Online two-section PV array fault diagnosis with optimized voltage sensor locations

Photovoltaic (PV) stations have been widely built in the world to utilize solar energy directly. In order to reduce the capital and operational costs, early fault diagnosis is playing an increasingly important role by enabling the long effective operation of PV arrays. This paper analyzes the terminal characteristics of faulty PV strings and arrays, and it develops a PV array fault diagnosis technique. The terminal current-voltage curve of a faulty PV array is divided into two sections, i.e., high-voltage and low-voltage fault diagnosis sections. The corresponding working points of healthy string modules and of healthy and faulty modules in an unhealthy string are then analyzed for each section. By probing into different working points, a faulty PV module can be located. The fault information is of critical importance for the maximum power point tracking and the array dynamical reconfiguration. Furthermore, the string current sensors can be eliminated, and the number of voltage sensors can be reduced by optimizing voltage sensor locations. Typical fault scenarios including monostring, multistring, and a partial shadow for a 1.6-kW 3 $times$ 3 PV array are presented and experimentally tested to confirm the effectiveness of the proposed fault diagnosis method.

A multi-organisational approach for disaster preparedness and response:the use of optimisation and GIS for facility location, stock pre-positioning, resource allocation and relief distribution

From 1992 to 2012 4.4 billion people were affected by disasters with almost 2 trillion USD in damages and 1.3 million people killed worldwide. The increasing threat of disasters stresses the need to provide solutions for the challenges faced by disaster managers, such as the logistical deployment of resources required to provide relief to victims. The location of emergency facilities, stock prepositioning, evacuation, inventory management, resource allocation, and relief distribution have been identified to directly impact the relief provided to victims during the disaster. Managing appropriately these factors is critical to reduce suffering. Disaster management commonly attracts several organisations working alongside each other and sharing resources to cope with the emergency. Coordinating these agencies is a complex task but there is little research considering multiple organisations, and none actually optimising the number of actors required to avoid shortages and convergence. The aim of the this research is to develop a system for disaster management based on a combination of optimisation techniques and geographical information systems (GIS) to aid multi-organisational decision-making. An integrated decision system was created comprising a cartographic model implemented in GIS to discard floodable facilities, combined with two models focused on optimising the decisions regarding location of emergency facilities, stock prepositioning, the allocation of resources and relief distribution, along with the number of actors required to perform these activities. Three in-depth case studies in Mexico were studied gathering information from different organisations. The cartographic model proved to reduce the risk to select unsuitable facilities. The preparedness and response models showed the capacity to optimise the decisions and the number of organisations required for logistical activities, pointing towards an excess of actors involved in all cases. The system as a whole demonstrated its capacity to provide integrated support for disaster preparedness and response, along with the existence of room for improvement for Mexican organisations in flood management.