Harmonic Estimation Of Distorted Power Signals Using PSO – Adaline

In recent times, power system harmonics has got a great deal of interest by many Power system Engineers. It is primarily due to the fact that non-linear loads comprise an increasing portion of the total load for a typical industrial plant. This increase in proportion of non-linear load and due to increased use of semi-conductor based power processors by utility companies has detoriated the Power Quality. Harmonics are a mathematical way of describing distortion in voltage or current waveform. The term harmonic refers to a component of a waveform occurs at an integer multiple of the fundamental frequency. Several methods had been proposed, such as discrete Fourier transforms, least square error technique, Kalman filtering, adaptive notch filters etc; Unlike above techniques, which treat harmonic estimation as completely non-linear problem there are some other hybrid techniques like Genetic Algorithm (GA), LS-Adaline, LS-PSOPC which decompose the problem of harmonic estimation into linear and non-linear problem. The results of LS-PSOPC and LS-Adaline has most attractive features of compactness and fastness. . Our new proposed technique tries to reduce the pitfalls in the LS-PSOPC, LS-Adaline techniques. With new technique we tried to estimate the Amplitudes by Least square estimator, frequency of the signal by PSOPC and phases of the harmonics by Adaline technique using MATLAB program. Harmonic signals were estimated by using LS-PSOPC, PSOPC-Adaline. Errors in estimating the signal by both the techniques are calculated and compared with each other

Distribution system reliability enhancement

Practically all everyday life tasks from economic transactions to entertainment depend on the availability of electricity. Some customers have come to expect a higher level of power quality and availability from their electric utility. Federal and state standards are now mandated for power service quality and utilities may be penalized if the number of interruptions exceeds the mandated standards. In order to meet the requirement for safety, reliability and quality of supply in distribution system, adaptive relaying and optimal network reconfiguration are proposed. By optimizing the system to be better prepared to handle a fault, the end result will be that in the event of a fault, the minimum number of customers will be affected. Thus reliability will increase. The main function of power system protection is to detect and remove the faulted parts as fast and as selectively as possible. The problem of coordinating protective relays in electric power systems consists of selecting suitable settings such that their fundamental protective function is met under the requirements of sensitivity, selectivity, reliability, and speed. In the proposed adaptive relaying approach, weather data will be incorporated as follows. By using real-time weather information, the potential area that might be affected by the severe weather will be determined. An algorithm is proposed for adaptive optimal relay setting (relays will optimally react to a potential fault). Different types of relays (and relay functions) and fuses will be considered in this optimization problem as well as their coordination with others. The proposed optimization method is based on mixed integer programming that will provide the optimal relay settings including pickup current, time dial setting, and different relay functions and so on. The main function of optimal network reconfiguration is to maximize the power supply using existing breakers and switches in the system. The ability to quickly and flexibly reconfigure the power system of an interconnected network of feeders is a key component of Smart Grid. New technologies are being injected into the distribution systems such as advanced metering, distribution automation, distribution generation and distributed storage. With these new technologies, the optimal network reconfiguration becomes more complicated. The proposed algorithms will be implemented and demonstrated on a realistic test system. The end result will be improved reliability. The improvements will be quantified with reliability indexes such as SAIDI.M.S.Committee Chair: A.P. Meliopoulos; Committee Member: Carlos S Grijalva; Committee Member: Deepakraj M Diva

Integration of Massive Plug-in Hybrid Electric Vehicles into Power Distribution Systems: Modeling, Optimization, and Impact Analysis

With the development of vehicle-to-grid (V2G) technology, it is highly promising to use plug-in hybrid electric vehicles (PHEVs) as a new form of distributed energy resources. However, the uncertainties in the power market and the conflicts among different stakeholders make the integration of PHEVs a highly challenging task. Moreover, the integration of PHEVs may lead to negative effects on the power grid performance if the PHEV fleets are not properly managed. This dissertation studies various aspects of the integration of PHEVs into power distribution systems, including the PHEV load demand modeling, smart charging algorithms, frequency regulation, reliability-differentiated service, charging navigation, and adequacy assessment of power distribution systems. This dissertation presents a comprehensive methodology for modeling the load demand of PHEVs. Based on this stochastic model of PHEV, a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm is proposed to integrate PHEVs into a residential distribution grid. This dissertation also develops an innovative load frequency control system, and proposes a hierarchical game framework for PHEVs to optimize their charging process and participate in frequency regulation simultaneously. The potential of using PHEVs to enable reliability-differentiated service in residential distribution grids has been investigated in this dissertation. Further, an integrated electric vehicle (EV) charging navigation framework has been proposed in this dissertation which takes into consideration the impacts from both the power system and transportation system. Finally, this dissertation proposes a comprehensive framework for adequacy evaluation of power distribution networks with PHEVs penetration. This dissertation provides innovative, viable business models for enabling the integration of massive PHEVs into the power grid. It helps evolve the current power grid into a more reliable and efficient system

Intelligent power system operation in an uncertain environment

This dissertation presents some challenging problems in power system operations. The efficacy of a heuristic method, namely, modified discrete particle swarm optimization (MDPSO) algorithm is illustrated and compared with other methods by solving the reliability based generator maintenance scheduling (GMS) optimization problem of a practical hydrothermal power system. The concept of multiple swarms is incorporated into the MDPSO algorithm to form a robust multiple swarms-modified particle swarm optimization (MS-MDPSO) algorithm and applied to solving the GMS problem on two power systems. Heuristic methods are proposed to circumvent the problems of imposed non-smooth assumptions common with the classical approaches in solving the challenging dynamic economic dispatch problem. The multi-objective combined economic and emission dispatch (MO-CEED) optimization problem for a wind-hydrothermal power system is formulated and solved in this dissertation. This MO-CEED problem formulation becomes a challenging problem because of the presence of uncertainty in wind power. A family of distributed optimal Pareto fronts for the MO-CEED problem has been generated for different scenarios of capacity credit of wind power. A real-time (RT) network stability index is formulated for determining a power system\u27s ability to continue to provide service (electric energy) in a RT manner in case of an unforeseen catastrophic contingency. Cascading stages of fuzzy inference system is applied to combine non real-time (NRT) and RT power system assessments. NRT analysis involves eigenvalue and transient energy analysis. RT analysis involves angle, voltage and frequency stability indices. RT Network status index is implemented in real-time on a practical power system --Abstract, page iv

Secure Remote Attestation for Safety-Critical Embedded and IoT Devices

In recent years, embedded and cyber-physical systems (CPS), under the guise of Internet-of-Things (IoT), have entered many aspects of daily life. Despite many benefits, this develop-ment also greatly expands the so-called attack surface and turns these newly computerizedgadgets into attractive attack targets. One key component in securing IoT devices is malwaredetection, which is typically attained with (secure) remote attestation. Remote attestationis a distinct security service that allows a trusted verifier to verify the internal state of aremote untrusted device. Remote attestation is especially relevant for low/medium-end em-bedded devices that are incapable of protecting themselves against malware infection. Assafety-critical IoT devices become commonplace, it is crucial for remote attestation not tointerfere with the device’s normal operations. In this dissertation, we identify major issues inreconciling remote attestation and safety-critical application needs. We show that existingattestation techniques require devices to perform uninterruptible (atomic) operations duringattestation. Such operations can be time-consuming and thus may be harmful to the device’ssafety-critical functionality. On the other hand, simply relaxing security requirements of re-mote attestation can lead to other vulnerabilities. To resolve this conflict, this dissertationpresents the design, implementation, and evaluation of several mitigation techniques. In par-ticular, we propose two light-weight techniques capable of providing interruptible attestationmodality. In contrast to traditional techniques, our proposed techniques allow interrupts tooccur during attestation while ensuring malware detection via shuffled memory traversals ormemory locking mechanisms. Another type of techniques pursued in this dissertation aimsto minimize the real-time computation overhead during attestation. We propose using peri-odic self-measurements to measure and record the device’s state, resulting in more flexiblescheduling of the attestation process and also in no real-time burden as part of its interactionwith verifier. This technique is particularly suitable for swarm settings with a potentiallylarge number of safety-critical devices. Finally, we develop a remote attestation HYDRAarchitecture, based on a formally verified component, and use it as a building block in ourproposed mitigation techniques. We believe that this architecture may be of independentinterest

Dynamic Reactive Assignment of Tasks in Real-Time Automated Guided Vehicle Environments with Potential Interruptions

An efficient management of production plants has to consider several external and internal factors, such as potential interruptions of the ongoing processes. Automated guided vehicles (AGVs) are becoming a widespread technology that offers many advantages. These AGVs can perform complex tasks in an autonomous way. However, an inefficient schedule of the tasks assigned to an AGV can suffer from unwanted interruptions and idle times, which in turn will affect the total time required by the AGV to complete its assigned tasks. In order to avoid these issues, this paper proposes a heuristic-based approach that: (i) makes use of a delay matrix to estimate circuit delays for different daily times; (ii) employs these estimates to define an initial itinerary of tasks for an AGV; and (iii) dynamically adjusts the initial agenda as new information on actual delays is obtained by the system. The objective is to minimize the total time required for the AGV to complete all the assigned tasks, taking into account situations that generate unexpected disruptions along the circuits that the AGV follows. In order to test and validate the proposed approach, a series of computational experiments utilizing real-life data are carried out. These experiments allow us to measure the improvement gap with respect to the former policy used by the system managers

Dynamic reactive assignment of tasks in real-time automated guided vehicle environments with potential interruptions

An efficient management of production plants has to consider several external and internal factors, such as potential interruptions of the ongoing processes. Automated guided vehicles (AGVs) are becoming a widespread technology that offers many advantages. These AGVs can perform complex tasks in an autonomous way. However, an inefficient schedule of the tasks assigned to an AGV can suffer from unwanted interruptions and idle times, which in turn will affect the total time required by the AGV to complete its assigned tasks. In order to avoid these issues, this paper proposes a heuristic-based approach that: (i) makes use of a delay matrix to estimate circuit delays for different daily times; (ii) employs these estimates to define an initial itinerary of tasks for an AGV; and (iii) dynamically adjusts the initial agenda as new information on actual delays is obtained by the system. The objective is to minimize the total time required for the AGV to complete all the assigned tasks, taking into account situations that generate unexpected disruptions along the circuits that the AGV follows. In order to test and validate the proposed approach, a series of computational experiments utilizing real-life data are carried out. These experiments allow us to measure the improvement gap with respect to the former policy used by the system managers.This work has been partially supported by the Spanish Ministry of Industry, Commerce and Tourism (AEI-010500-2021b-54), the EU Comission (HORIZON-CL4-2021-TWIN-TRANSITION-01-07, 101057294 AIDEAS), and the Generalitat Valenciana (PROMETEO/2021/065).Peer ReviewedPostprint (published version

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Conceptual multi-agent system design for distributed scheduling systems

With the progressive increase in the complexity of dynamic environments, systems require an evolutionary configuration and optimization to meet the increased demand. In this sense, any change in the conditions of systems or products may require distributed scheduling and resource allocation of more elementary services. Centralized approaches might fall into bottleneck issues, becoming complex to adapt, especially in case of unexpected events. Thus, Multi-agent systems (MAS) can extract their automatic and autonomous behaviour to enhance the task effort distribution and support the scheduling decision-making. On the other hand, MAS is able to obtain quick solutions, through cooperation and smart control by agents, empowered by their coordination and interoperability. By leveraging an architecture that benefits of a collaboration with distributed artificial intelligence, it is proposed an approach based on a conceptual MAS design that allows distributed and intelligent management to promote technological innovation in basic concepts of society for more sustainable in everyday applications for domains with emerging needs, such as, manufacturing and healthcare scheduling systems.This work has been supported by FCT - Fundação para a Ciência e a Tecnologia within the R&D Units Projects Scope: UIDB/00319/2020 and UIDB/05757/2020. Filipe Alves is supported by FCT Doctorate Grant Reference SFRH/BD/143745/2019.info:eu-repo/semantics/publishedVersio