A review of optimal planning active distribution system:Models, methods, and future researches

Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders

Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while taking system topology into account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus benchmark system. Simulation results show that the GCN model significantly outperforms other widely-used machine learning schemes with very high fault location accuracy. In addition, the proposed approach is robust to measurement noise and data loss errors. Data visualization results of two competing neural networks are presented to explore the mechanism of GCN's superior performance. A data augmentation procedure is proposed to increase the robustness of the model under various levels of noise and data loss errors. Further experiments show that the model can adapt to topology changes of distribution networks and perform well with a limited number of measured buses.Comment: Accepcted by IEEE Journal on Selected Areas in Communicatio

Scenario Planning Analysis for Startup Business Case Study: Kemilau Indonesia Magazine

This research explained about the application of scenario planning analysis as solution for Kemilau Indonesia Magazine (KIM) which was born from the opportunities as information and promotion media of tourism. As a new player, KIM has several internal and external issues that impact on its survival. The technique used in this method is to collect data and information to be analyzed in order to generate predictions as a preparation for decision making process. This method give an overview of early warning, implications and options in each scenario drawn from the analysis of external and internal as recommendation for the company which could be used as a reference in determining and developing company’s business strategy. Keywords: Kemilau Indonesia Magazine, scenario planning, strategic decision making, tourism magazine industryÂ

A comparison of type 1 and type 2 fuzzy logic controller for DC motor system

In today’s modern world application, there are high-level uncertainties that are faced and affecting every kind of operation in various industries. Thus, researchers, today are on the rise to find solutions that will able help to reduce these uncertainties in many types of situations especially control system applications. The type-1 Fuzzy Logic Controller is shown not to be able to handle a high level of uncertainties and the new type-2 Fuzzy Logic Controller is now being said to be able to do a better performance than the type-1 especially in controlling a DC motor system. This can be seen by the simulation graph that clearly observes the comparison of both types. The result where FLC type 2 outperforms FLC type 1 with reduced settling time and rising time can be seen. In conclusion, the new type-2 FLC is now able to overcome the limits of what type-1 FLC are able to do and this will give birth to better and improved performance of new Fuzzy Logic Controllers that is well suited as controllers for DC motor system. This paper will briefly discuss the comparison between both of these types of FLC and the benefits

Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms

Abstract Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.</jats:p

A Chebyshev interval method for nonlinear dynamic systems under uncertainty

This paper proposes a new interval analysis method for the dynamic response of nonlinear systems with uncertain-but-bounded parameters using Chebyshev polynomial series. Interval model can be used to describe nonlinear dynamic systems under uncertainty with low-order Taylor series expansions. However, the Taylor series-based interval method can only suit problems with small uncertain levels. To account for larger uncertain levels, this study introduces Chebyshev series expansions into interval model to develop a new uncertain method for dynamic nonlinear systems. In contrast to the Taylor series, the Chebyshev series can offer a higher numerical accuracy in the approximation of solutions. The Chebyshev inclusion function is developed to control the overestimation in interval computations, based on the truncated Chevbyshev series expansion. The Mehler integral is used to calculate the coefficients of Chebyshev polynomials. With the proposed Chebyshev approximation, the set of ordinary differential equations (ODEs) with interval parameters can be transformed to a new set of ODEs with deterministic parameters, to which many numerical solvers for ODEs can be directly applied. Two numerical examples are applied to demonstrate the effectiveness of the proposed method, in particular its ability to effectively control the overestimation as a non-intrusive method. © 2012 Elsevier Inc

A digital twin architecture for effective product lifecycle cost estimation

Lifecycle cost estimation is crucial for high-value manufacturing sectors, in particular at the early product design stage, to maintain their product affordability and manufacturing profitability within the market. Accordingly, it is important to identify through-life cost reduction opportunities. However, this is a challenging task for designers at the early product lifecycle stage due to the lack of complete historical data and the existence of high-level uncertainties within the product and service cost data. Moreover, the complexity of maintenance, repair, and overhaul interventions during the operation stage reduces the designers’ decision-making confidence level at the earlier stages. This paper aims to address these challenges by proposing a novel Digital Twin (DT) architecture that uses adaptive data structure and ontologies to automatically produce the cost model from data mined information throughout a product lifecycle. The DT architecture supports designers by capturing data in terms of consumed and caused cost and automates the data flow to provide an adaptive cost estimation method across the product lifecycle. The DT enables designers to estimate the lifecycle cost at the early stage and to identify the through-life cost reduction opportunities effectively. Thereby, it is expected that the proposed DT supports OEMs to reduce the total lifecycle cost and improve the efficiency of their product development. A case study of lifecycle cost estimation in the machine tool industry is considered for testing the validity of the DT architecture