Monitoring, Control, and Protection

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    Integrated Centralized Substation Protection
    ( 2018-01-03) Meliopoulos, A P ; Cokkinides, George ; Albinali, Hussain ; Myrda, Paul ; Farantatos, Evangelos
    Substation cyber assets are mission critical for protection and control of substations. Managing and ensuring their secure operation is of paramount importance. A known vulnerability is hidden failures which are responsible for about 10% of mis-operations and their detrimental effects on system reliability. The paper presents an integrated centralized substation protection approach that is based on the recently developed setting-less relays which are integrated into a centralized substation protection scheme with the following features: (a) fast, dependable and secure protection of each substation protection zone by a settingless relay, (b) supervision of each settingless relay by validating relay input data by a substation wide state estimator, (c) self-healing against hidden failures by detecting and identifying compromised data and replacing them with estimated values, thus ensuring that the settingless relays will always operate on validated data. The paper provides a summary review of the settingless protective relay and introduces the Integrated Centralized Substation Protection Scheme (ICSP) which uses the data from all settingless relays in the substation to perform a substation wide state estimation. The state estimator uses a hypothesis testing algorithm to determine whether (a) data are valid with no faults or hidden failures, (b) data are valid and a fault exists in the system, or (c) some data are invalid due to hidden failures. In the last case, the state estimator uses the substation state and model to replace the compromised data with estimated values and thus enabling self-immunization against hidden failures. A byproduct of the method is the substation state estimate which is transmitted to the control center where it is used with the state from all substations to synthesize the system wide state estimate and model. Architectural issues are addressed as well as migration issues of existing systems into the proposed ICSP.
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    Learning Schemes for Power System Protection
    ( 2018-01-03) Lassetter, Carter ; Cotilla-Sanchez, Eduardo ; Kim, Jinsub
    In this paper, learning algorithms are leveraged to advance power system protection. Advancements in power system protection have come in different forms such as the development of new control strategies and the introduction of a new system architecture such as a microgrid. In this paper, we propose two learning schemes to make accurate predictions and optimal decisions related to power system protection and microgrid control. First, we present a neural network approach to learn a classifier that can predict stable reconnection timings for an islanded sub-network. Second, we present a learning-based control scheme for power system protection based on the policy rollout. In the proposed scheme, we incorporate online simulation using the commercial PSS/e simulator. Optimal decisions are obtained in real time to prevent cascading failures as well as maximize the load served. We validate our methods with the dynamics simulator and test cases RTS-96 and Poland.
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    Redefining Requirements of Ancillary Services for Technology Agnostic Sources
    ( 2018-01-03) Bondy, Daniel Esteban Morales ; MacDonald, Jason ; Kara, Emre Can ; Gehrke, Oliver ; Heussen, Kai ; Chassin, David ; Kiliccote, Sila ; Bindner, Henrik W.
    New sources for ancillary services are needed, yet the requirements for service provision in most countries are explicitly formulated for traditional generators. This leads to waste of the potential for new technologies to deliver ancillary services. In order to harness this potential, we propose to parameterize the requirements of ancillary services so that reserves can be built by combining the advantageous properties of different technologies. The proposal is exemplified through a laboratory test where it shown that the system needs can be covered through cheaper and smaller reserves.
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    Locational Accuracy of VIP Indices for Voltage Collapse Margin Estimation
    ( 2018-01-03) Begovic, Miroslav ; Peerzada, Aaqib ; Nuqui, Reynaldo ; Picone, Benjamin
    Nearly two decades of work on VIP (Voltage Instability Prediction) has enhanced the ability to obtain information on system vulnerability to voltage collapse based on minimum local information. Several indices have been developed over the past 20 years for local monitoring of voltage collapse problems, each with a different level of complexity with respect to computational and communicational infrastructure needed. This article addresses the disparity found in the VIP-derived margins and attempts to study the allocation of critical (more accurate) VIP locations across a power network during system changes. Additionally, a sensitivity metric is proposed to track the accurate VIP locations in real-time under increased system loading which could also lead to a meaningful data fusion of the more accurate VIP-derived margins.
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    Novel Optimization-Based Algorithms for a Substation Voltage Controller Using Local PMU Measurements
    ( 2018-01-03) Amelian, Mohammad ; Badayos, Noah ; Venkatasubramanian, Vaithianathan ; Habibi-Ashrafi, Farrokh ; Salazar, Armando ; Abu-Jaradeh, Backer
    This paper presents an improved version of a local voltage controller for a transmission substation. The controller uses available phasor measurement units (PMUs) at the substation, for optimal management of its local reactive (VAr) control resources, such as shunt reactive devices and transformer taps. Two optimization formulations with different objectives are introduced based on various operating criteria in electric utilities. The first approach aims to minimize the required reactive power injection such that it corrects the substation bus voltages to be within pre-specified limits so as to be close as possible to the optimal values. The second one minimizes the number of switching actions that are needed to correct the voltages to be within limits. Genetic algorithm (GA) is used for solving these discrete optimization problems. Performance of the proposed formulations is tested and analyzed through simulations for a typical substation in Southern California transmission network. Finally, the results from the two approaches are compared and discussed.