A Software-based Low-Jitter Servo Clock for Inexpensive Phasor Measurement Units

This paper presents the design and the implementation of a servo-clock (SC) for low-cost Phasor Measurement Units (PMUs). The SC relies on a classic Proportional Integral (PI) controller, which has been properly tuned to minimize the synchronization error due to the local oscillator triggering the on-board timer. The SC has been implemented into a PMU prototype developed within the OpenPMU project using a BeagleBone Black (BBB) board. The distinctive feature of the proposed solution is its ability to track an input Pulse-Per-Second (PPS) reference with good long-term stability and with no need for specific on-board synchronization circuitry. Indeed, the SC implementation relies only on one co-processor for real-time application and requires just an input PPS signal that could be distributed from a single substation clock

A Method for the Measurement of Digitizers’ Absolute Phase Error

A lot of engineering applications, from telecommunications to power systems, require accurate measurement of phase angles. Some of them, like synchrophasor measurement and calibration of instrument transformers with digital output, in order to reach high phase measurement accuracy, require the knowledge of phase error of digitizers. Therefore, in this paper a method for the measurement of digitizers’ absolute phase errors is proposed. It adopts a sinewave and two square waves, that are the digitizer sample clock and a phase reference signal. Combining the measurements of the relative phase differences between the adopted signals it is possible to accurately evaluate the absolute phase error of a digitize

Synchronized Measurement System for Railway Application

In the light of the recent European directives that regulate railway networks in EU, in order to implement the monitoring and controlling of the railways power supply network, an accurate and reliable knowledge of the exchanged energy between the train and the railway grid is an essential task. Therefore, a measurement system for railway applications must accurately evaluate energy and power quality. In order to do this, the synchronization to a common time reference of all the measurement devices of the network is mandatory. In this paper, a flexible measurement instrument for analysing different types of signals that could be found in railway systems is presented. The proposed instrument has extreme flexibility about the nature of input signals and it implements a synchronization technique to the absolute time via Global Positioning System (GPS). The implementation of the measurement system, along with evaluation of synchronization accuracy, is discussed

Fast Iterative-Interpolated DFT Phasor Estimator Considering Out-of-band Interference

For interpolated discrete Fourier transform (IpDFT)-based phasor estimators, the out-of-band interference (OOBI) test is among the most challenging ones. The typical iterative-interpolated DFT (i-IpDFT) phasor estimator utilizes a two-step iterative framework to eliminate the effects of the negative frequency and OOBI. However, the speed of estimation is limited by the adopted frequency estimator and the redundant iterations. To this end, this article proposes a fast i-IpDFT (FiIpDFT) method for the phasor estimation of an OOBI contaminated signal, which utilizes the three-point IpDFT (I3pDFT) technique. The proposed method first applies a noniterative frequency, amplitude, and phase estimator to eliminate the negative frequency interference. Then, a straightforward formula and two-stop criterion are introduced to reduce the computational burden of the OOBI elimination process. The accuracy and effectiveness of the proposed FiIpDFT method are validated by simulations. These are designed, under steady and dynamic conditions, according to the requirements of the Standard IEC/IEEE 60255-118-1

A Set-up for Static and Dynamic Characterization of Voltage and Current Transducers used in Railway Application

In the recent years, much more attention is paid to energy and power quality measurement in railway system. In both the case, the voltage and current transducers play a crucial role and their accuracy could determine the performance level of whole measurement system. To obtain reliable results, the accuracy of transducers should be tested with waveforms as close as possible to real working conditions. To assess the metrological characteristic of DC voltage and current transducers under real operating conditions, this paper presents a calibration set up able to generate up to 6 kV for DC voltage and up to 300 A for DC current. The system is able to generate complex and non-stationary test signals which go beyond the standard characterization procedures. Dynamic tests can be derived from real signals obtained from experimental data. For this aim, a specific software tool was developed and here it is presented

Towards Automated Machine Learning on Imperfect Data for Situational Awareness in Power System

The increasing penetration of renewable energy sources (such as solar and wind) and incoming widespread electric vehicles charging introduce new challenges in the power system. Due to the variability and uncertainty of these sources, reliable and cost-effective operations of the power system rely on high level of situational awareness. Thanks to the wide deployment of sensors (e.g., phasor measurement units (PMUs) and smart meters) and the emerging smart Internet of Things (IoT) sensing devices in the electric grid, large amounts of data are being collected, which provide golden opportunities to achieve high level of situational awareness for reliable and cost-effective grid operations.To better utilize the data, this dissertation aims to develop Machine Learning (ML) methods and provide fundamental understanding and systematic exploitation of ML for situational awareness using large amounts of imperfect data collected in power systems, in order to improve the reliability and resilience of power systems.However, building excellent ML models needs clean, accurate and sufficient training data. The data collected from the real-world power system is of low quality. For example, the data collected from wind farms contains a mixture of ramp and non-ramp as well as the mingle of heterogeneous dynamics data; the data in the transmission grid contains noisy, missing, insufficient and inaccurate timestamp data. Employing ML without considering these distinct features in real-world applications cannot build good ML models. This dissertation aims to address these challenges in two applications, wind generation forecast and power system event classification, by developing ML models in an automated way with less efforts from domain experts, as the cost of processing such large amounts of imperfect data by experts can be prohibitive in practice.First, we take heterogeneous dynamics into consideration, especially for ramp events. A Drifting Streaming Peaks-over-Threshold (DSPOT) enhanced self-evolving neural networks-based short-term wind farm generation forecast is proposed by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events, based on which different neural networks are trained to learn different dynamics of wind farm generation. As the efficacy of the neural networks relies on the quality of training datasets (i.e., the classification accuracy of ramp and non-ramp events), a Bayesian optimization based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. Next, we address the challenges of event classification due to the low-quality PMU measurements and event logs. A novel machine learning framework is proposed for robust event classification, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Moreover, with the small number of good features, we need much less training data to train a good event classifier, which can address the challenge of insufficient and imbalanced training data, and the training time is negligible compared to neural network based approaches. Based on the proposed framework, we developed a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data. Subsequently, we address the challenge of insufficient training labels. The real-world PMU data is often incomplete and noisy, which can significantly reduce the efficacy of existing machine learning techniques that require high-quality labeled training data. To obtain high-quality event logs for large amounts of PMU measurements, it requires significant efforts from domain experts to maintain the event logs and even hand-label the events, which can be prohibitively costly or impractical in practice. So we develop a weakly supervised machine learning approach that can learn a good event classifier using a few labeled PMU data. The key idea is to learn the labels from unlabeled data using a probabilistic generative model, in order to improve the training of the event classifiers. Experimental results show that even with 95\% of unlabeled data, the average accuracy of the proposed method can still achieve 78.4\%. This provides a promising way for domain experts to maintain the event logs in a less expensive and automated manner. Finally, we conclude the dissertation and discuss future directions

Vulnerability Assessment and Privacy-preserving Computations in Smart Grid

Modern advances in sensor, computing, and communication technologies enable various smart grid applications which highlight the vulnerability that requires novel approaches to the field of cybersecurity. While substantial numbers of technologies have been adopted to protect cyber attacks in smart grid, there lacks a comprehensive review of the implementations, impacts, and solutions of cyber attacks specific to the smart grid.In this dissertation, we are motivated to evaluate the security requirements for the smart grid which include three main properties: confidentiality, integrity, and availability. First, we review the cyber-physical security of the synchrophasor network, which highlights all three aspects of security issues. Taking the synchrophasor network as an example, we give an overview of how to attack a smart grid network. We test three types of attacks and show the impact of each attack consisting of denial-of-service attack, sniffing attack, and false data injection attack.Next, we discuss how to protect against each attack. For protecting availability, we examine possible defense strategies for the associated vulnerabilities.For protecting data integrity, a small-scale prototype of secure synchrophasor network is presented with different cryptosystems. Besides, a deep learning based time-series anomaly detector is proposed to detect injected measurement. Our approach observes both data measurements and network traffic features to jointly learn system states and can detect attacks when state vector estimator fails.For protecting data confidentiality, we propose privacy-preserving algorithms for two important smart grid applications. 1) A distributed privacy-preserving quadratic optimization algorithm to solve Security Constrained Optimal Power Flow (SCOPF) problem. The SCOPF problem is decomposed into small subproblems using the Alternating Direction Method of Multipliers (ADMM) and gradient projection algorithms. 2) We use Paillier cryptosystem to secure the computation of the power system dynamic simulation. The IEEE 3-Machine 9-Bus System is used to implement and demonstrate the proposed scheme. The security and performance analysis of our implementations demonstrate that our algorithms can prevent chosen-ciphertext attacks at a reasonable cost

On Multiple-Resonator-based Implementation of IEC/IEEE Standard P-Class Compliant PMUs

This article deals with the implementation of the P-Class PMU compliant with IEC/IEEE Standard 60255-118-1:2018 by usage of a multiple-resonator (MR)-based approach for harmonic analysis having been proposed recently. In previously published articles, it has been shown that a trade-off between opposite requirements is possible by shifting a measurement time stamp along the filter window. Positioning the time stamp in a proximity of the time window center assures flat-top frequency responses. In this article, through simulation tests carried out under various conditions, it is shown that requirements of the IEC/IEEE Standard 60255-118-1:2018 can be satisfied by the second and third order MR structure for particular conditions of the time stamp location