Data-Driven Flow and Injection Estimation in PMU-Unobservable Transmission Systems

Fast and accurate knowledge of power flows and power injections is needed for a variety of applications in the electric grid. Phasor measurement units (PMUs) can be used to directly compute them at high speeds; however, a large number of PMUs will be needed for computing all the flows and injections. Similarly, if they are calculated from the outputs of a linear state estimator, then their accuracy will deteriorate due to the quadratic relationship between voltage and power. This paper employs machine learning to perform fast and accurate flow and injection estimation in power systems that are sparsely observed by PMUs. We train a deep neural network (DNN) to learn the mapping function between PMU measurements and power flows/injections. The relation between power flows and injections is incorporated into the DNN by adding a linear constraint to its loss function. The results obtained using the IEEE 118-bus system indicate that the proposed approach performs more accurate flow/injection estimation in severely unobservable power systems compared to other data-driven methods.Comment: 5 pages, 1 figur

Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes

Recently, there has been a major emphasis on developing data-driven approaches involving machine learning (ML) for high-speed static state estimation (SE) in power systems. The emphasis stems from the ability of ML to overcome difficulties associated with model-based approaches, such as the handling of non-Gaussian measurement noise. However, topology changes pose a stiff challenge for performing ML-based SE because the training and test environments become different when such changes occur. This paper overcomes this challenge by formulating a graph neural network-based time-synchronized state estimator that considers the physical connections of the power system during the training itself. The superiority of the proposed approach over the model-based linear state estimator in the presence of non-Gaussian measurement noise and a regular deep neural network-based state estimator in the presence of topology changes is demonstrated for the IEEE 118-bus system.Comment: 5 pages, 2 figure

Application of GMR Sensors to Non-Contact Current Monitoring, Fault Detection and Classification in Electricity Distribution Networks

 A reliable uninterrupted electricity supply is essential to a functioning society and a growing economy. Traditionally, low voltage distribution networks (LVDN) are less well monitored, despite being more extensive. The reliability, cost, and compatibility of sensors are the primary constraints to implementing a large-scale monitoring system at LVDN. Faults at this level have the potential to damage public property and cause fatal accidents, but their detection is complicated by the characteristics of some faults. Therefore, this thesis’s overall goal is to evaluate the feasibility of non-contact giant magneto resistive (GMR) sensors for LVDN overhead line monitoring. Two applications were evaluated: 1) non-contact current sensing and 2) fault detection and classification. A review of low and high impedance faults, their characteristics, and the existing detection methods indicates that fault detection schemes and characterization have mainly focused on the high voltage levels. In this thesis, a novel 400-volt (3φ-neutral) physical test facility is developed with proper personnel safety and system security following the industry guidelines, to generate fault and system event data for analysis. Cross-coupled alternating current (AC) magnetic fields from the overhead line arrangement were captured via a GMR sensor. Three typical off the shelf single-axis GMR sensors were used to develop a 3-axis GMR sensor head to capture the spatial magnetic fields. The sensors were individually calibrated with respect to one single true calibrated sensor. This thesis describes the procedure and for achieving the correct alignment of the sensor output with the AC phase direction. A method for fault detection and classification using GMR sensor measurements is also presented in this thesis. This was developed using deep learning algorithms. Two supervised deep learning algorithms were evaluated to classify the fault and system event signatures from the magnetic field measurements. The analysis shows a hybrid model designed with a convolution neural network and gate recurrent unit performed best. This followed by a proposed decision framework is shown to detect and classify faults and normal system events with high security and reliability. In addition to the detection of faults, this thesis also confirms the feasibility of using non-contact calibrated GMR sensors for overhead line current sensing. The analysis shows a minimum of two vertically placed 2D sensor heads are required to calculate the individual phase current data from magnetic field measurements. Compared to measured data, this calculation was found to be more than 90% accurate. In summary, this thesis presents the differentiating characteristics of faults and system events observed in experiments in a purpose-built 400-volt network simulation facility. Non-contact GMR sensors are shown to be an alternative to traditional current sensors for LVDN current sensing, and for fault detection and classification via deep learning (DL)-based methods. </p

Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models

Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation

Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models

Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation

Characterization of 400 Volt High Impedance Fault with Current and Magnetic Field Measurements

Electrical faults, which can occur at all voltage levels in an electricity supply system, are a health and safety risk. Multi-branch distribution networks represent a significant ongoing challenge for fault detection, with the greatest challenge being high impedance fault (HIF) detection. To date, research has focused at higher voltage levels and fault monitoring sensors have traditionally only been installed in limited locations within the higher voltage networks. The main contributions of this paper are to characterize a high impedance fault (HIF) involving a tree branch and to experimentally verify the feasibility of giant magneto-resistive (GMR) sensors, located distant from the overhead lines, for fault detection. In a purpose-built 400 V physical simulation test facility, we have collected current and magnetic field data during HIF involving a tree branch. We have identified new characteristics in the early stages of this fault type, which persist for a reasonable length of time but are only observable when suitable signal processing techniques are applied. New detection schemes will, therefore, need to be developed to detect such faults. GMR sensors were found to be suitable for observing the characteristics of HIF, validating their potential use for fault detection. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works