The studies on the photovoltaic (PV) generation are extensively increasing, since it is considered as an essentially inexhaustible and broadly available energy resource. However, the output power induced in the photovoltaic modules depends on solar radiation and temperature of the solar cells. Therefore, to maximize the efficiency of the renewable energy system, it is necessary to track the maximum power point of the PV array. In this paper, a maximum power point tracker using fuzzy set theory is presented to improve energy conversion efficiency. A new method is proposed, by using a fuzzy cognitive network, which is in close cooperation with the presented fuzzy controller. The new method gives a very good maximum power operation of any PV array under different conditions such as changing insolation and temperature. The simulation studies show the effectiveness of the proposed algorithm

State-of-the-art Predictive Maintenance (PM) of Electrical Machines (EMs) focuses on employing Artificial Intelligence (AI) methods with well-established measurement and processing techniques while exploring new combinations, to further establish itself a profitable venture in industry. The latest trend in industrial manufacturing and monitoring is the Digital Twin (DT) which is just now being defined and explored, showing promising results in facilitating the realization of the Industry 4.0 concept. While PM efforts closely resemble suggested DT methodologies and would greatly benefit from improved data handling and availability, a lack of combination regarding the two concepts is detected in literature. In addition, the next-generation-Digital-Twin (nexDT) definition is yet ambiguous. Existing DT reviews discuss broader definitions and include citations often irrelevant to PM. This work aims to redefine the nexDT concept by reviewing latest descriptions in broader literature while establishing a specialized denotation for EM manufacturing, PM, and control, encapsulating most of the relevant work in the process, and providing a new definition specifically catered to PM, serving as a foundation for future endeavors. A brief review of both DT research and PM state-of-the-art spanning the last five years is presented, followed by the conjunction of core concepts into a definitive description. Finally, surmised benefits and future work prospects are reported, especially focused on enabling PM state-of-the-art in AI techniques.

Electric vehicles (EVs) have become widespread during the last decade because of the distinct advantages they offer compared to the conventional ones. However, the increased penetration of EVs in the global transportation market has led increased electricity demands, which is expected to affect the operation of energy distribution systems. In the present paper, a demonstration about the effects of uncontrolled EVs charging in a case study low voltage (LV) network is demonstrated and a fuzzy energy management strategy for the coordination of EV charging in LV networks is presented, by including the distance of the EVs from the transformers in the fuzzy management systems for the first time. The Institute of Electrical and Electronics Engineers (IEEE) European Test Feeder is used as a case study low voltage distribution grid. In particular, the developed system configuration takes into consideration the architecture of the grid, the ampacities of the lines and the voltages at the system’s buses. Moreover, electric vehicles are considered as agent-based models, which are characterized by the model of each EV, the state-of-charge of their batteries and the charging power. In particular, an investigation into the effects of uncontrolled charging is performed, in which two approaches are examined. The first approach investigates the maximum number of chargeable EVs in the case study network and how it is influenced by the grid’s household loads. The second approach examines the number of network undervoltages and lines ampacity violations in a set of simulation scenarios. The results of the first approach show that the distance of the EVs from the networks substation affects the maximum number of chargeable EVs in a significant manner. Based on the observed results of the two approaches, a fuzzy management system is designed for the coordination of EV changing, which takes into account the distance from the EV charging points to the feeder substation, the state-of-charge of the EVs’ batteries and the EVs’ charging delay time.

A critical factor in industrial production maintenance is decision‐making in fault diagnosis. Motor current signature analysis (MCSA) is an established condition‐based maintenance method to make fault diagnosis in induction motors (IMs). The occurrence of multiple interacting faults, as well as emergent behaviour, can be tackled efficiently especially when MCSA is combined with distributed problem‐solving artificial intelligence (AI) techniques. Therefore, in this study, an intelligent multi‐agent system (MAS) is presented to make decisions on the fault conditioning of a three‐phase squirrel cage IM. The incorporated agents represent different health conditions of the same IM, with faults that may occur in the rotor bars. All agents utilise an AI method, and use for training MCSA experimental data. A supervisor agent (SA) initially communicates with agents employing feed‐forward artificial neural networks trained with the back‐propagation algorithm and performs the final fault diagnosis by evaluating their responses. When a decision cannot be made on the fault type, the SA employs another agent that uses the k‐nearest neighbour rule. The proposed method achieves high fault diagnosis accuracy. Its performance is also compared with results previously obtained from the same motor when an adaptive neuro‐fuzzy inference system combined with a subtractive clustering method had been used.