Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement

Distribution network reconfiguration (DNR) has proved to be an economical and effective way to improve the reliability of distribution systems. As optimal network configuration depends on system operating states (e.g., loads at each node), existing analytical and population-based approaches need to repeat the entire analysis and computation to find the optimal network configuration with a change in system operating states. Contrary to this, if properly trained, deep reinforcement learning (DRL)-based DNR can determine optimal or near-optimal configuration quickly even with changes in system states. In this paper, a Deep Q Learning-based framework is proposed for the optimal DNR to improve reliability of the system. An optimization problem is formulated with an objective function that minimizes the average curtailed power. Constraints of the optimization problem are radial topology constraint and all nodes traversing constraint. The distribution network is modeled as a graph and the optimal network configuration is determined by searching for an optimal spanning tree. The optimal spanning tree is the spanning tree with the minimum value of the average curtailed power. The effectiveness of the proposed framework is demonstrated through several case studies on 33-node and 69-node distribution test systems

ANALYZING AIRCRAFT LANDING DECISIONMAKING THROUGH FUZZY LOGIC APPROACH: A COMPARATIVE STUDY

Due to the importance of weather in people's lives, various groups have advocated for accurate climate information. However, weather predictions can often be unclear or ambiguous. Weather advice and information are crucial in determining the safety of landing an aircraft in aviation. To address this, Mamdani Fuzzy Logic will be used to compare two scenarios: one with three inputs (wind direction, wind speed, and visibility) and another that includes the pilot's experience to assess its impact on the landing process. A fuzzy logic-based intelligent system generates three decisions: feasible, careful, and not feasible for landing an aircraft on a runway. The difference rate between the two experiments was 68%, indicating that the pilot's experience played a significant role and forced its importance in the results

An Efficient Cluster-based Routing Protocol in Cognitive Radio Net-work

Cognitive Radio Networks (CRNs) are being studied intensively and gaining importance as spectrum is the heavily underutilized. CRN has the capability to exploit smartly the unutilized frequency spectrum. Recently, the research community started to work in the area of cognitive radio routing. In a flat topology, all nodes are of the same level and functionality, thus making it simple and efficient for smaller networks. However, when the network is large with sparse nodes, the routing information becomes more complex making cluster-based techniques really relevant to tackle such situations. In a cluster-based routing, all nodes in the network are dynamically organized into partitions called groups or clusters. In each cluster, a cluster head is chosen to help in the data transmission management and to maintain cluster membership information. This paper proposes a novel routing protocol for cognitive radio ad hoc networks (CRAHNs) based on clustering model which amends swiftly to the topological changes and establishes the routing efficiently. Our proposed approach is thoroughly evaluated through simulation study. The results state the suitability of the proposed protocol for cognitive radio ad hoc networks and demonstrate that it has better performance in terms of finding the source-destination route, reducing the amount of messages that are transmitted all over the network and minimizing the routing delay.Comment: International Conference on Advanced Communication Systems and Signal Processing (ICOSIP 2015), Nov 2015, TLEMCEN, Algeria. 201

Reserve Allocation in Active Distribution Systems for Tertiary Frequency Regulation: A Coalitional Game Theory-based Approach

This paper proposes a coalitional game theory-based approach for reserve optimization to enable DERs participate in tertiary frequency regulation. A two-stage approach is proposed to effectively and precisely allocate spinning reserve requirements from each DER in distribution systems. In the first stage, two types of characteristic functions: worthiness index (WI) and power loss reduction (PLR) of each coalition are computed. In the second stage, the equivalent Shapley values are computed based on the characteristic functions, which are used to determine distribution factors for reserve allocation among DERs