A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China
Hebei Key Laboratory of Distributed Energy Storage and Micro-grid (North China Electric Power University), Baoding 071003, China
China Resources Power Holdings Company Limited, Shenzhen 518001, China
C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal
INESC TEC and the Faculty of Engineering of the University of Porto, 4200-465 Porto, Portugal
INESC-ID, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
Author to whom correspondence should be addressed.
Energies 2018, 11(7), 1750; https://doi.org/10.3390/en11071750
Received: 28 April 2018 / Revised: 24 June 2018 / Accepted: 27 June 2018 / Published: 4 July 2018
(This article belongs to the Special Issue Distribution System Operation and Control)
Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.