Advances In Industrial Engineering And Management (AIEM

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019

ANNs in particular, have involved enormous attention due to the variety of advantages they offer over the conventional method. A standout amongst the most energizing and potentially productive ongoing developments is expanding use of Artificial Intelligence techniques. ANNs in particular, have involved enormous attention due to the variety of advantages they offer over the conventional method. Among these advantages the self-adaptive, flexible computational tool, fast speed, and robustness are the most profound. This paper presents an overview of ANNs in power system subjects like load forecasting, transient stability analysis and economic load dispatch.

A continuous and reliable supply of electricity is necessary for the functioning of today's modern and advanced society. Since the early to mid 1980s, most of the effort in power systems analysis has turned away from the methodology of formal mathematical modeling which came from the areas of operations research, control theory and numerical analysis to the less rigorous and less tedious techniques of artificial intelligence (AI). Power systems keep on increasing on the basis of geographical regions, assets additions, and introduction of new technologies in generation, transmission and distribution of electricity. AI techniques have become popular for solving different problems in power systems like control, planning, scheduling, forecast, etc. These techniques can deal with difficult tasks faced by applications in modern large power systems with even more interconnections installed to meet increasing load demand. The application of these techniques has been successful in many areas of power system engineering.

International Journal of Engineering, Science and Technology, 2010

Electric power systems, around the world, are changing in terms of structure, operation, management and ownership due to technical, financial and ideological reasons. Power system keeps on expanding in terms of geographical areas, assets additions, and penetration of new technologies in generation, transmission and distribution. This makes the electric power system complex, heavily stressed and thereby vulnerable to cascade outages. The conventional methods in solving the power system design, planning, operation and control problems have been very extensively used for different applications but these methods suffer from several difficulties due to necessities of derivative existence, providing suboptimal solutions, etc. Computation intelligent (CI) methods can give better solution in several conditions and are being widely applied in the electrical engineering applications. This paper highlights the application of computational intelligence methods in power system problems. Various types of CI methods, which are widely used in power system, are also discussed in the brief.

Electric Power Systems Research, 1992

Problems related to power system operation and control are complex and time consuming because of the non-linearities involved in their formulation and solution. Fast solutions to these problems can be obtained only through parallel processing. Neural nets provide massive parallel processing facilities and may also be used efficiently to model systems with non-linearities. The capabilities of neural nets can, therefore, be well utilized in modelling and processing problems related to power systems. In order to reduce the burden on computers, algorithms involving optimization and complex equations can be converted to heuristics. These heuristics can then be represented in terms of rules and an expert system can be built, with the added advantage of obtaining solutions in a time intensive fashion. This paper studies the application of neural nets to problem solving in power system operation and control, and demonstrates how present methods for solving such problems can be converted to the neural net approach.

Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications , 2020

The purpose of this chapter is to highlight the main technologies of Artificial Intelligence used in power system where the traditional methods will not be able to catch up all condition of operating and dispatching. Moreover, for each technology mentioned in the chapter there is a brief description where is used exactly power system. Moreover, these methods improve the operation and productivity of the power system by controlling voltage, stability, power-flow, and load frequency. It also permits to control the network such as location, size, and control of equipment and devices. The automation of the power system ensures to support the restoration , fault diagnosis, management, and network security. It is necessary to identify the appropriate AI technique to use it in planning, monitoring, and controlling the power system. Finally the chapter will highlight briefly sustainable side of using AI in power system.

International Journal of Energy Optimization and Engineering, 2020

In the guise of artificial neural networks (ANNs), genetic/evolutionary computation algorithms (GAs/ECAs), fuzzy logic (FL) inference systems (FLIS) and their variants as well as combinations, the computational intelligence (CI) paradigm has been applied to nuclear energy (NE) since the late 1980s as a set of efficient and accurate, non-parametric, robust-to-noise as well as to-missing-information, non-invasive on-line tools for monitoring, predicting and overall controlling nuclear (power) plant (N(P)P) operation. Since then, the resulting CI-based implementations have afforded increasingly reliable as well as robust performance, demonstrating their potential as either stand-alone tools, or - whenever more advantageous - combined with each other as well as with traditional signal processing techniques. The present review is focused upon the application of CI methodologies to the - generally acknowledged as - key-issues of N(P)P operation, namely: control, diagnostics and fault dete...

2017

In this chapter, we will underline the importance of the key performance indicators (KPIs) computation for power plants' management. The main scope of the KPIs is to continuously monitor and improve the business and technological processes. Such indicators show the efficiency of a process or a system in relation with norms, targets or plans. They usually provide investors and stakeholders a better image regarding location, equipment technology, layout and design, solar and wind exposure in case of renewable energy sources and maintenance strategies. We will present the most important KPIs such as energy performance index, compensated performance ratio, power performance index, yield, and performance, and we will compare these KPIs in terms of relevance and propose a set of new KPIs relevant for maintenance activities. We will also present a case study of a business intelligence (BI) dashboard developed for renewable power plant operation in order to analyze the KPIs. The BI solution contains a data level for data management, an analytical model with KPI framework and forecasting methods based on artificial neural networks (ANN) for estimating the generated energy from renewable energy sources and an interactive dashboard for advanced analytics and decision support.

International Journal for Research in Applied Science and Engineering Technology, 2019

With increased competitiveness in power generation industries, more resources are directed in optimizing plant operation, including fault detection and diagnosis. One of the most powerful tools in faults detection and diagnosis is artificial intelligence (AI). Faults should be detected early so correct mitigation measures can be taken, whilst false alarms should be eschewed to avoid unnecessary interruption and downtime. For the last few decades there has been major interest towards intelligent condition monitoring system (ICMS) application in power plant especially with AI development particularly in artificial neural network (ANN). ANN is based on quite simple principles, but takes advantage of their mathematical nature, non-linear iteration to demonstrate powerful problem solving ability. With massive possibility and room for improvement in AI, the inspiration for researching them are apparent, and literally, hundreds of papers have been published, discussing the findings of hybrid AI for condition monitoring purposes. In this paper, the studies of ANN and fuzzy logic application will be presented.

Electric Power Systems Research, 1993

Artificial neural networks (ANNs), representing computational paradigms based on a biological metaphor, are rapidly gaining popularity among power system researchers. The number of ANN applications to electric power system problems has increased dramatically in the last two years, fired by both theoretical and application successes in a variety of disciplines. The theory and application developments associated with ANNs are largely interdisciplinary. More than 3500 publications are cited in the literature on ANNs and related applications. Because of limitations on space, this paper has a limited but well-defined objective, namely, to focus attention on significant ANN-related publications involving typical power system problems, and provide epitomizing evaluation and critical comment. More specifically, this paper presents a bibliographical survey of the research and explosive development of many ANN-related applications in electric power systems based on a subset of over 60 published articles. A brief overview of the ANN theory, models and applications is presented. Potential areas of application are identified and future trends are discussed.

The energy sector around the world is facing increasing difficulties due to increased energy requirement, efficiency, changes in demand & supply design & be short of analysis necessary for best possible administration. These difficulties are greatest in growing markets. This makes the electrical network multipart, highly stressed & therefore subject to subsequent failures. The efficiency problems are mainly problematical because the proliferation of informal grid connections means that a large amount of electricity is not metered or billed, leading to losses and increased CO2 emissions. Traditional methods of solving network design, problems of scheduling, processing & managing have been widely designed for various supplications, however these techniques suffer from various complexities because of the need for inferred existence, provision of non-optimal solutions, etc. These difficulties can be overcome by transmitting knowledge of the energy sector on to CI software suppliers. With careful development, artificial intelligence systems can be particularly helpful for automating structured and routine tasks and giving people the ability to tackle the problem of energy challenges in future. Computational Intelligence (CI) can provide a better solution under different circumstances & are broadly used in electric engineering applications. Almost all developed nations are using computational intelligence techniques which enable communication amongst IOT devices, smart meters & smart grid network. These advances can help improve power supply administration, efficiency, clarity, and increment the utilization of sustainable power sources. The utilization of computational intelligence techniques in electricity network wherever it can have serious effects as clean, low-priced & consistent power is essential for improvement. his article focuses the use of computer-assisted intelligence techniques in PS issues. Different types of intelligent techniques which are widely used in the electrical network explained in details.