A Fault-Tolerant P-Q Decoupled Control Scheme for Static Synchronous Series Compensator

Control of nonlinear devices in power systems relies on the availability and the quality of sensor measurements. Measurements can be corrupted or interrupted due to sensor failure, broken or bad connections, bad communication, or malfunction of some hardware or software (referred to as missing sensor measurements in this paper). This paper proposes a fault-tolerant control scheme (FTCS) for a static synchronous series compensator (SSSC). This FTCS consists of a sensor evaluation and (missing sensor) restoration scheme (SERS) cascaded with a P-Q decoupled control scheme (PQDC). It is able to provide effective control to the SSSC when single or multiple crucial sensor measurements are unavailable. Simulation studies are carried out to examine the validity of the proposed FTCS. During the simulations, single and multiple phase current sensors are assumed to be missing, respectively. Results show that the SERS restores the missing data correctly during steady and transient states, including small and large disturbances, and unbalanced three-phase operation. Thus, the FTCS continuously provides effective control to the SSSC with and without missing sensor measurements

Fault-Tolerant Optimal Neurocontrol for a Static Synchronous Series Compensator Connected to a Power Network

This paper proposes a novel fault-tolerant optimal neurocontrol scheme (FTONC) for a static synchronous series compensator (SSSC) connected to a multimachine benchmark power system. The dual heuristic programming technique and radial basis function neural networks are used to design a nonlinear optimal neurocontroller (NONC) for the external control of the SSSC. Compared to the conventional external linear controller, the NONC improves the damping performance of the SSSC. The internal control of the SSSC is achieved by a conventional linear controller. A sensor evaluation and (missing sensor) restoration scheme (SERS) is designed by using the autoassociative neural networks and particle swarm optimization. This SERS provides a set of fault-tolerant measurements to the SSSC controllers, and therefore, guarantees a fault-tolerant control for the SSSC. The proposed FTONC is verified by simulation studies in the PSCAD/EMTDC environment

Measurements, Models, Systems and Design

531 s.

A Review on Application of Artificial Intelligence Techniques in Microgrids

A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources (RERs), sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This paper presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring

Maintenance management of tractors and agricultural machinery: Preventive maintenance systems

Agricultural machinery maintenance has a crucial role for successful agricultural production.  It aims at guaranteeing the safety of operations and availability of machines and related equipment for cultivation operation.  Moreover, it is one major cost for agriculture operations.  Thus, the increased competition in agricultural production demands maintenance improvement, aiming at the reduction of maintenance expenditures while keeping the safety of operations.  This issue is addressed by the methodology presented in this paper.  So, the aim of this paper was to give brief introduction to various preventive maintenance systems specially condition-based maintenance (CBM) techniques, selection of condition monitoring techniques and understanding of condition monitoring (CM) intervals, advancement in CBM, standardization of CBM system, CBM approach on agricultural machinery, advantages and disadvantages of CBM.  The first step of the methodology consists of concept condition monitoring approach for the equipment preventive maintenance; its purpose is the identification of state-of-the-art in the CM of agricultural machinery, describing the different maintenance strategies, CM techniques and methods.  The second step builds the signal processing procedure for extracting information relevant to targeted failure modes.   Keywords: agricultural machinery, fault detection, fault diagnosis, signal processing, maintenance managemen

Artificial Intelligence for Resilience in Smart Grid Operations

Today, the electric power grid is transforming into a highly interconnected network of advanced technologies, equipment, and controls to enable a smarter grid. The growing complexity of smart grid requires resilient operation and control. Power system resilience is defined as the ability to harden the system against and quickly recover from high-impact, low-frequency events. The introduction of two-way flows of information and electricity in the smart grid raises concerns of cyber-physical attacks. Proliferated penetration of renewable energy sources such as solar photovoltaic (PV) and wind power introduce challenges due to the high variability and uncertainty in generation. Unintentional disruptions and power system component outages have become a threat to real-time power system operations. Recent extreme weather events and natural disasters such as hurricanes, storms, and wildfires demonstrate the importance of resilience in the power system. It is essential to find solutions to overcome these challenges in maintaining resilience in smart grid. In this dissertation, artificial intelligence (AI) based approaches have been developed to enhance resilience in smart grid. Methods for optimal automatic generation control (AGC) have been developed for multi-area multi-machine power systems. Reliable AI models have been developed for predicting solar irradiance, PV power generation, and power system frequencies. The proposed short-horizon AI prediction models ranging from few seconds to a minute plus, outperform the state-of-art persistence models. The AI prediction models have been applied to provide situational intelligence for power system operations. An enhanced tie-line bias control in a multi-area power system for variable and uncertain environments has been developed with predicted PV power and bus frequencies. A distributed and parallel security-constrained optimal power flow (SCOPF) algorithm has been developed to overcome the challenges in solving SCOPF problem for large power networks. The methods have been developed and tested on an experimental laboratory platform consisting of real-time digital simulators, hardware/software phasor measurement units, and a real-time weather station

Forecasting in Mathematics

Mathematical probability and statistics are an attractive, thriving, and respectable part of mathematics. Some mathematicians and philosophers of science say they are the gateway to mathematics’ deepest mysteries. Moreover, mathematical statistics denotes an accumulation of mathematical discussions connected with efforts to most efficiently collect and use numerical data subject to random or deterministic variations. Currently, the concept of probability and mathematical statistics has become one of the fundamental notions of modern science and the philosophy of nature. This book is an illustration of the use of mathematics to solve specific problems in engineering, statistics, and science in general