A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features

Fei Wang, Kangping Li, Xinkang Wang, Lihui Jiang, Jianguo Ren, Zengqiang Mi, Miadreza Shafie-khah and João P. S. Catalão
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Fei Wang: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
Kangping Li: Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China
Xinkang Wang: Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China
Lihui Jiang: China Resources Power Holdings Company Limited, Shenzhen 518001, China
Jianguo Ren: China Resources Power Holdings Company Limited, Shenzhen 518001, China
Zengqiang Mi: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
Miadreza Shafie-khah: C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal
João P. S. Catalão: C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal

Energies, 2018, vol. 11, issue 7, 1-19

Abstract: 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.

Keywords: distributed photovoltaic; capacity estimation; weather status driven difference features; support vector machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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