
AB The high penetration of distributed generation (DG) has set up a challenge for energy management and consequently for the monitoring and assessment of power quality (PQ). Besides, there are new types of disturbances owing to the uncontrolled connections of non-linear loads. The stochastic behaviour triggers the need for new holistic indicators which also deal with big data of PQ in terms of compression and scalability so as to extract the useful information regarding different network states and the prevailing PQ disturbances for future risk assessment and energy management systems. Permanent and continuous monitoring would guarantee the report to claim for damages and to assess the risk of PQ distortions. In this context, we propose a measurement method that postulates the use of two-dimensional (2D) diagrams based on higher-order statistics (HOSs) and a previous voltage quality index that assesses the voltage supply waveform in a continous monitoring campaign. Being suitable for both PQ and reliability applications, the results conclude that the inclusion of HOS measurements in the industrial metrological reports helps characterize the deviations of the voltage supply waveform, extracting the individual customers' pattern fingerprint, and compressing the data from both time and spatial aspects. The method allows a continuous and robust performance needed in the SG framework. Consequently, the method can be used by an average consumer as a probabilistic method to assess the risk of PQ deviations in site characterization.
PB IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
SN 2196-5625
YR 2022
FD 2022-01
LK http://hdl.handle.net/10498/26494
UL http://hdl.handle.net/10498/26494
LA eng
NO This work was supported by the Spanish Ministry of Science and Innovation (Statal Agency for Research), and the EU (AEI/FEDER/UE) via project PID2019-108953RB-C21 Strategies for Aggregated Generation of Photovoltaic Plants: Energy and Meteorological Operational Data (SAGPVEMOD), and the precedent TEC2016-77632-C3-3-R.
DS Repositorio Institucional de la Universidad de Cádiz
RD 14-nov-2026