State Estimation Fusion for Linear Microgrids over an Unreliable Network

Microgrids should be continuously monitored in order to maintain suitable voltages over time. Microgrids are mainly monitored remotely, and their measurement data transmitted through lossy communication networks are vulnerable to cyberattacks and packet loss. The current study leverages the idea of data fusion to address this problem. Hence, this paper investigates the effects of estimation fusion using various machine-learning (ML) regression methods as data fusion methods by aggregating the distributed Kalman filter (KF)-based state estimates of a linear smart microgrid in order to achieve more accurate and reliable state estimates. This unreliability in measurements is because they are received through a lossy communication network that incorporates packet loss and cyberattacks. In addition to ML regression methods, multi-layer perceptron (MLP) and dependent ordered weighted averaging (DOWA) operators are also employed for further comparisons. The results of simulation on the IEEE 4-bus model validate the effectiveness of the employed ML regression methods through the RMSE, MAE and R-squared indices under the condition of missing and manipulated measurements. In general, the results obtained by the Random Forest regression method were more accurate than those of other methods.This research was partially funded by public research projects of Spanish Ministry of Science and Innovation, references PID2020-118249RB-C22 and PDC2021-121567-C22 - AEI/10.13039/ 501100011033, and by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors, reference EPUC3M17

Smart grid state estimation and its applications to grid stabilization

University of Technology Sydney. Faculty of Engineering and Information Technology.The smart grid is expected to modernize the current electricity grid by commencing a new set of technologies and services that can make the electricity networks more secure, automated, cooperative and sustainable. The smart grid can integrate multiple distributed energy resources (DERs) into the main grid. The need for DERs is expected to become more important in the future smart grid due to the global warming and energy problems. Basically, the smart grid can spread the intelligence of the energy distribution and control system from the central unit to long-distance remote areas, thus enabling accurate state estimation and wide-area real-time monitoring of these intermittent energy sources. Reliable state estimation is a key technique to fulfil the control requirement and hence is an enabler for the automation of power grids. Driven by these motivations, this research explores the problem of state estimation and stabilization taking disturbances, cyber attacks and packet losses into consideration for the smart grid. The first contribution of this dissertation is to develop a least square based Kalman filter (KF) algorithm for state estimation, and an optimal feedback control framework for stabilizing the microgrid states. To begin with, the environment-friendly renewable microgrid incorporating multiple DERs is modelled to obtain discrete-time state-space linear equations where sensors are deployed to obtain system state information. The proposed smart grid communication system provides an opportunity to address the state regulation challenge by offering two-way communication links for microgrid information collection, estimation and stabilization. Interestingly, the developed least square based centralised KF algorithm is able to estimate the system states properly even at the beginning of the dynamic process, and the proposed H2 based optimal feedback controller is able to stabilize the microgrid states in a fairly short time. Unfortunately, the smart grid is susceptible to malicious cyber attacks, which can create serious technical, economic, social and control problems in power network operations. In contrast to the traditional cyber attack minimization techniques, this study proposes a recursive systematic convolutional (RSC) code and KF based method in the context of smart grids. The proposed RSC code is used to add redundancy in the microgrid states, and the log maximum a-posterior is used to recover the state information which is affected by random noises and cyber attacks. Once the estimated states are obtained, a semidefinite programming (SDP) based optimal feedback controller is proposed to regulate the system states. Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate as well as regulate the system states. The other significant contribution of this dissertation is to develop an adaptive-then-combine distributed dynamic approach for monitoring the grid under lossy communication links between wind turbines and the energy management system. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining local estimation results with weighting factors, which are calculated by minimizing the estimation error covariances based on SDP. Afterwards, the convergence analysis indicates that the estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the wind turbine and IEEE 6-bus distribution system. Furthermore, the distribution power sub-systems are usually interconnected to each other, so this research investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. The optimal local and neighbouring gains are computed to reach a consensus estimation after exchanging their information with the neighbouring estimators. Then the convergence of the developed algorithm is theoretically proved. Afterwards, a distributed controller is designed based on the SDP approach. Simulation results demonstrate the accuracy of the developed approaches. The penultimate contribution of this dissertation is to develop a distributed state estimation algorithm for interconnected power systems that only needs a consensus step. After modelling the interconnected synchronous generators, the optimal gain is determined to obtain a distributed state estimation. The consensus of the developed approach is proved based on the Lyapunov theory. From the circuit and system point of view, the proposed framework is useful for designing a practical energy management system as it has less computational complexity and provides accurate estimation results. The distributed state estimation algorithm is further modified by considering different observation matrices with both local and consensus steps. The optimal local gain is computed after minimizing the mean squared error between the true and estimated states. The consensus gain is determined by a convex optimization process with a given local gain. Moreover, the convergence of the proposed scheme is analysed after stacking all the estimation error dynamics. The efficacy of the developed approach is demonstrated using the environment-friendly renewable microgrid and IEEE 30-bus power system. Overall, the findings, theoretical development and analysis of this research represent a comprehensive source of information for smart grid state estimation and stabilization schemes, and will shed light on green smart energy management systems and monitoring centre design in future smart grid implementations. It is worth pointing out that the aforementioned contributions are very important in the smart grid community as communication impairments have a significant impact on grid stability and the distributed strategies can reduce communication burden and offer a sparse communication network

A novel robust predictive control system over imperfect networks

This paper aims to study on feedback control for a networked system with both uncertain delays, packet dropouts and disturbances. Here, a so-called robust predictive control (RPC) approach is designed as follows: 1- delays and packet dropouts are accurately detected online by a network problem detector (NPD); 2- a so-called PI-based neural network grey model (PINNGM) is developed in a general form for a capable of forecasting accurately in advance the network problems and the effects of disturbances on the system performance; 3- using the PINNGM outputs, a small adaptive buffer (SAB) is optimally generated on the remote side to deal with the large delays and/or packet dropouts and, therefore, simplify the control design; 4- based on the PINNGM and SAB, an adaptive sampling-based integral state feedback controller (ASISFC) is simply constructed to compensate the small delays and disturbances. Thus, the steady-state control performance is achieved with fast response, high adaptability and robustness. Case studies are finally provided to evaluate the effectiveness of the proposed approach

Design and Implementation of a Centralized Disturbance Detection System for Smart Microgrids

RÉSUMÉ L’excursion de fréquence et de tension sont parmi les défis nombreux qui se posent aux microréseaux. La détection des perturbations peut être effectuée par le système de surveillance centralisé de micro-réseaux qui utilise des données de synchrophasor rapportées à partir de différents noeuds. Les réseaux de communication de synchrophasor présentent des retards et des Pertes de paquets qui peuvent détériorer l’intégrité des données et donc compromettre la fiabilité des systèmes de surveillance et de contrôle des micro-réseaux intelligents. Ce mémoire présente un nouveau concentrateur de données de vecteurs de phase avancé (APDC) capable de contrer les manques de la communication et d’améliorer la qualité des ressources de la production décentralisée (DER) dans les micro-réseaux. L’APDC proposé utilise un système de compensation adaptatif pour obtenir une estimation efficace des éléments de données manquants. L’estimateur adaptatif utilise le taux de changement d’éléments de données pour choisir entre l’estimateur LMMSE et un estimateur basé sur les dérivés pour prédire les valeurs futures des éléments de données. Si, à un instant donné, les éléments de données synchrophasors de certaines unités de mesure de phasor (PMU) manquent, les valeurs estimées sont utilisées pour compenser les données manquantes. En outre, une unité de surveillance est proposée pour détecter de manière fiable les excursions en fréquence et identifier les DERs affectés par les îlotages. L’unité de surveillance utilise un algorithme de détection centralisé élaboré qui traite les données de fréquence pour distinguer entre l’îlotage possible des DERs et les perturbations du réseau de distribution. L’APDC proposé est développé sur la plate-forme OpenPDC en temps réel et sa performance est évaluée à l’aide d’une configuration expérimentale comprenant trois PMUs, un réseau de télécommunications, des interrupteurs, et un concentrateur de données de vecteurs de phase classique (PDC). Les résultats expérimentaux confirment une intégrité des données de haut niveau dans les conditions normales et perturbées des micro- réseaux. Des études sur l’effet du bruit de mesure montrent que l’APDC proposé est même efficace en présence de bruits sévères. De plus, une détection rapide et fiable des événements d’îlotage est obtenue en raison de l’amélioration considérable du temps de détection même en cas de pertes de données sévères et de bruit de mesure. Enfin, la performance de l’APDC proposé est comparée à une méthode d’estimation existante. Les résultats montrent l’avantage important de l’APDC, en particulier dans des conditions perturbées.----------ABSTRACT Microgrids are subject to various disturbances such as voltage transients and frequency excursions. Disturbance detection can be performed by a microgrid centralized monitoring system that employs synchrophasor data reported from different nodes within the microgrid. Synchrophasor communication networks exhibit delays and packet dropout that can undermine the data integrity and hence compromise the reliability of the monitoring and control systems of the smart microgrids. In this thesis, an advanced phasor data concentrators (APDC) is proposed that is capable of counteracting the communication impairments and improving the quality of monitoring of distributed energy resources (DERs) in microgrids. The proposed APDC utilizes an adaptive compensation scheme to achieve an efficient estimate of missing data elements. The adaptive estimator employs the rate of change of data elements to choose between the vector linear minimum mean square error (LMMSE) and the derivative-based estimators to predict the future values of data elements. Whenever the synchrophasor data elements of some phasor measurement units (PMU) are missing, the estimated values are used to compensate for the missing data. Moreover, a monitoring unit is proposed to reliably detect frequency excursions and identify the DERs affected by islanding events. The monitoring unit utilizes an elaborate centralized detection algorithm that processes frequency data to distinguish between possible islanding of DERs and disturbances occurred within the host grid. The proposed APDC is developed on a real-time OpenPDC platform and its performance is evaluated using an experimental setup including three PMUs, communication links, switches, and a conventional phasor data concentrator (PDC). The experimental results confirm a high-level data integrity under both normal and disturbed conditions. Studies on the effect of measurement noise show that the proposed APDC is even efficient in the presence of noise. Moreover, fast and reliable detection of islanding events is achieved even under severe data losses and measurement noise. Finally, the performance of the proposed APDC is compared with a recently proposed estimation method that shows the significant advantage of the APDC, especially under disturbed conditions

Smart grids as distributed learning control

The topic of smart grids has received a lot of attention but from a scientific point of view it is a highly imprecise concept. This paper attempts to describe what could ultimately work as a control process to fulfill the aims usually stated for such grids without throwing away some important principles established by the pioneers in power system control. In modern terms, we need distributed (or multi-agent) learning control which is suggested to work with a certain consensus mechanism which appears to leave room for achieving cyber-physical security, robustness and performance goals. © 2012 IEEE.published_or_final_versio

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts

The climate changes that are visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this book presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on energy internet, blockchain technology, and smart contracts, we hope that they are of interest to readers working in the related fields mentioned above

Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control

We consider a joint uplink and downlink scheduling problem of a fully distributed wireless networked control system (WNCS) with a limited number of frequency channels. Using elements of stochastic systems theory, we derive a sufficient stability condition of the WNCS, which is stated in terms of both the control and communication system parameters. Once the condition is satisfied, there exists a stationary and deterministic scheduling policy that can stabilize all plants of the WNCS. By analyzing and representing the per-step cost function of the WNCS in terms of a finite-length countable vector state, we formulate the optimal transmission scheduling problem into a Markov decision process and develop a deep-reinforcement-learning-based algorithm for solving it. Numerical results show that the proposed algorithm significantly outperforms benchmark policies.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl