Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification

This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two-dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.Comment: An updated version of this manuscript has been accepted by the 2018 IEEE International Conference on Energy Internet (ICEI), Beijing, Chin

Impact of non-synchronous generation on transmission oscillations paths

The large scale penetration of non-synchronous generation has been causing several impacts on the power systems dynamics. The low-frequency oscillations affect the power exchanged along the transmission lines/corridors. This paper uses the Multi-Prony Analysis mode estimation technique to monitor and suggest the dominant oscillation modes which can be useful for wide-area control purposes. Moreover, the oscillation modes are also monitored under gradual cases of non-synchronous generation integration in the system. The methodology is applied to two different test transmission systems: i) the two area system and, ii) the Nordic 32 system. The results illustrate the similarity and differences in the scenarios proposed

Early Anomaly Detection and Classification with Streaming Synchrophasor Data in Electric Energy Systems

The large-scale streaming data collected from the increasing deployed phasor measurement unit (PMU) devices poses significant difficulties for real-time data-driven analytics in power systems. This dissertation presents a dimensionality-reduction-based monitoring framework to make better use of the streaming PMU data for early anomaly detection and classification in power systems. The first part of this dissertation studies the fundamental dimensionality of large-scale PMU data, and proposes an online application for early anomaly detection using the reduced dimensionality. First, PMU data under both normal and abnormal conditions are analyzed by principal component analysis (PCA), and the results suggest an extremely low underlying dimensionality despite the large number of raw measurements. In comparison with prior work of utilizing multi-channel high-dimensional PMU data for power system anomaly detection, the proposed early anomaly detection algorithm employs the reduced-dimensional data from PCA, and detects the occurrence of an anomaly based on the change of core subspaces of the low-dimensional PMU data. Theoretical justification for the algorithm is provided using linear dynamical system theory. It is demonstrated that the proposed algorithm is capable to detect general power system anomalies at an earlier stage than would be possible by monitoring the raw PMU data. The second part of this dissertation investigates the classification of a special anomaly in power systems, low-frequency oscillation, which may cause severe impacts on power systems while at the same time is difficult to be accurately classified. We present a robust classification framework with online detection and mode estimation of low-frequency oscillations by using synchrophasor data. Based on persistent homology, a cyclicity response function is proposed to detect an oscillation, through the use of the low-dimensional features (pre-PCA features) extracted from PCA. Whenever the cyclicity response exceeds a numerically robust threshold, an oscillation can be detected. After the detection, PCA is applied again to extract the low-dimensional features (post-PCA features) from the multi-channel transient PMU data. It is shown that the post-PCA features preserve the underlying modal information in a more robust way in comparison to raw synchrophasor measurements. Based on the post-PCA features, fast Fourier transform (FFT) and Prony analysis can be subsequently applied to extract modal information of the oscillation. The proposed classification framework offers system operators a data-driven analytical tool for fast detection of low-frequency oscillation and robust mode estimation against high measurement noise

キャンパスWAMSによる改良されたヒルベルトホーン変換を用いた電力系統動揺特性解析

九州工業大学博士学位論文 学位記番号:工博甲第390号 学位授与年月日:平成27年3月25日Chapter1. Introduction||Chapter2. Wide-Area Measurement System Using Synchrophasors Technology||Chapter3. On-line Oscillation Characteristics Monitoring Algorithm Analysis||Chapter4. The Enhanced HHT Method||Chapter5. The Developed Oscillation Monitoring System||Chapter6. ConclusionsThis dissertation presents a complete oscillation monitoring system based on real-time wide-area measurements from PMUs. This oscillation monitoring system employs the enhanced Hilbert-Huang transform (HHT) to analyze power system oscillation characteristics and estimate the damping of oscillatory modes from ambient data. This new oscillation system can give an indication of the damping of transient oscillations that will follow a disturbance, once it occurs. The application is based on a system identification procedure that is carried out in real-time. This research studies various low frequency oscillation analysis algorithms. It mainly introduces the concept, character and implementation process of FFT, WLT and HHT method. According to the characteristics of low frequency oscillation signal we can get advantage and disadvantage of these algorithms. It is important to remember that power system is actually a high-order time-varying nonlinear system. Only under certain circumstances can it be simplified to linear or time-invariant systems. Although ambient condition is reasonably molded as a linear system, for system response following some events, nonlinearities play an important role in the measured data. HHT is a new type of nonlinear and non-stationary signal processing method. Compared with other methods, HHT has absolute advantage of analyzing low frequency oscillation signal because the power system responses following system disturbances contain both linear and nonlinear phenomena. Nevertheless, the traditional methods, whether FFT or WLT, etc. the signals are approximately processed as linear signal when analysis non-linear and non-stationary signals. This feature is the main advantage of HHT algorithm, which is also widely used by the reasons. Secondly, HHT method is adaptive, which means that can be adaptive extracted from the signal decomposed by EMD itself. It is based on an adaptive basis, and the frequency is defined through the Hilbert transform. Consequently, the "base" of Fourier transform is the trigonometric functions, the "base" of wavelet transform requires pre-selected. Therefore, HHT has completely adaptability. Third, it is suitable for analysis mutation signal. Due to the Heisenberg uncertainty principle constraint, many traditional algorithms must be satisfied the product of frequency window by time window is constant. This property makes these algorithms cannot achieve high precision both in time domain and frequency domain at the same time. Nevertheless, there is no uncertainty principle limitation on time or frequency resolution from the convolution pairs based on a priori bases. For these reasons, it can be said applying HHT method to dealing with power system oscillation signal is a good choice. However, it is still have some issues need to be resolved carefully. To ensure accurate monitoring of system dynamics with noise-polluted WAMS measurements, serval key signal-processing techniques are implemented to improve HHT method in this research: Data pre-treatment processing, the boundary end effect problem caused by the Empirical mode decomposition(EMD) algorithm and the boundary end effect problem caused by Hilbert transform based on Auto-Regressive and Moving Average Model (ARMA). There are six methods: a). polynomial extension method, b). slope method extension method, c). parallel extension method, d). extreme point symmetric extension method, e). mirror method f). Boundary local characteristic scale extension methods are used to inhibit the boundary end effects, which results in a serious distortion in the EMD sifting process. Furthermore, an integrated scheme for the monitoring and detection of low-frequency oscillations has been developed based on HHT algorithm for oscillation analysis in CampusWAMS projects. By analyzing the real-time synchro-phasors, the proposed scheme is competent to identify the characteristics of the low-frequency oscillations in real-time. Third, this dissertation presents an estimation algorithm method based on enhanced HHT for the parameters of a low frequency oscillation signal in power system. In the end, the developed scheme is tested with simulated signals and measurements from CampusWAMS. An oscillation monitoring system based on real-time wide-area measurements from PMUs is established. It can determine the center rage frequency of the concerned mode automatically and accurately, which is then be used to determine the parameter of the extraction. The extracted mode frequency, damping and mode shape can be detected by this oscillation monitoring system. The results have convincingly demonstrated the validity and practicability of the developed scheme

Algorithms to Improve Performance of Wide Area Measurement Systems of Electric Power Systems

Power system operation has become increasingly complex due to high load growth and increasing market pressure. The occurrence of major blackouts in many power systems around the world has necessitated the use of synchrophasor based Wide Area Measurement Systems (WAMS) for grid monitoring. Synchrophasor technology is comparatively new in the area of power systems. Phasor measurement units (PMUs) and phasor data concentrators (PDCs) are new to the substations and control centers. Even though PMUs have been installed in many power grids, the number of installed PMUs is still low with respect to the number of buses or lines. Currently, WAMS systems face many challenges. This thesis is an attempt towards solving some of the technical problems faced by the WAMS systems. This thesis addresses four problems related to synchrophasor estimation, synchrophasor quality detection, synchrophasor communication and synchrophasor application. In the first part, a synchrophasor estimation algorithm has been proposed. The proposed algorithm is simple, requires lesser computations, and satisfies all the steady state and dynamic performance criteria of the IEEE Standard C37.118.1-2011 and also suitable for protection applications. The proposed algorithm performs satisfactorily during system faults and it has lower response time during larger disturbances. In the second part, areas of synchrophasor communication which can be improved by applying compressive sampling (CS) are identified. It is shown that CS can reduce bandwidth requirements for WAMS networks. It is also shown that CS can successfully reconstruct system dynamics at higher rates using synchrophasors reported at sub-Nyquist rate. Many synchrophasor applications are not designed to use fault/switching transient synchrophasors. In this thesis, an algorithm has been proposed to detect fault/switching transient synchrophasors. The proposed algorithm works satisfactorily during smaller and larger step changes, oscillations and missing data. Fault transient synchrophasors are not usable in WAMS applications as they represent a combination of fault and no-fault scenario. In the fourth part, two algorithms have been proposed to extract fault synchrophasor from fault transient synchrophasor in PDC. The proposed algorithms extract fault synchrophasors accurately in presence of noise, off-nominal frequencies, harmonics, and frequency estimation errors

FFTと連続ウェーブレット変換法を用いた同期位相計測に基づく電力システムのモード検出とダンピング推定

The thesis carries out the estimation of damping as well as the frequencymode of inter area oscillations in the range of 0.1 to 1.0 Hz. This belongs underthe topic of angle stability management of power systems. Previously some otherstudies had been conducted in this area at which most of them employed themethods such as the least squares, Yule-Walker, autoregressive (AR),autoregressive moving average (ARMA), the Kalman filter and the subspacemethod. Another research also had been conducted which based on Fast FourierTransform (FFT) analysis individually, the damping ratio and frequencyoscillation were estimated from eigenvalue of the matrix associated to a SingleMachine Infinite Bus (SMIB) model. An output-only-based simplified oscillationmodel was developed to estimate the characteristic of inter-area power oscillationbased on extracted oscillation data. However, this previous method did notexplain how to calculate damping ratio without considering any simplified model.Furthermore, the behavior of the signal during certain time of analysis could notbe described.This thesis promotes a novel approach in analyzing PMU data based onFast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT)algorithm. Then proceed by demodulating the slicing signal at a particular peakand ridge of the signal using a decrement technique. The approach applied in thisthesis can be classified into the non-parametric approach, where it works directlyon the data. The damping calculation method in this thesis emphasized on theaccurate and robust damping estimations which was proved by attempting thesimulation towards various level of signal to noise ratio (SNR).To verify the outcome of this method a synthesized signal contains ofthree ringdown modes representing a real signal from PMU was analyzed. Theresults were compared to the given parameters and it was clearly shown that thismethod gave the result within an acceptable range of error. Additionally, theacceptability of this method was also verified by comparing to the result ofeigenvalue-based calculation on a standard power system model. The simulationindicated the results of the two approaches fitted each other means this FFT-CWTis workable to assess the damping ratio of a small signal oscillation in powersystem. The advantage of this method is no prior data of the system required;hence this approach is very applicable in the power system where gathering datafrom the network is not attainable.This thesis also elaborated the application of wide area signal recorded byPMU, refined by the FFT-CWT method, for controlling the oscillation damping ofpower system. The simulation showed the application of wide area signal as aninput to the damping controller has a great prospective to countermeasure the interarea oscillation in the system.九州工業大学博士学位論文 学位記番号:工博甲第413号 学位授与年月日:平成28年3月25日1. INTRODUCTION|2. SYNCHROPHASOR MEASUREMENT AND THE METHOD OF ANALYSIS|3. FAST FOURIER TRANSFORM AND CONTINUOUS WAVELET TRANSFORM APPROACH|4. APPLICATION OF THE APPROACH FOR MODE AND DAMPING CALCULATION|5. WIDE AREA SIGNAL DAMPING CONTROLLER|6. CONCLUSION AND FUTURE WORK九州工業大学平成27年

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

Comparative review of methods for stability monitoring in electrical power systems and vibrating structures

This study provides a review of methods used for stability monitoring in two different fields, electrical power systems and vibration analysis, with the aim of increasing awareness of and highlighting opportunities for cross-fertilisation. The nature of the problems that require stability monitoring in both fields are discussed here as well as the approaches that have been taken. The review of power systems methods is presented in two parts: methods for ambient or normal operation and methods for transient or post-fault operation. Similarly, the review of methods for vibration analysis is presented in two parts: methods for stationary or linear time-invariant data and methods for non-stationary or non-linear time-variant data. Some observations and comments are made regarding methods that have already been applied in both fields including recommendations for the use of different sets of algorithms that have not been utilised to date. Additionally, methods that have been applied to vibration analysis and have potential for power systems stability monitoring are discussed and recommended. � 2010 The Institution of Engineering and Technology

Real-Time Machine Learning Models To Detect Cyber And Physical Anomalies In Power Systems

A Smart Grid is a cyber-physical system (CPS) that tightly integrates computation and networking with physical processes to provide reliable two-way communication between electricity companies and customers. However, the grid availability and integrity are constantly threatened by both physical faults and cyber-attacks which may have a detrimental socio-economic impact. The frequency of the faults and attacks is increasing every year due to the extreme weather events and strong reliance on the open internet architecture that is vulnerable to cyber-attacks. In May 2021, for instance, Colonial Pipeline, one of the largest pipeline operators in the U.S., transports refined gasoline and jet fuel from Texas up the East Coast to New York was forced to shut down after being attacked by ransomware, causing prices to rise at gasoline pumps across the country. Enhancing situational awareness within the grid can alleviate these risks and avoid their adverse consequences. As part of this process, the phasor measurement units (PMU) are among the suitable assets since they collect time-synchronized measurements of grid status (30-120 samples/s), enabling the operators to react rapidly to potential anomalies. However, it is still challenging to process and analyze the open-ended source of PMU data as there are more than 2500 PMU distributed across the U.S. and Canada, where each of which generates more than 1.5 TB/month of streamed data. Further, the offline machine learning algorithms cannot be used in this scenario, as they require loading and scanning the entire dataset before processing. The ultimate objective of this dissertation is to develop early detection of cyber and physical anomalies in a real-time streaming environment setting by mining multi-variate large-scale synchrophasor data. To accomplish this objective, we start by investigating the cyber and physical anomalies, analyzing their impact, and critically reviewing the current detection approaches. Then, multiple machine learning models were designed to identify physical and cyber anomalies; the first one is an artificial neural network-based approach for detecting the False Data Injection (FDI) attack. This attack was specifically selected as it poses a serious risk to the integrity and availability of the grid; Secondly, we extend this approach by developing a Random Forest Regressor-based model which not only detects anomalies, but also identifies their location and duration; Lastly, we develop a real-time hoeffding tree-based model for detecting anomalies in steaming networks, and explicitly handling concept drifts. These models have been tested and the experimental results confirmed their superiority over the state-of-the-art models in terms of detection accuracy, false-positive rate, and processing time, making them potential candidates for strengthening the grid\u27s security

Deep Learning Applied to PMU Data in Power Systems

With the advent of Wide Area Measurement Systems and the consequent proliferation of digital measurement devices such as PMUs, control centers are being flooded with growing amounts of data. Therefore, operators are craving for efficient techniques to digest the incoming data, enhancing grid operations by making use of knowledge extraction. Driven by the volumes of data involved, innovative methods in the field of Artificial Intelligence are emerging for harnessing information without declaring complex analytical models. In fact, learning to recognize patterns seems to be the answer to overcome the challenges imposed by processing the huge volumes of raw data involved in PMU-based WAMS. Hence, Deep Learning Frameworks are applied as computational learning techniques so as to extract features from electrical frequency records collected by the Brazillian Medfasee BT Project. More specifically, the work developed proposes a classifier of dynamic events such as generation loss, load shedding, etc., based on frequency change