Shading Fault Detection Method for Household Photovoltaic Power Stations Based on Inherent Characteristics of Monthly String Current Data Mapping

The poor outdoor operating conditions of household photovoltaic(PV)make the power station prone to various faults.However,the dispersion of household PV installations often increases the difficulty and cost of operation and maintenance(O&M).Although the remote monitoring and fault detection of a PV power station can be realized by the use of operation data,the particularity of a household power station also brings many problems to fault detection.In this study,we propose a shading fault detection method of household PV power based on inherent characteristics of monthly string current data mapping.The ideal current peak obtained by a new fitting method is used to normalize string current data.The current probability density function(PDF)at each time point is estimated by kernel density estimation(KDE).Through the normalized current data corresponding to the maximum probability density,the inherent characteristics of the strings are obtained,such that whether the strings have shading can be judged and the shading degree can then be evaluated.Not only are no additional sensors needed to collect environmental data,such as irradiation and temperature,but also simulating the detailed parameters of the power station is not required.The interference caused by meteorological factors can thus be eliminated,which can be easily used in old power stations and newly constructed power stations.The effectiveness and performance of the proposed shading fault detection method is verified by experimental data collected from the actual household PV power station.Index Terms-Data fitting,fault detection,household photovoltaic(PV),kernel density estimation(KDE),shading degree.

Optimal regulation of flexible loads in rural residential buildings considering mobile batteries:A case study in Shaanxi Province

While the grid-connected capacity of rural household photovoltaics is increasing rapidly,achieving dynamic supply-demand matching despite fluctuations in solar energy is challenging.With the rapid development of rural electrification,battery-powered technologies,such as electric vehicles and electric agricultural machinery,are becoming increasingly popular in rural areas.In this context,utilizing idle mobile batteries to assist in energy storage for rural residential buildings offers a new way to solve the problem of dynamic supply-demand matching.In this study,a field survey was conducted on several typical fruit-growing villages in the Central Shaanxi Plain in Shaanxi Province of China.Typical rural households were selected to calculate the electricity loads of the residential buildings,with due consideration to the intervention of mobile batteries.Under the premise of installing 3 kW household photovoltaic systems in rural households,an economical efficiency-oriented model was built for the optimal regulation of flexible loads.The results were compared in the context of two patterns of electricity consumption,i.e.,unidirectional charging of mobile batteries from buildings and bidirectional charging and discharging between mobile batteries and buildings.The bidirectional pattern significantly increased the photovoltaic consumption of typical rural households on various typical days.Specifically,during both scenarios of not implementing time-of-use and implementing time-of-use,the typical day of the winter slack farming season exhibited the best photovoltaic consumption effect among all types of typical days.Additionally,the bidirectional pattern also result in a significant increase in the annual electricity sales revenues for typical rural households.