Evaluating the Novel Application of a Class of Sampled Regulators for Power System Control

With the rapid development of society, the progress of science and technology, computer technology through continuous improvement and perfect has been widely used in various fields. In power system, the main function of the optimal control theory is in the midst of all the solutions to find a more scientific and reasonable solution. The optimal control theory in modern control theory occupies a very important role[1]. In the control system, is the most important part of computer, computer is mainly to complete the online control, the optimal control theory to thoroughly applied to the practical work, improve the work efficiency of the power system to ensure the reliability and security of power system With the rapid development of science and technology, power system automation direction gradually, the safe operation of power system automation is decided by the control theory [2]. With the rapid development of the optimal control theory and its application in electric power system more and more deep, the existence of this theory is mainly in all solutions in the search for a suitable method. The optimal control theory is in the last century 60 s come up an idea, after half a century of development, its application in power system is perfect, the application result in power system is also very obvious.

In the past few years, distributed cooperative control of multiagent systems has received much attention from various scientific fields due to its wide engineering applications. A multiagent system typically contains numerous nodes and lots of links among these individual nodes. It is thus difficult or practically impossible to design a centralized controller to control all the nodes. Within this context, control of large, multiagent systems is achieved by designing some distributed controllers where only some local information is involved. One changeling issue in solving the cooperative control problem of multiagent systems is that neighboring agents may communicate with each other in a constrained communication environment. Distributed cooperative control of multiagent systems has definite meaning in analyzing and designing modern power systems. It has been known that recent trends in the modernization of power systems require communication networks that support the inclusion of new devices, for example, smart meters, and intelligent electronic devices, to reduce operation and maintenance costs, and integrate distributed renewable energy sources. In this case, the centralized analysis and control techniques for power systems are inapplicable. The main aims of this special issue are to present analysis methods for cooperative control of multiagent systems and discuss their potential applications in modern power systems. Call for papers has been carefully prepared by the guest editors and posted on the journal's web page, which has received much attention from researchers in different scientific communities. We have received 15 papers in related research fields. All manuscripts submitted to this special issue went through a thorough peer-refereeing process. Based on the anonymous reviewers' reports, 7 original research articles are finally accepted. We hope that the papers published in this special issue will be useful to researchers in the fields of distributed cooperative control and power systems. We also hope that the published papers will arouse further research in the topics presented as well as in the other related topics.

Efficient operation and planning of power systems is important for a reliable and sustainable electricity supply. Therefore, optimization techniques have been applied to several optimization problems in power systems in order to achieve technical and economic efficiency. This paper presents an overview of existing optimization techniques and applications in power systems, with a special focus on multi-objective optimization in power system planning. Power system planning is by its nature a very complex multi-objective optimization problem involving perspectives of different stakeholders. Besides, a single stakeholder can also have various objectives that need to be optimized at the same time. This paper provides a review of the state-of-the-art in multi-objective evolutionary algorithms applied to power systems planning problems.

Ensuring pipeline stability is a fundamental aspect of subsea pipeline design and can contribute a significant proportion of project costs in regions with large diameter trunklines, shallow water and severe geotechnical and metocean conditions [1]. Reducing the conservatism and simplifications of existing pipeline stabilisation design methods therefore offers economic benefits to hydrocarbon producers necessary to ensure the ongoing viability of projects in these regions. To realise this potential and reduce the conservatism of the existing design methods, a more accurate understanding of the hydrodynamic loads exerted by waves and currents is required. This paper investigates one of the inherent assumptions incorporated into the existing design methods through the arrangement of previous experimental investigations to determine whether rectilinear motion provides a reasonable approximation to simulate the near seabed orbital particle paths in wind-generated waves. This assumption is based on the flattening of particle paths to ellipsoids with depth and ignores the small vertical velocity components near the seabed. Based on the hydrodynamic forces calculated numerically using a validated Computational Fluid Dynamics (CFD) model for rectilinear and orbital wave modelling it is concluded that pipeline stabilisation requirements calculated in accordance with the DNV-RP-F109 absolute lateral static stability design method and rectilinear wave motion assumption are conservative. It is also concluded that the hydrodynamic force asymmetry in favour of the reverse half wave cycle caused by the vertical velocity components in orbital wave conditions requires further consideration to determine the implication for dynamic lateral stability design methods.

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks are typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high-dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNN structures, e. g., graph convolutional networks, are summarized. Key applications in power systems such as fault scenario application, time-series prediction, power flow calculation, and data generation are reviewed in detail. Further-more, main issues and some research trends about the applications of GNNs in power systems are discussed.

Game theory-based approaches have recently gained traction in a wide range of applications, importantly in power and energy systems. With the onset of cooperation as a new perspective for solving power system problems, as well as the nature of power system problems, it is now necessary to seek appropriate game theory-based tools that permit the investigation and analysis of the behavior and relationships of various players in power system problems. In this context, this paper performs a literature review on coalitional game theory's most recent advancements and applications in power and energy systems. First, we provide a brief overview of the coalitional game theory's fundamental ideas, current theoretical advancements, and various solution concepts. Second, We examine the recent applications in power and energy systems. Finally, we explore the challenges, limitations, and future research possibilities With applications in power and energy systems in the hopes of furthering the literature by strengthening the applications of coalitional game theory in power and energy systems.

On parameter-dependent Lyapunov functions for robust fault detection filter design with application in power systems

In recent decades, because of the speedy progress of the smart grid and the deepening of the reforms of the energy system, demand-side users can contribute to the collaboration of the energy network, with the main right to public energy procurement and the main right to sell energy. The demand side of the smart grid and the open energy market provides users with more adoptions, and game theory is expected to become an important tool for optimizing multi-stakeholder decision-making and solving many problems in this area. In this regard, this review article first reviews the recent development of game theory application in modern power systems, in addition to discussing in detail the typical gaming behavior of the current energy demand side. Second, it focuses on the application of game theory mainly in three aspects: distributed energy users, high-energy energy users, and medium- and low-energy users. Game theory is used in optimizing the distributed energy coordination, optimizing the energy purchasing strategy, responding to the needs of commercial and residential users, and ensuring network communication. Later, a comprehensive analysis of recent trends in game theory application in power systems is carried out in detail.

For decades, developments have been taking place, separately, in the areas of power systems, digital signal processing and automatic control. Despite of some isolated cases, where the “trajectories” touched, there never was a time when these areas more benefited from each other than in the last few years. Traditionally, power systems problems and applications have been solved by means of purely analog circuits, while an enormous number of digital signal processing and control algorithms have been developed by people working in the communications and control areas. The ever increasing improvement of the semiconductor industry on one hand, and the rising of power electronics applications on the other, have changed this scenario forever, paving the way to the very fruitful area of digital control and signal processing applied on power systems and power electronics. This new area has been applying successfully all the knowledge gathered to improve processes, like power quality monitoring, power system’s protection, power conditioning and synchronization of distributed generators (among others), and the most used digital techniques have been digital filtering, discrete Fourier transform, phased-locked loop tracking methods and more recently, the Kalman filtering (Kalman, 1960). The Kalman filter (KF) was originally proposed to solve a control theoretic problem: considering a linear time-invariant (LTI) system, including state disturbances and measurement errors, how to obtain the best process’ LTI state-estimator (in a stochastic sense, that is, minimizing the covariance of the estimation error), in order to be used in a state feedback control law? In conjunction with optimal linear quadratic regulator, the KF found its first application in the well known LQG control (Linear Quadratic Gaussian). After, it became very popular in other areas, according to (Papoulis, 1991), when people became aware of its desirable properties as an estimator. Then several different applications progressively emerged in economics, image processing and biomedical instrumentation, to name a few. More recently, the KF found applications as part of more complex systems, as in an adaptive control system – see for example (Sastry & Bodson, 1989), where it is shown that the RLS (recursive least mean square) algorithm is a particular case of the KF – and attempts to find a nonlinear KF have been taking place, as in (Wong & Yau, 1999) and (Colon & Pait, 2004). O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

Electric power system is one of the most important infra-structure of modern digital society. This energy, which is easy to control, to be converted any type of energy, and clean, is becoming the standard how the society is developed well and the demand of electricity is increasing rapidly over the world. However, in most highly developed electrical power system, there are several difficulties related from generation to distribution. Usually, power generation is located remote area from the load center, long transmission and distribution lines have to be constructed and maintained to meet required reliability, power quality and economic point of views. Reliable, cheap, efficient conductor is required to support desirable electric power systems. Most of conductors used in modern power system facilities, for example, generator, transformer, transmission line, cable, motor etc., are copper or aluminum. They have resistance R which restricts the capability of thermal rating of electric facilities with the ohmic loss. If there is a conductor with no loss, we can make efficient electrical facilities. Superconductor, which is zero resistance, is a promising solution to make innovation on electric facilities. This chapter introduces various power system facilities based on superconductor application. First of all, superconducting cable is most applicable solution to solve transmission congestion problem in high power density area such as metropolitan cities with its high density transmission capability. Recently developed superconducting cable in distribution class can deliver about 5 times more power than conventional XLPE cable at same dimension. DC superconducting cable is also in developing stage to eliminate AC loss in superconductor, and will be applied to HVDC transmission system. Section 2 introduces superconducting cable in power system. Second promising one is Superconducting Fault Current Limiter (SFCL).With the development of power system, short circuit fault currents are increasing much more than conventional power system which is the components of present system. For example, a lot of circuit breakers have to be replaced higher level break capacity in case of source impedance is reduced by increased power system generation and/or reinforced transmission and distribution system. SFCL can limit fault current fast, within 1/2 cycle, using quench effect of superconductor in case of current exceeds specified fault current. Also, it can supply a solution on power system voltage sag problem. Section 3 introduces various type of SFCL and their application. Other promising applications in power system are Superconducting Synchronous Condenser (DSC : SuperVar) and Superconducting motor. SuperVar is a good solution as

Various decision making methods have been proposed in the past for diverse applications. However, there is still a need for improvement: existing methods are either time consuming or difficult to expand when needed. This paper presents a novel decision making method based on binary feature matrix. This new method has the advantages of easier expansion of the rule set with additional features and being faster. Potential applications in electric power system are discussed.

Effectiveness of learning algorithms with attack and defense mechanisms for power systems

Nowadays, the increasing energy demand, development of smart grids, and the combination of different energy systems have led to the complexity of power systems. On the other hand, ever-expanding energy consumption, development of industry and technology systems, and high penetration of solar and wind energies have made electricity networks operate in more complex and uncertain conditions. Therefore, analysis of traditional power and energy systems requires physical modeling and extensive numerical computation. To analyze these systems’ behavior, advanced metering and condition monitoring devices and systems are utilized, which generate huge amounts of data. Assessment of these data is approximately impossible by conventional methods and requires powerful data mining procedures. Machine learning, deep learning, and a variety of regression, classification, and clustering algorithms are powerful tools to use in these issues. These procedures can be utilized for load/demand forecasting, demand response evaluation, defect/fault detection in electrical equipment, power system analysis and control, cybersecurity, and renewable energy generation prediction. Understanding the structure and functioning of each learning method is therefore one of the most important issues in the application of them to solve power system problems. In this chapter, we will introduce and discuss selected methods of data mining based on their learning, structure, formulation, mode of operation, and application in power systems. Literature on machine learning and deep learning procedures, train and test process of networked methods, and, finally, applications of each procedure are presented in this chapter.

IEC 62439–3 is a standard that specifies bumpless redundancy especially in the case of multiple communication failures. Whilst designed specifically for power system applications connected in ring topologies the specification bears an import beyond that of its initial topology domain that of ring based communication structures. As an example, the smart grid initiative taking place in the power system community will lead power system and other applications share common communication medium and devices. This means a scope of operation that covers the aspect of network load and the efficient coupling of rings. In this paper, we present the main concepts of both profiles and discuss their application in an open environment.

Presently power system operation produces huge volumes of data that is still treated in a very limited way. Knowledge discovery and machine learning can make use of these data resulting in relevant knowledge with very positive impact. In the context of competitive electricity markets these data is of even higher value making clear the trend to make data mining techniques application in power systems more relevant. This paper presents two cases based on real data, showing the importance of the use of data mining for supporting demand response and for supporting player strategic behavior.