The optimization algorithm is designed using mixed-integer programming (MIP). Moreover, the possibility of installing different cable options, with different prices and capacities, is included. optimal location and number of tie switches. cable routing and optimal location and number of switching devices (circuit breakers and reclosers) 2. This approach merges different problems together and solves them in a two-stage process, as follows: 1. The prime focus of this work is to assist utilities by developing a new integrated approach which considers the impacts of system reliability in distribution system planning (DSP). Based on this investigation, the most suitable and optimal machine learning technique can be identified and used for future work.ĭistribution utilities aim to operate and plan their network in a secure and economical way. Typically, asset management plays an essential role in determining the quality and profitability of the elements in the power network. The proper functioning, maintenance and controlling operations of the electric components are key challenging and demanding tasks in the power distribution systems. Then, the challenges and managing operations of the asset management strategies are discussed based on the technical and economic factors. In addition to this, a comparative analysis between the ML models is performed, identifying the advantages and disadvantages of these techniques. Moreover, the importance of using ML models for proper decision making based on the asset management plan is illustrated in a detailed manner. The articles are categorized according to their purpose: 1) classification, 2) machine learning, and 3) artificial intelligence mechanisms. In this review article, Machine Learning (ML) models are analyzed to improve the lifespan of the electrical components based on the maintenance management and assessment planning policies. Also, it investigates the challenges behind asset management and its maintenance activities. This study aims to study the different kinds of Machine Learning (ML) models and their working principles for asset management in power networks. The results also show that the supporting potential of DGs and batteries in preventive maintenance scheduling allows a significant reduction of load losses. We show the effectiveness and efficiency of the proposed approach via a case study based on a modified IEEE-34 bus distribution network, where we also compare a branch-and-bound and a particle swarm optimization solver. Then a stochastic scenario-based mixed-integer non-linear programming problem is formulated to determine the short-term maintenance schedule. Moreover, a method is proposed to capture the influence of maintenance decisions in the model of the served load from DGs and batteries via generation of topological constraints. To deal with the large-scale complexity of the network, a depth-first-search clustering method is used to divide the network into zones. In this paper, a novel short-term preventive maintenance method is proposed that explicitly considers the support potential of DGs and batteries as well as uncertainties in the power generated by the DGs. Distributed generators (DGs) and batteries can be used to support power to nearby loads when they are isolated due to maintenance. Preventive maintenance is applied in distribution networks to prevent failures by performing maintenance actions on components that are at risk. The utilization of information technologies (IT) plays a central role in the asset management of a DUE in all timescales Finally, strategic planning is discussed, where distributed generation (DG) is evaluated in the context of long-term system planning strategy. The issues regarding the maintenance of assets including the optimal maintenance strategy and optimal outage plan are presented under the title of mid-term asset management. Secure and reliable operation of the network, system monitoring and control, fault restoration are among the issues discussed in the paper. Short-term asset management is related with operational issues. This study categorizes the asset management strategies of a (DUE) based on short-term, mid-term and long-term timescales and shows that their coordination plays a critical role in making strategic decisions. The principal objective of the asset management in a power distribution utility enterprise (DUE) is to guide the acquisition, use and disposal of distribution system assets to provide the level of service required by customers in the cost-effective manner, encompassing the strategic planning, maintenance and utilization and operation of a physical resource throughout its life.
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