Failure Mode and Effect Analysis (FMEA) has already been used as a qualitative measure for identifying failure modes and causes, in order to mitigate the effects of failure in different sectors of power systems. This paper presents a quantitative approach called Risk-Based-FMEA, based on the failure probabilities and incurred failure costs instead of rating scales. As a case study, this approach has been applied to a direct drive wind turbine. The results show that the definition of failure modes priorities based on their contribution to the total failure cost of the wind turbine is more realistic and practical than the common FMEA approach. Using MS Excel spreadsheet platform, the proposed method can be generalized for different types of wind turbines. In addition, the effective failure cost factors are investigated through sensitivity analysis, by which the wind turbine owner can determine the suitable approach to reduce the total failure cost.

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Smart homes, as active participants in a smart grid, may no longer be modeled by passive load curves; because their interactive communication and bidirectional power flow within the smart grid affects demand, generation, and electricity rates. To consider such dynamic environmental properties, we use a multiagent-system-based approach in which individual homes are autonomous agents making rational decisions to buy, sell, or store electricity based on their present and expected future amount of load, generation, and storage, accounting for the benefits each decision can offer. In the proposed scheme, home agents prioritize their decisions based on the expected utilities they provide. Smart homes' intention to minimize their electricity bills is in line with the grid's aim to flatten the total demand curve. With a set of case studies and sensitivity analyses, we show how the overall performance of the home agents converges-as an emergent behavior-to an equilibrium benefiting both the entities in different operational conditions and determines the situations in which conventional homes would benefit from purchasing their own local generation-storage systems.

Distributed generation (DG) is the future of energy. This technology allows the bidirectional flow of power within an electrical network. Researchers are faced with many challenges to the accurate implementation of protection schemes for DG-connected distribution network. The schemes designed must satisfy the performance requirements of selectivity, reliability, and sensitivity. Most researchers opine that conventional protection schemes based on over current detection are insufficient to completely and accurately protect a DG-connected distributed power system. There are many challenges that need to be tackled before embarking upon the journey to successfully implement these schemes. This paper summarizes the major challenges which one can encounter while designing protection schemes for DG-connected distribution networks. Some possible solutions from the literature are also mentioned. Moreover, a suggested solution for protecting future active distribution networks is provided. It is expected that this paper will act as a benchmark for future researchers in this field to tackle the challenges related to the protection of active distribution networks.

Increasing capacity of connected wind power generation to utilities brings new opportunities and also problems to the utilities and customers. Evolution and analyzing of the connection conditions and effects of wind farms especially on remote areas are the main aspects of developing wind power on the utilities. The problem is that these wind turbine that mostly uses induction generators, tend to drain large amounts of VARs from the grid, potentially causing low voltage and maybe voltage stability problems for the utility owner, especially in the case of large load variation on distribution feeder. To investigate the impact of large wind farms on power system voltage stability, one of the Iran's wind farms is modeled in DIgSILENT software and the simulation results shows the effect of using FACTS devices including SVC and STATCOM on power system performance.