Synchronized measurement data conditioning and real-time applications

Phasor measurement units (PMU), measuring voltage and current phasor with synchronized timestamps, is the fundamental component in wide-area monitoring systems (WAMS) and reveals complex dynamic behaviors of large power systems. The synchronized measurements collected from power grid may degrade due to many factors and impacts of the distorted synchronized measurement data are significant to WAMS. This dissertation focus on developing and improving applications with distorted synchronized measurements from power grid. The contributions of this dissertation are summarized below. In Chapter 2, synchronized frequency measurements of 13 power grids over the world, including both mainland and island systems, are retrieved from Frequency Monitoring Network (FNET/GridEye) and the statistical analysis of the typical power grids are presented. The probability functions of the power grid frequency based on the measurements are calculated and categorized. Developments of generation trip/load shedding and line outage events detection and localization based on high-density PMU measurements are investigated in Chapters 3 and 4 respectively. Four different types of abnormal synchronized measurements are identified from the PMU measurements of a power grid. The impacts of the abnormal synchronized measurements on generation trip/load shedding events detection and localization are evaluated. A line outage localization method based on power flow measurements is proposed to improve the accuracy of line outage events location estimation. A deep learning model is developed to detect abnormal synchronized measurements in Chapter 5. The performance of the model is evaluated with abnormal synchronized measurements from a power grid under normal operation status. Some types of abnormal synchronized measurements in the testing cases are recently observed and reported. An extensive study of hyper-parameters in the model is conducted and evaluation metrics of the model performance are presented. A non-contact synchronized measurements study using electric field strength is investigated in Chapter 6. The theoretical foundation and equation derivations are presented. The calculation process for a single circuit AC transmission line and a double circuit AC transmission line are derived. The derived method is implemented with Matlab and tested in simulation cases

Modern Power System Dynamic Performance Improvement through Big Data Analysis

Higher penetration of Renewable Energy (RE) is causing generation uncertainty and reduction of system inertia for the modern power system. This phenomenon brings more challenges on the power system dynamic behavior, especially the frequency oscillation and excursion, voltage and transient stability problems. This dissertation work extracts the most useful information from the power system features and improves the system dynamic behavior by big data analysis through three aspects: inertia distribution estimation, actuator placement, and operational studies.First of all, a pioneer work for finding the physical location of COI in the system and creating accurate and useful inertia distribution map is presented. Theoretical proof and dynamic simulation validation have been provided to support the proposed method for inertia distribution estimation based on measurement PMU data. Estimation results are obtained for a radial system, a meshed system, IEEE 39 bus-test system, the Chilean system, and a real utility system in the US. Then, this work provided two control actuator placement strategy using measurement data samples and machine learning algorithms. The first strategy is for the system with single oscillation mode. Control actuators should be placed at the bus that are far away from the COI bus. This rule increased damping ratio of eamples systems up to 14\% and hugely reduced the computational complexity from the simulation results of the Chilean system. The second rule is created for system with multiple dynamic problems. General and effective guidance for planners is obtained for IEEE 39-bus system and IEEE 118-bus system using machine learning algorithms by finding the relationship between system most significant features and system dynamic performance. Lastly, it studied the real-time voltage security assessment and key link identification in cascading failure analysis. A proposed deep-learning framework has Achieved the highest accuracy and lower computational time for real-time security analysis. In addition, key links are identified through distance matrix calculation and probability tree generation using 400,000 data samples from the Western Electricity Coordinating Council (WECC) system

Aplicação de interpretabilidade para melhorar o desempenho de um classificador LSTM para eventos de sistema de potência

Orientador: Daniel DottaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Atualmente, uma grande quantidade de dados é coletada pelos WAMS (Wide Area Measurement Systems). Portanto, existe uma clara necessidade de métodos de aprendizagem de máquina (ML - Machine Learning), capazes de extrair informações relevantes e confiáveis dos dados de sincrofasores. Entre as abordagens de ML, os modelos de Rede Neural Profunda (DNN - Deep Neural Network) têm a vantagem de aprender diretamente com os dados, tornando essas abordagens não dependentes das técnicas de extração de atributos. No entanto, esses modelos profundos produzem classificadores caixa-preta (black-box) que podem suscitar preocupações quando aplicados a ambientes de alto risco (infraestrutura crítica), como o sistema elétrico de potência (EPS-Electric Power Systems). Neste trabalho, a aplicação de um método orientado a dados (data-driven) explicável é realizada a fim de inspecionar o desempenho do classificador DNN para identificação de eventos usando medições de sincrofasores. O classificador DNN é uma LSTM (Long-Short Term Memory) que tem demostrado bom desempenho na extração de características dinâmicas. A principal vantagem dessa abordagem é o uso de uma inspeção baseada em interpretabilidade denominada SHAP (SHapley Additive exPlanation), que é baseada na teoria dos jogos cooperativos (valores Shapley), que fornece os meios para avaliar as previsões da LSTM, destacando as partes das séries temporais de entrada que mais contribuíram para a identificação dos eventos e detecção de possíveis vieses. Além disso, usando a inspeção SHAP juntamente com o conhecimento de domínio (domain knowledge) sobre o problema, o desempenho e a coerência do classificador LSTM são aprimorados ao escolher o classificador que não apenas possui a maior acurácia de identificação (IAR - Identification Accuracy Rate), mas também é coerente com o conhecimento de domínio do problema, minimizando possíveis vieses detectados. O uso dessa abordagem interpretável é útil porque: i) explica como o classificador LSTM está tomando suas decisões; ii) ajuda o designer a melhorar o treinamento do classificador; iii) certifica que o classificador resultante tem um desempenho consistente e coerente de acordo com o conhecimento do domínio; iv) quando o usuário entende que o classificador está tomando decisões coerentes, reduz claramente as preocupações da aplicação dos métodos DNN em uma infraestrutura crítica. O método proposto é avaliado usando registros reais de eventos sincrofasores do Sistema Interligado Nacional (SIN)Abstract: Nowadays, vast amounts of data are collected by Wide Area Measurement Systems (WAMS). Therefore, there is an obvious necessity for Machine Learning (ML) methods, as useful knowledge to extract relevant and reliable information from this synchrophasor data. Among the ML approaches, the Deep Neural Network (DNN) models provide an important opportunity to advance direct learning from the data, making these approaches independent from feature extraction techniques. However, these deep models produce black-box classifiers that can be matter of concern when applying to high-risk environment (critical infrastructure) such as the EPS (Electric Power Systems). In this work, the application of an explainable data-driven method is carried out in order to inspect the performance of DNN classifier for event identification using synchrophasor measurements. The DNN classifier is a Long-Short Term Memory (LSTM) with positive performance in the extraction of dynamic features. The principal benefit of this approach is the use of an interpretability inspection named SHAP (SHapley Additive exPlanation) values, which are based on cooperative game theory (Shapley values). These SHAP values provide the means to evaluate the predictions of the LSTM, highlight the parts of the input time-series with the most contribution to the identification of the events, and detect possible bias. Moreover, by employing the SHAP inspection along with domain knowledge of the problem, the performance and coherence of the LSTM classifier will be improved by choosing the classifier that not only has highest Identification Accuracy Rate (IAR) but is also coherent with domain knowledge of the problem, minimizing detected bias. The application of this interpretable approach is desirable because: i) it explains how the LSTM classifier is making its decisions; ii) it helps the designer to improve the training of the classifier; iii) it certifies that the resulting classifier has a consistent and coherent performance according to domain knowledge of the problem; iv) it clearly reduces the concerns of the application of DNN methods in a critical infrastructure, in the cases that the user understands that the classifier is taking coherent decisions. The proposed method has been evaluated using real synchrophasor event records from the Brazilian Interconnected Power System (BIPS)MestradoEnergia ElétricaMestre em Engenharia Elétrica2017/25425-5FAPES

Electromechanical Dynamics of High Photovoltaic Power Grids

This dissertation study focuses on the impact of high PV penetration on power grid electromechanical dynamics. Several major aspects of power grid electromechanical dynamics are studied under high PV penetration, including frequency response and control, inter-area oscillations, transient rotor angle stability and electromechanical wave propagation.To obtain dynamic models that can reasonably represent future power systems, Chapter One studies the co-optimization of generation and transmission with large-scale wind and solar. The stochastic nature of renewables is considered in the formulation of mixed-integer programming model. Chapter Two presents the development procedures of high PV model and investigates the impact of high PV penetration on frequency responses. Chapter Three studies the impact of PV penetration on inter-area oscillations of the U.S. Eastern Interconnection system. Chapter Four presents the impacts of high PV on other electromechanical dynamic issues, including transient rotor angle stability and electromechanical wave propagation. Chapter Five investigates the frequency response enhancement by conventional resources. Chapter Six explores system frequency response improvement through real power control of wind and PV. For improving situation awareness and frequency control, Chapter Seven studies disturbance location determination based on electromechanical wave propagation. In addition, a new method is developed to generate the electromechanical wave propagation speed map, which is useful to detect system inertia distribution change. Chapter Eight provides a review on power grid data architectures for monitoring and controlling power grids. Challenges and essential elements of data architecture are analyzed to identify various requirements for operating high-renewable power grids and a conceptual data architecture is proposed. Conclusions of this dissertation study are given in Chapter Nine

Deep learning driven data analytics for smart grids

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonAs advanced metering infrastructure (AMI) and wide area monitoring systems (WAMSs) are being deployed rapidly and widely, the conventional power grid is transitioning towards the smart grid at an increasing speed. A number of smart metering devices and real-time monitoring systems are capable to generate a huge volume of data on a daily basis. However, a variety of generated data can be made full use of to advance the development of the smart grid through big data analytics, especially, deep learning. Thus, the thesis is focused on data analysis for smart grids from three different aspects. Firstly, a real-time data driven event detection method is presented, which is quite robust when dealing with corrupted and significantly noisy data of phase measurement units (PMUs). To be specific, the presented event detection method is based on a novel combination of random matrix theory (RMT) and Kalman filtering. Furthermore, a dynamic Kalman filtering technique is proposed through the adjustment of the measurement noise covariance matrix as the data conditioner of the presented method in order to condition PMU data. The experimental results show that the presented method is indeed quite robust in such practical situations that include significant levels of noisy or missing PMU data. Secondly, a short-term residential load forecasting method is proposed on the basis of deep learning and k-means clustering, which is capable to extract similarity of residential load effectively and perform prediction accurately at the individual residential level. Specifically, it makes full use of k-means clustering to extract similarity among residential load and deep learning to extract complex patterns of residential load. In addition, in order to improve the forecasting accuracy, a comprehensive feature expression strategy is utilised to describe load characteristics of each time step in detail. The experimental results suggest that the proposed method can achieve a high forecasting accuracy in terms of both root mean square error (RMSE) and mean absolute error (MAE). Thirdly, an online individual residential load forecasting method is developed based on a combination of deep learning and dynamic mirror descent (DMD), which is able to predict residential load in real time and adjust the prediction error over time in order to improve the prediction performance. More specifically, it firstly employs a long short term memory (LSTM) network to build a prediction model offline, and then applies it online with DMD correcting the prediction error. In order to increase the prediction accuracy, a comprehensive feature expression strategy is used to describe load characteristics at each time step in detail. The experimental results indicate that the developed method can obtain a high prediction accuracy in terms of both RMSE and MAE. To sum up, the proposed real-time event detection method contributes to the monitoring and operation of smart grids, while the proposed residential load forecasting methods contribute to the demand side response in smart grids.TDX-ASSIS

Advanced Data Analytics for Data-rich Multistage Manufacturing Processes

Nowadays, multistage manufacturing processes (MMPs) are usually equipped with complex sensing systems. They generate data with several unique characteristics: the output quality measurements from each stage are of different types, the comprehensive set of inputs (or process variables) have distinct degrees of influence over the process, and the relationship between the inputs and outputs is sometimes ambiguous, and multiple types of faults repetitively occur to the process during its operation. These characteristics of the data lead to new challenges in the data analytics of MMPs. In this thesis, we conduct three studies to tackle those new challenges from MMPs. In the first study, we propose a feature ranking scheme that ranks the process features based on their relationship with the final product quality. Our ranking scheme is called sparse distance correlation (SpaDC), and it satisfies the important diversity criteria from the engineering perspective and encourages the features that uniquely characterize the manufacturing process to be prioritized. The theoretical properties of SpaDC are studied. Simulations, as well as two real-case studies, are conducted to validate the method. In the second study, we propose a holistic modeling approach for the MMPs, aiming at understanding how intermediate quality measurements of mixed profile outputs relate to sparse effective inputs. This model can identify the effective inputs, output variation patterns, and establish connections between them. Specifically, the aforementioned objective is achieved by formulating and solving an optimization problem that involves the effects of process inputs on the outputs across the entire MMP. This ADMM algorithm that solves this problem is highly parallelizable and thus can handle a large amount of data of mixed types obtained from MMPs. In the third study, a retrospective analysis method is proposed for multiple functional signals. This method simultaneously identifies when multiple events occur to the system and characterizes how they affect the multiple sensing signals. A problem is formulated using the dictionary learning method, and the solution is obtained by iteratively updating the event signatures and sequences using ADMM algorithms. In the end, the potential extensions to the general interconnect systems are discussed.Ph.D

Prédiction de l'instabilité dynamique des réseaux électriques par apprentissage supervisé des signaux de réponses post-contingence sur des dictionnaires surcomplets

Ces dernières décennies, l'intégration aux réseaux électriques de capteurs intelligents incorporant la mesure synchronisée des phaseurs a contribué à enrichir considérablement les bases de données de surveillance en temps réel de la stabilité des réseaux électriques. En parallèle, la lutte aux changements climatiques s'est accompagnée d'un déploiement généralisé des sources d'énergies renouvelables dont l'intermittence de la production et le déficit d'inertie dû à l'interface de celle-ci par l'électronique de puissance, contribuent à augmenter les risques d'instabilité à la suite de contingences de réseau. Dans ce contexte, nous proposons d'appliquer aux données de synchrophaseurs de nouvelles approches d'intelligence de données inspirées par l'analyse massive des séries chronologiques et l'apprentissage sur des dictionnaires supervisés, permettant d'extraire des centaines d'attributs décrivant concisément les estimations d'état dynamique des générateurs de réseaux électriques. La mise en évidence d'une signification physique de ces attributs permet ensuite une classification de la stabilité dynamique qui s'éloigne de ce fait des boîtes noires produites par un apprentissage en profondeur « à l'aveugle » des séries chronologiques, pour évoluer vers une approche transparente plus adaptée à la salle de conduite des réseaux et acceptable pour les ingénieurs d'exploitation. Cette approche d'apprentissage machine « interprétable » par les humains, débouche de surcroît sur une détection fiable, utilisant de courtes fenêtres de données de vitesses d'alternateurs directement mesurées ou reconstituées par estimation d'état dynamique à partir de l'instant d'élimination du défaut, pour détecter toute instabilité subséquente, avec un temps de préemption suffisant pour activer des contremesures permettant de sauvegarder la stabilité du réseau et ainsi prévenir les pannes majeures. Notre travail aborde l'exploitation de cette nouvelle niche d'information par deux approches complémentaires d'intelligence des données : 1) une analyse non parcimonieuse d'une base d'attributs se chiffrant par centaines, calculés automatiquement par l'analyse numérique massive des séries chronologiques de signaux de réponses post-contingence des générateurs; et 2) une analyse parcimonieuse exploitant l'apprentissage supervisée de grands dictionnaires surcomplets pour habiliter une prédiction de l'instabilité sur de courtes fenêtres de données avec une représentation vectorielle creuse (contenant un grand nombre de zéros) et donc numériquement très efficiente en plus de l'interprétabilité inhérente des atomes constituant les dictionnaires. Au niveau méthodologique, l'approche non parcimonieuse vise à implémenter plusieurs méthodes analytiques combinées (notamment la transformée de Fourier, la transformée en ondelette, la méthode de Welch, la méthode de périodogramme et les exposants de Lyapunov) pour extraire du signal de réponse de chaque générateur des centaines d'attributs labellisés et servant à construire un espace physique d'indicateurs de stabilité à haute dimension (HDSI). Ceux-ci sont ensuite utilisés pour développer les prédicteurs de stabilité sur la base d'algorithmes standard de machine learning, par exemple le convolutional neural network (CNN), long short-term memory (LSTM), support vector machine (SVM), AdaBoost ou les forêts aléatoires. L'approche parcimonieuse implémentée consiste à développer deux techniques complémentaires : 1) un dictionnaire d'apprentissage supervisé joint (SLOD) au classificateur et 2) vingt dictionnaires d'apprentissage séparés des signaux associés aux cas stable/instable. Alors que le SLOD utilise des dictionnaires adaptatifs inspirés des données mesurées et apprises hors-ligne, la deuxième approche utilise des dictionnaires fixes pour reconstruire séparément les signaux des classes stables et instables. Dans les deux cas, l'étape finale consiste à identifier automatiquement en temps réel, la classe d'appartenance d'une réponse par reconstruction des signaux associés à partir des dictionnaires appris hors-ligne. L'analyse parcimonieuse des réponses des générateurs sur un dictionnaire d'apprentissage adaptatif joint au classificateur a été implémenté à partir de l'algorithme K-singular value de composition (KSVD) couplé à l'orthogonal matching pursuit (OMP), afin de reconstruire et prédire la stabilité dynamique des réseaux électriques. De plus, vingt décompositions parcimonieuses des signaux sur des dictionnaires fixes (simples et hybrides) ont permis de développer des classificateurs prédisant chaque classe séparément sur la base de la transformée en cosinus discrète (DCT), en sinus discrète (DST), en ondelette (DWT), de la transformée de Haar (DHT), et le dictionnaire de Dirac (DI) couplés à l'orthogonal matching pursuit (OMP). Cette étude démontre que la décomposition parcimonieuse sur un dictionnaire adaptatif joint au classificateur offre une performance proche de l'idéal (c'est-à-dire : 99,99 % précision, 99,99 % sécurité et 99,99 % fiabilité) de loin supérieure à celle d'un classificateur à reconstruction de signaux basée sur les vingt dictionnaires fixes ou adaptatifs séparés, et les classificateurs basés sur les moteurs de machine learning (SVM, ANN, DT, RF, AdaBoost, CNN et LSTM) implémentés à partir des indices HDSI extraits de la base de données des vitesses des rotors des réseaux IEEE 2 area 4 machines, IEEE 39 -bus et IEEE 68 -bus. Toutefois, le temps de resimulation (replay) en temps réel des dictionnaires fixes/adaptatifs séparés est nettement inférieur (de 30-40%) à celui observé pour le dictionnaire adaptatif à classificateur joint / SLOD, et les algorithmes modernes de machine learning utilisant les attributs de type HDSI comme intrants.In recent decades, the integration of smart sensors incorporating synchronized phasor measurements units (PMU) into power grids has contributed to a significant improvement of the databases for real-time monitoring of power grid stability. In parallel, the fight against climate change has been accompanied by a widespread deployment of renewable energy sources whose intermittency of production and the lack of inertia due to the interface of the latter by power electronics; contribute to increase the risks of instability following network contingencies. In this context, we propose to apply new data intelligence approaches inspired by massive time series analysis and supervised dictionary learning to synchrophasor data, allowing the extraction of hundreds of attributes concisely describing the dynamic state estimates of power system generators. The physical meaning identification of these attributes then allows for an online classification of dynamic stability, thus moving away from the black boxes produced by «blind» deep learning of time series to a transparent approach more suitable for the network control room and acceptable to operating engineers. This human-interpretable machine learning approach also leads to reliable detection, using short windows of generator speed data directly measured or reconstructed by dynamic state estimation from the instant of fault elimination, to detect any subsequent instability, with sufficient preemption time to activate false measures to safeguard the network stability and thus prevent major outages. Our work addresses the exploitation of this new information through two complementary data intelligence approaches : 1) a non-sparse analysis of an attribute base numbering in the hundreds, computed automatically by massive numerical analysis of post-contingency response signal time series from generators; and 2) a sparse analysis exploiting supervised learning of large overcomplete dictionaries to enable instability prediction over short windows of data with a hollow vector representation (containing a large number of zeros) and thus numerically very efficient in addition to the inherent interpretability of the atoms constituting the dictionaries. Methodologically, the non-sparse approach aims to implement several combined analytical methods (including Fourier transform, wavelet transform, Welch's method, periodogram method and Lyapunov exponents) to extract hundreds of labeled attributes from the response signal of each generator and used to construct a physical space of high-dimensional stability indicators (HDSI). These are used to develop stability predictors based on standard machine learning algorithms, e.g., CNN, LSTM, SVM, AdaBoost or random forests. The implemented sparse approach consists in developing two complementary techniques: 1) a supervised learning dictionary attached (SLOD) to the classifier and 2) twenty separate dictionaries learning of the signals associated with the stable/instable cases. While the SLOD uses adaptive dictionaries inspired by the measured and learned offline data, the second approach uses fixed dictionaries to reconstruct the stable and unstable signals classes separately. In both cases, the final step is automatically identified in real time the status to which a response belongs by reconstructing the associated signals from the off-line learned dictionaries. The sparse analysis of generator responses on an adaptive learning dictionary attached to the classifier was implemented using the K-singular value decomposition (KSVD) algorithm coupled with orthogonal matching pursuit (OMP), to reconstruct and predict online the dynamic stability of power systems. In addition, twenty sparse signal decompositions on fixed dictionaries (simple and hybrid) were used to develop classifiers predicting each class separately based on the discrete cosine transform (DCT), discrete sine transform (DST), wavelet transform (DWT), Haar transform (DHT), and Dirac dictionary (DI) coupled with the orthogonal matching pursuit (OMP). This study demonstrates that sparse decomposition on joined adaptive dictionary to the classifier provides near ideal performance (i.e.: 99.99% accuracy, 99.99% security, and 99.99% reliability) far superior to that of a classifier has signal reconstruction based on the twenty separate fixed or adaptive dictionaries and classifiers based on machine learning engines (SVM, ANN, DT, RF, AdaBoost, CNN, and LSTM) implemented from HDSI indices extracted from the rotor speed database of the IEEE 2 area 4 machines, IEEE 39 -bus, and IEEE 68 -bus test systems. However, the real-time replay time of the separate fixed/adaptive dictionaries is significantly lower (by 30-40%) than that observed for the adaptive dictionary with joint classifier/SLOD, and modern machine learning algorithms using HDSI-like attributes as inputs