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At least 577 records · Page 32

Cybersecurity Risk Profiles for Distributed Energy Resource Management Systems

Managing the digitalization of increasingly diversity energy resources is a complex challenge for energy systems planners and managers. As the penetration of solar photovoltaics (PV) and other distributed renewable energy resources (DERs) expands, distributed energy resource management systems (DERMS) will play an increasingly important role in managing, monitoring, and controlling DERs as electric systems before more distributed, interconnected, and networked. However, the cybersecurity implications of DERMS deployments are not well understood today. A lack of understanding around the cybersecurity implications of DERMS deployments and variability in the security posture of DERMS vendors, owners, and operators could introduce new security risks to evolving electric power systems. This paper describes cybersecurity attack scenarios on DERMS, identifies related cybersecurity standards and guidelines, reviews the security features of state-of-the-art DERMS solutions, and offers cybersecurity guidance for DERMS vendors, owners, and operators to protect DERMS' unique capabilities. Standardizing cybersecurity requirements for DERMS could help improve the security of DERMS integrations and improve innovations that are more secure by design. The cybersecurity guidance found in this paper is intended to offer a unified approach and lay the foundation for future standardization of DERMS cybersecurity to reduce risk to the solar industry and other renewable energy stakeholders when integrating these technologies with electric power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Long Duration Exposure Facility - A multipurpose free-flying experiment carrier

The Long Duration Exposure Facility (LDEF) is being developed by NASA to accommodate, using the Space Shuttle, a class of technology, science and applications experiments which require a free-flying exposure in space, but which do not require a sophisticated or dedicated satellite. The facility is a simple reusable structure about 14 ft in diameter and 30 ft long, which is three-axis gravity-gradient stabilized when free flying in space. Various aspects of the LDEF are examined, including the attitude stabilization system, the experiment trays, the experiment exposure control canister,and the environment monitoring system. LDEF mission plans are considered, as is experiment selection - the space debris damage experiment, the thermal coating experiment, the solar cells and arrays experiment, and the active optical components experiment.

Kinard, W. H.↗

An Updated Methodology for Enhancing Risk Monitors with Integrated Equipment Condition Assessment

Small modular reactors (SMRs) generally include reactors with electric output of ~350 MWe or less (this cutoff varies somewhat but is substantially less than full-size plant output of 700 MWe or more). Advanced SMRs (AdvSMRs) refer to a specific class of SMRs and are based on modularization of advanced reactor concepts. Enhancing affordability of AdvSMRs will be critical to ensuring wider deployment, as AdvSMRs suffer from loss of economies of scale inherent in small reactors when compared to large (~greater than 600 MWe output) reactors and the controllable day-to-day costs of AdvSMRs will be dominated by operation and maintenance (O&M) costs. Technologies that help characterize real-time risk are important for controlling O&M costs. Risk monitors are used in current nuclear power plants to provide a point-in-time estimate of the system risk given the current plant configuration (e.g., equipment availability, operational regime, and environmental conditions). However, current risk monitors are unable to support the capability requirements listed above as they do not take into account plant-specific normal, abnormal, and deteriorating states of active components and systems. This report documents technology developments towards enhancing risk monitors that, if integrated with supervisory plant control systems, can provide the capability requirements listed and meet the goals of controlling O&M costs. The report describes research results on augmenting an initial methodology for enhanced risk monitors that integrate real-time information about equipment condition and POF into risk monitors. Methods to propagate uncertainty through the enhanced risk monitor are evaluated. Available data to quantify the level of uncertainty and the POF of key components are examined for their relevance, and a status update of this data evaluation is described. Finally, we describe potential targets for developing new risk metrics that may be useful for studying trade-offs for economic operation while maintaining adequate safety margins.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Work Experience Report

The NASA Platform for Autonomous Systems (NPAS) toolkit is currently being used at the NASA John C. Stennis Space Center (SSC) to develop the INSIGHT program, which will autonomously monitor and control the Nitrogen System of the High Pressure Gas Facility (HPGF) on site. The INSIGHT program is in need of generic timing capabilities in order to perform timing based actions such as pump usage timing and sequence step timing. The purpose of this project was to develop a timing module that could fulfill these requirements and be adaptable for expanded use in the future. The code was written in Gensym G2 software platform, the same as INSIGHT, and was written generically to ensure compatibility with any G2 program. Currently, the module has two timing capabilities, a stopwatch function and a countdown function. Although the module has gone through some functionality testing, actual integration of the module into NPAS and the INSIGHT program is contingent on the module passing later checks.

Guo, Daniel↗

Autonomous control

KSC has been developing the Knowledge-Based Autonomous Test Engineer (KATE), which is a tool for performing automated monitoring, diagnosis, and control of electromechanical devices. KATE employs artificial intelligence computing techniques to perform these functions. The KATE system consists of a generic shell and a knowledge base. The KATE shell is the portion of the system which performs the monitoring, diagnosis, and control functions. It is generic in the sense that it is application independent. This means that the monitoring activity, for instance, will be performed with the same algorithms regardless of the particular physical device being used. The knowledge base is the portion of the system which contains specific functional and behavorial information about the physical device KATE is working with. Work is nearing completion on a project at KSC to interface a Texas Instruments Explorer running a LISP version of KATE with a Generic Checkout System (GCS) test-bed to control a physical simulation of a shuttle tanking system (humorously called the Red Wagon because of its color and mobility). The Autonomous Control System (ACS) project supplements and extends the KATE/GCS project by adding three other major activities. The activities include: porting KATE from the Texas Instruments Explorer machine to an Intel 80386-based UNIX workstation in the LISP language; rewriting KATE as necessary to run on the same 80386 workstation but in the Ada language; and investigating software and techniques to translate ANSI Standard Common LISP to Mil Standard Ada. Primary goals of this task are as follows: (1) establish the advantages of using expert systems to provide intelligent autonomous software for Space Station Freedom applications; (2) determine the feasibility of using Ada as the run-time environment for model-based expert systems; (3) provide insight into the advantages and disadvantagesof using LISP or Ada in the run-time environment for expert systems; and (4) compare the performance of the 80386 processor to a symbolic processor as a delivery vehicle for expert systems.

Brown, Barbara↗

Johnson Space Center's Solar and Wind-Based Renewable Energy System

The NASA Johnson Space Center (JSC) in Houston, Texas has a Sustainability Partnership team that seeks ways for earth-based sustainability practices to also benefit space exploration research. A renewable energy gathering system was installed in 2007 at the JSC Child Care Center (CCC) which also offers a potential test bed for space exploration power generation and remote monitoring and control concepts. The system comprises: 1) several different types of photovoltaic panels (29 kW), 2) two wind-turbines (3.6 kW total), and 3) one roof-mounted solar thermal water heater and tank. A tie to the JSC local electrical grid was provided to accommodate excess power. The total first year electrical energy production was 53 megawatt-hours. A web-based real-time metering system collects and reports system performance and weather data. Improvements in areas of the CCC that were detected during subsequent energy analyses and some concepts for future efforts are also presented.

Vasquez, A.↗

Mars Observer screen display design for a multimission environment

The Multi Mission Control Team (MMCT) is responsible for support to real time operations of the Mars Observer Mission. The team has the responsibility for monitoring the ground data system for the integrity of the telemetry and command data links. It also supports the Mars Observers Spacecraft Team in monitoring spacecraft events. The Data Monitor and Display subsystem (DMD) workstation provides the data interface with the ground data system. DMD workstation displays were developed to support the Mission Controllers in accomplishing their assigned tasks for supporting the Mars Observer mission. The display design concepts that were used in the Mar Observer MMCT displays to minimize the cognitive demands on the controllers and enhance the MMCT operations were presented. The Data Monitor and Display subsystem (DMD) is the controllers window into the spacecraft and the ground data system. The DMD is a workstation that provides a variety of formatted data displays to the controller. The displays present both spacecraft telemetry data and ground system monitor data. Some displays are preplanned and developed prior to the operations in which they are used. These are called fixed displays and are quite versatile in format and content. Other displays and plots can be created in real time. These displays have limited formats but flexibility in content. These are called list or message displays. They can be rapidly generated by the controller as needed. The MMCT display repertoire provides a mix of displays appropriate to the needs of the MMCT controllers.

Chafin, Roy L.↗

Flexible Microsensor Array for the Root Zone Monitoring of Porous Tube Plant Growth System

Control of oxygen and water in the root zone is vital to support plant growth in the microgravity environment. The ability to control these sometimes opposing parameters in the root zone is dependent upon the availability of sensors to detect these elements and provide feedback for control systems. In the present study we demonstrate the feasibility of using microsensor arrays on a flexible substrate for dissolved oxygen detection, and a 4-point impedance microprobe for surface wetness detection on the surface of a porous tube (PT) nutrient delivery system. The oxygen microsensor reported surface oxygen concentrations that correlated with the oxygen concentrations of the solution inside the PT when operated at positive pressures. At negative pressures the microsensor shows convergence to zero saturation (2.2 micro mol/L) values due to inadequate water film formation on porous tube surface. The 4-point microprobe is useful as a wetness detector as it provides a clear differentiation between dry and wet surfaces. The unique features of the dissolved oxygen microsensor array and 4-point microprobe include small and simple design, flexibility and multipoint sensing. The demonstrated technology is anticipated to provide low cost, and highly reliable sensor feedback monitoring plant growth nutrient delivery system in both terrestrial and microgravity environments.

Sathyan, Sandeep↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

A demonstration of an intelligent control system for a reusable rocket engine

An Intelligent Control System for reusable rocket engines is under development at NASA Lewis Research Center. The primary objective is to extend the useful life of a reusable rocket propulsion system while minimizing between flight maintenance and maximizing engine life and performance through improved control and monitoring algorithms and additional sensing and actuation. This paper describes current progress towards proof-of-concept of an Intelligent Control System for the Space Shuttle Main Engine. A subset of identifiable and accommodatable engine failure modes is selected for preliminary demonstration. Failure models are developed retaining only first order effects and included in a simplified nonlinear simulation of the rocket engine for analysis under closed loop control. The engine level coordinator acts as an interface between the diagnostic and control systems, and translates thrust and mixture ratio commands dictated by mission requirements, and engine status (health) into engine operational strategies carried out by a multivariable control. Control reconfiguration achieves fault tolerance if the nominal (healthy engine) control cannot. Each of the aforementioned functionalities is discussed in the context of an example to illustrate the operation of the system in the context of a representative failure. A graphical user interface allows the researcher to monitor the Intelligent Control System and engine performance under various failure modes selected for demonstration.

Musgrave, Jeffrey L.↗

Visualization and Analytics of Distribution Systems with Deep Penetration of Distributed Energy Resources (VADER)

In its current state, the distribution system is incapable of handling small to moderate amounts of photovoltaic (PV) penetration. This is because it was initially designed for handling passive loads which, at the level of a substation, have low variability and are forecastable with high accuracy. It has been an open loop system with little monitoring and control. With the addition of PV energy sources, the overall scenario will change dramatically due to (1) two way power flow on network; and (2) high aggregate variability. Additionally, changes on the consumption side lead to a number of smart loads, Electric Vehicles (EVs), and Demand Response. These fundamental changes in the characteristics of the generation and consumption of power will lead to a number of practical engineering problems which must be overcome to allow increased penetration of Distributed PV. Solving the unique engineering challenges which arise at moderate levels of PV penetration requires closed loop integration of data from (1) PV sources; (2) customer load data from smart meters; (3) EV charging data; and (4) local and line mounted precision instruments. These data are not traditionally used by utilities in operations since they are ”non-SCADA” and the current grid does not require such levels of control. To integrate this data and provide real time intelligence from these non-SCADA data, we created the Visualization and Analytics of Distribution Systems with Deep Penetration of Distributed Energy Resources (VADER) platform. VADER is a collection of analytics enabled by integration of massive and heterogeneous data streams for granular real-time monitoring, visualization and control of Distributed Energy Resources (DER) in distribution networks. VADER analytics enable utilities to have greater visibility into distributed energy resources. We built several batch- and stream-analytics in VADER which help operators better understand the impact of distributed energy resources on the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Python-EPICS RF Conditioning Automatic Control System at the Spallation Neutron Source

The RF Test Facility (RFTF) at the Spallation Neutron Source (SNS) is used for the conditioning of RF compo-nents such as ceramic vacuum windows and power cou-plers prior to their installation in the H- ion linear accel-erator. This process exposes components to high-power RF fields and thermal cycling to improve performance and remove surface impurities. To automate and optimize this process, a Python-based EPICS control system was developed alongside targeted hardware upgrades. The system enables real-time monitoring and control of RF power levels, temperature, and vacuum pressure. A user-friendly graphical interface was implemented using CS-Studio (Phoebus), allowing operators to adjust parameters and collect data efficiently. The system integrates a High-Power Protection Module (HPM) for interlocks based on vacuum and arc detection, ensuring safe operation. These upgrades have significantly improved the efficiency, accuracy, and safety of RF conditioning at the SNS RFTF. This paper describes the updated RF conditioning sys-tem, highlighting the software and hardware develop-ments and their application in support of the Proton Pow-er Upgrade (PPU) project.

Lee, Sung-Woo [ORNL] (ORCID:000000030915835X)↗

Field Test of Boiler Primary Loop Temperature Controller

Beyond these initial system efficiency upgrades are an emerging class of Advanced Load Monitoring (ALM) aftermarket controllers that dynamically respond to the boiler load, with claims of 10% to 30% of fuel savings over a heating season. For hydronic boilers specifically, these devices perform load monitoring, with continuous measurement of supply and in some cases return water temperatures. Energy savings from these ALM controllers are derived from dynamic management of the boiler differential, where a microprocessor with memory of past boiler cycles prevents the boiler from firing for a period of time, to limit cycling losses and inefficient operation during perceived low load conditions. These differ from OTR controllers, which vary boiler setpoint temperatures with ambient conditions while maintaining a fixed differential. PARR installed and monitored the performance of one type of ALM controller, the M2G from Greffen Systems, at multifamily sites in the city of Chicago and its suburb Cary, IL, both with existing OTR control. Results show that energy savings depend on the degree to which boilers are over-sized for their load, represented by cycling rates. Also savings vary over the heating season with cycling rates, with greater savings observed in shoulder months. Over the monitoring period, over-sized boilers at one site showed reductions in cycling and energy consumption in line with prior laboratory studies, while less over-sized boilers at another site showed muted savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

MIUS Integration and Subsystem Test (MIST) data system

A data system for use in testing integrated subsystems of a modular integrated utility system (MIUS) is presented. The MIUS integration and subsystem test (MIST) data system is reviewed from its conception through its checkout and operation as the controlling portion of the MIST facility. The MIST data system provides a real time monitoring and control function that allows for complete evaluation of the performance of the mechanical and electrical subsystems, as well as controls the operation of the various components of the system. In addition to the aforementioned capabilities, the MIST data system provides computerized control of test operations such that minimum manpower is necessary to set up, operate, and shut down subsystems during test periods.

Pringle, L. M.↗

A Transparent Translation from Legacy System Model into Common Information Model

Advances in the smart grid are forcing utilities toward better monitoring, control, and analysis of distribution systems, and require extensive cyber-based, intelligent systems and applications to realize various functionalities. The ability of systems, or components within systems, to interact and exchange services or information with each other is the key to the success of smart grid technologies, and it requires efficient information exchanging and data sharing infrastructure. The Common Information Model (CIM) is a standard that allows different applications to exchange information about an electrical system, and it has become a widely accepted solution for information exchange among different platforms and applications. However, most existing legacy systems were developed using their own languages rather than CIM. Integrating such legacy systems is a challenge for utilities, and the appropriate use of the integrated legacy systems is even more intricate. Thus, this paper describes an approach and open-source tool developed to translate legacy system models into CIM format. The developed tool was tested for a commercial distribution management system and simulation results have proved its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A Transparent Translation from Legacy System Model into Common Information Model: Preprint

Advance in smart grid is forcing utilities towards better monitoring, control and analysis of distribution systems, and requires extensive cyber-based intelligent systems and applications to realize various functionalities. The ability of systems, or components within systems, to interact and exchange services or information with each other is the key to the success of smart grid technologies, and it requires efficient information exchanging and data sharing infrastructure. The Common Information Model (CIM) is a standard that allows different applications to exchange information about an electrical system, and it has become a widely accepted solution for information exchange among different platforms and applications. However, most existing legacy systems are not developed using CIM, but using their own languages. Integrating such legacy systems is a challenge for utilities, and the appropriate utilization of the integrated legacy systems is even more intricate. Thus, this paper has developed an approach and open-source tool in order to translate legacy system models into CIM format. The developed tool is tested for a commercial distribution management system and simulation results have proved its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗