A Novel Architecture for Data Management and Control in Autonomous Intelligent Microgrid

AbstractIntelligent microgrid with distributed energy sources is considered as the next generation grid to mitigate the present day power system issues. Intelligent microgrid should facilitate monitoring and distributed control of the system using smart components. For effective, reliable and intelligent operation of such a system, it needs to use the advanced communication and intelligent information processing techniques. This paper explores the possibility of managing an autonomous intelligent microgrid with prioritized loads, utilizing the existing communication networks to acquire data and manage from a central location. The central control center runs an energy management algorithm, utilizing the load and source data acquired from the clients, to maximize the power delivery to the higher priority loads. The proposed scheme enables the consumers to dynamically set their load priority and fix the rate for selling the power generated by them within the autonomous grid, thereby ensuring consumer participation in the development of power infrastructure. Further, a load management algorithm for the reliable operation of autonomous intelligent microgrid with prioritized loads is proposed and its effectiveness is illustrated with a case study

A Review on Expert System Applications in Power Plants

The control and monitoring of power generation plants is being complicated day by day, with the increase size and capacity of equipments involved in power generation process. This calls for the presence of experienced and well trained operators for decision making and management of various plant related activities. Scarcity of well trained and experienced plant operators is one of the major problems faced by modern power industry. Application of artificial intelligence techniques, especially expert systems whose main characteristics is to simulate expert plant operator’s actions is one of the actively researched areas in the field of plant automation. This paper presents an overview of various expert system applications in power generation plants. It points out technological advancement of expert system technology and its integration with various types of modern techniques such as fuzzy, neural network, machine vision and data acquisition systems. Expert system can significantly reduce the work load on plant operators and experts, and act as an expert for plant fault diagnosis and maintenance. Various other applications include data processing, alarm reduction, schedule optimisation, operator training and evaluation. The review point out that integration of modern techniques such as neural network, fuzzy, machine vision, data base, simulators etc. with conventional rule based methodologies have added greater dimensions to problem solving capabilities of an expert system.DOI:http://dx.doi.org/10.11591/ijece.v4i1.502

Torque capability improvement of sensorless FOC induction machine in field weakening for propulsion purposes

An electric propulsion system is generally based on torque controlled electric drive and DC series motors are traditionally used for propulsion system. Induction machines, which are reliable, low cost and have less maintenance, satisfy the characteristics of the propulsion and reinstating the DC series motor. Field oriented control (FOC) of induction machines can decouple its torque control from field control which allows the induction motor to act like a separately excited DC motor. In this paper, the characteristic control of induction motor is achieved through appropriate design modification of induction motor by varying magnetizing current to produce maximum torque in field weakening (FW) region. Thus to improve the torque capability of induction machine in FW region by varying machine parameters. The sensorless operation of the induction motor is carried out by adopting model reference adaptive system (MRAS) using sliding mode control (SMC) and a FW algorithm based on the voltage and current constraints. The simulation of the induction motor drive models based on the design options have been carried out and analyzed the simulation results

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Transforming distribution system into a sustainable isolatedmicrogrid considering contingency

Currently, the distribution system has been adapted to include a variety of Distributed Energy Resources (DERs). Maximum benefits can be extracted from the distribution system with high penetration of DERs by transforming it into a sustainable, isolated microgrid. The key aspects to be addressed for this transformation are the determination of the slack bus and assurance of reliable supply to the prioritized loads even during contingency. This paper explores the possibilities of transforming the existing distribution system into a sustainable isolated net-work by determining the slack bus and the optimal locations and capacity of Distributed Generators (DGs) in the isolated network, taking into account the contingencies due to faults in the network. A combined sensitivity index is formulated to determine the most sensitive buses for DG placement. Further, the reliability based on the loss of load in the isolated system when a fault occurs is evaluated, and the modifications required in for reliability improvement are discussed. The supremacy of the transformed isolated network with distributed generators is comprehended by comparing the results from conventional IEEE 33-bus grid connected test system and modified IEEE 33-bus isolated test system having no interconnection with the main grid