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Offshore wind farm (OWF) is the largest renewable energy resource. The electrical interconnection cost of OWFs is a considerable fraction of the overall design cost of the farm. In order to minimize the investment and operational costs, this paper proposes an optimization formulation to find the optimal electrical interconnection configuration of wind turbines (WTs), and the optimal cable sizing simultaneously. This simultaneous minimization of total trenching length and cable dimensions creates a complex optimization problem that is solved by the harmony search (HS) algorithm. In this paper, two distinct methods of full and partial optimal cable sizing are considered to comprehensively assess the optimal interconnection layout of OWFs. Furthermore, various shipping and burying costs as well as various WTs power ratings are considered in order to investigate their impact on the optimal electrical interconnection system. The optimal electrical interconnection design obtained by the HS algorithm corresponds to a lower cost that together with the technological developments can help policy makers increase the use of offshore wind energy as a feasible unlimited renewable resource in their energy production portfolios.

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Abstract-Development of large-scale Off-shore Wind Farms (OWFs) around the world has created different technical and economic challenges. Optimal configuration of the electrical interconnection of the OWFs is a key factor to minimize the investment and operational costs. This paper proposes an optimization formulation by using a Genetic Algorithm (GA) to find the optimal electrical interconnection configuration for a given OWF topology with different number of turbines. A search algorithm has been incorporated into the objective function that enumerates all feasible power flow directions from all turbines towards the power collecting hub, identifying the power flow path that minimizes the cable dimensions and thus the overall cost. The algorithm has been tested on several OWF topologies. The impact of the location of the hub has also been investigated by way of simulations. Furthermore, a comparison between single-hub and multi-hub solution has been made for multi-OWF system. The results show that multi-hub solution can be a better choice in general, especially when only the interconnection cost is compared. Index Terms-Off-shore Wind Farm (OWF), optimal interconnection configuration, Genetic Algorithm (GA), Transmission line I. INTRODUCTIONOff-shore Wind Farms (OWFs) are nowadays becoming a preferred solution over on-shore wind farms, to reduce the environmental implications in order to meet the high electrical power demand of modern societies [1]. In general, major difference between the cost of OWF and on-shore ones is the foundations cost, the cost of maintenance and electrical interconnection and transmission system to the shore.In the past years, some studies aimed to find optimal interconnection configuration by addressing different power collecting systems for on-shore and off-shore wind farms. Reference [2] compares different power collecting methodologies in order to find optimal electrical configuration in on-shore wind farms by considering total cable length with minimum spanning tree algorithm. A similar study has been done in [3] for OWFs. However, they have only considered minimization of the total cable length, have worked on optimal interconnection configuration of OWFs by using metaheuristic methods without considering cable dimensions in the optimization problem. Furthermore, the impact of the location of the power collecting hub within the farm is not investigated in most literature.Some other studies have only investigated the optimal configuration for the transmission system which brings the OWF power to the mainland, without considering the electrical interconnection of the wind turbines within the farm [7], [8].This paper aims to cover this gap in the literature by optimizing both the interconnecting cable dimensions and the interconnection configuration by using a Genetic Algorithm (GA), as well as illustrating the impact of the location of the hub on the overall cost. Including cable dimension in the fitness function, creates a nonlinear optimization problem (due to existence of a varia...

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Original scientific paper In today's world, many companies use the thermal imaging (infrared), in order to prevent failures and improve the reliability of the electrical networks. In fact, the technical inspection of the electrical equipment using thermal cameras, is the most effective method for preventive defect detection. This contribution deals with, a systematic method in which, areas suspected of failure, are identified through computer-aided thermal image processing. To this end, the candidate areas are determined, using adaptive threshold and, a number of features are extracted from them. Next, using a genetic algorithm (GA), the irrelevant features are omitted. Finally, by means of a hybrid classifier, the pattern of positive and false positive areas, have been identified. This classifier can also be used as a filter, after extracting the candidate areas. This method is tested on images taken from Tehran northwest substations. As a result, applying the feature selection algorithm leads to a faster intelligent fault detection and higher Reliability, especially in widespread networks, which is known as an effective validation for the proposed method.