Virtual Acquisition Method for Operation Data of Distributed PV Applying the Mixture of Grey Relational Theory and BP Neural Work

doi:10.12204/j.issn.1000-7229.2021.01.014

Abstract

Abstract:

In order to realize the full coverage collection of operation and maintenance data for the distributed photovoltaic station with the characteristics of many scattered and disordered points in a wide area, the best way is to configure a set of data acquisition devices for each distributed photovoltaic station. Therefore, it will also face the problems such as huge investment cost and heavy operation and maintenance tasks. This paper proposes a virtual acquisition method for distributed photovoltaic data applying the mixture of grey relational degree and BP neural network, and realizes the operation and maintenance data acquisition of the distributed photovoltaic station in the whole region under the condition of installing a small number of data acquisition devices. A distributed photovoltaic power station in a certain region of Jiangsu province is selected as the research object. Firstly, the historical operation and maintenance data of a distributed photovoltaic power station with data acquisition devices installed in the region are analyzed by using the grey relational theory, and the characteristic curves of irradiance and distributed photovoltaic output are obtained. Then, the correlation degree between the real-time daily irradiance information and historical irradiance data of the photovoltaic station to be virtually collected in the region is calculated, and the historical date with correlation degree above 0.9 is selected as the similar date, and then the BP neural network data virtual acquisition model is established according to the historical data of similar days, which is used to realize the virtual collection of distributed photovoltaic data in the region. Finally, the case verifies that the photovoltaic output power collected by this method has high precision and that the method can realize the virtual collection of photovoltaic output power data in the grid area.

Key words: distributed photovoltaic power station, grey relevance theory, virtual acquisition, neural network

CLC Number:

TM711

ZHANG Linghao, ZHANG Ming, JI Wenlu, FANG Lei, QIN Yufei, GE Leijiao. Virtual Acquisition Method for Operation Data of Distributed PV Applying the Mixture of Grey Relational Theory and BP Neural Work[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(1): 125-131.

Figures/Tables 15

Fig.1 Curves of irradiance and PV output power

Fig.2 Structure of BP neural network

Fig.3 Schematic diagram of virtual acquisition model

Fig.4 Schematic diagram of the analysis results of similar days in January, February and March

Fig.5 Schematic diagram of the analysis results of similar days in June, July and August

Fig.6 Schematic diagram of the analysis results of similar days in October, November and December

Fig.7 Irradiance curves of similar days in January, February and march

Fig.8 Irradiance curves of similar days in June, July and August

Fig.9 Irradiance curves of similar days in October, November and December

Fig.10 Training results of BP neural network

Fig.11 Results of virtual collection of photovoltaic data in January, February and March

Fig.12 Results of virtual collection of photovoltaic data in June, July and August

Fig.13 Virtual collection results of photovoltaic data in October, November and December

Table 1 Error index analysiskW

Fig.14 Virtual acquisition of different PV stations

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