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Olama, Mohammed M

Publications and source records attributed to Olama, Mohammed M.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Inertia Estimation Under High Penetration of Inverter-based Resources

Many types of renewable energy sources, especially solar and wind generation, are connected to the electric grid through power-electronic-based interfaces (inverters). These inverter-based resources (IBRs) are mechanically decoupled from the grid, which reduces system inertia and thus may compromise its stability and reliability. In this study, we examine the impact of high penetration of IBRs on the power grid’s inertia. To achieve this, we intentionally introduce disturbances into a simulation case study, such as a step load change, to observe and record the system’s frequency responses. This study offers a nuanced understanding of how the integration of IBRs affects grid stability, and it provides essential guidance for future grid management and resilience strategies.

Yadav, Ajay↗
DOE OSTI2024

Measurement-Based Approach for Inertia-Trend Analysis of the US Western Interconnection

Rising deployment of inverter-based resources (IBRs), characterized by a lack of rotating mass, is decreasing the total inertia of the system. This can lead to an increased Rate of Change of Frequency (RoCoF) during the disturbance and false activation of protective devices. There is a need to assess the inertia over the past decade amidst the evolving landscape of renewable energy sources to develop strategies for integrating energy storage, enhancing resilience measures, and ensuring the stable and reliable operation of the grid. Therefore, a realistic assessment of the inertia trend using a measurement-based approach that addresses the limitations of existing models is proposed. An inertia study of the Western Interconnection in the United States is performed utilizing the data from 2013 to 2022, obtained from FNET/ GridEye network. The three-second RoCoF time window is chosen for the study as it showed an optimum balance between a strong correlation with the power imbalance (ΔP) and minimum inclusion of primary response from governor. The obtained inertia trend result shows a small percentage declination of inertia over the decade. By examining the result alongside a generation mix graph, insights are gained into the dynamic interplay between shifting energy landscape and system inertia.

Dulal, Saurav↗
DOE OSTI2024

Model-Free Control of Indoor Temperatures in Residential Buildings: Convergence Analysis

Model-free control (MFC) has recently been applied in multiple fields, including indoor air temperature regulation in buildings. It is a data-enabled feedback tracking control strategy for complex systems using a simplified representation of the ultra-local approximation model through the unique information of input-output behavior. MFC is a prevailing control strategy for systems with unknown or poorly known system dynamic models. Thus, it is a relatively simple, but efficient, trajectory tracking controller. In this paper, we investigate the convergence rate of MFC when applied to controlling indoor temperatures in residential buildings subject to outdoor weather disturbances. Numerical results show that MFC is quite robust to external disturbances, and its rate of convergence is almost one, i.e., it converges Q-sublinearly (slower than linearly).

Wu, Tumin↗
DOE OSTI2017

Statistical Analysis of Solar PV Power Frequency Spectrum for Optimal Employment of Building Loads

In this paper, a statistical analysis of the frequency spectrum of solar photovoltaic (PV) power output is conducted. This analysis quantifies the frequency content that can be used for purposes such as developing optimal employment of building loads and distributed energy resources. One year of solar PV power output data was collected and analyzed using one-second resolution to find ideal bounds and levels for the different frequency components. The annual, seasonal, and monthly statistics of the PV frequency content are computed and illustrated in boxplot format. To examine the compatibility of building loads for PV consumption, a spectral analysis of building loads such as Heating, Ventilation and Air-Conditioning (HVAC) units and water heaters was performed. This defined the bandwidth over which these devices can operate. Results show that nearly all of the PV output (about 98%) is contained within frequencies lower than 1 mHz (equivalent to ~15 min), which is compatible for consumption with local building loads such as HVAC units and water heaters. Medium frequencies in the range of ~15 min to ~1 min are likely to be suitable for consumption by fan equipment of variable air volume HVAC systems that have time constants in the range of few seconds to few minutes. This study indicates that most of the PV generation can be consumed by building loads with the help of proper control strategies, thereby reducing impact on the grid and the size of storage systems.

boxplot↗
DOE OSTI2016

SUPERVISORY CONTROL FOR PEAK REDUCTION IN COMMERCIAL BUILDINGS WHILE MAINTAINING COMFORT

This paper describes a supervisory control strategy for limiting peak power demand by small and medium commercial buildings while still meeting the business needs of the occupants. This control strategy has two features that make it relevant to new and existing buildings. First, it is designed to operate with building equipment, such as air conditioning and refrigeration systems, as they are presently installed in most small and medium commercial buildings. Because of this, the supervisory control could be realized as a software-only retrofit to existing building management systems. Second, the proposed control acts as a supervisory management layer over existing control systems, rather than replacing them outright. The primary idea of this approach is that the controls for individual building equipment request energy resources for a control action and the supervisory control examines the requests and decides which control actions to allow while satisfying a limit on peak power demand.

Nutaro, James J↗
DOE OSTI2015

An Integrated Signaling-Encryption Mechanism to Reduce Error Propagation in Wireless Communications: Performance Analyses

Traditional encryption techniques require packet overhead, produce processing time delay, and suffer from severe quality of service deterioration due to fades and interference in wireless channels. These issues reduce the effective transmission data rate (throughput) considerably in wireless communications, where data rate with limited bandwidth is the main constraint. In this paper, performance evaluation analyses are conducted for an integrated signaling-encryption mechanism that is secure and enables improved throughput and probability of bit-error in wireless channels. This mechanism eliminates the drawbacks stated herein by encrypting only a small portion of an entire transmitted frame, while the rest is not subject to traditional encryption but goes through a signaling process (designed transformation) with the plaintext of the portion selected for encryption. We also propose to incorporate error correction coding solely on the small encrypted portion of the data to drastically improve the overall bit-error rate performance while not noticeably increasing the required bit-rate. We focus on validating the signaling-encryption mechanism utilizing Hamming and convolutional error correction coding by conducting an end-to-end system-level simulation-based study. The average probability of bit-error and throughput of the encryption mechanism are evaluated over standard Gaussian and Rayleigh fading-type channels and compared to the ones of the conventional advanced encryption standard (AES).

advanced encryption standard (AES)↗
DOE OSTI2014

Stochastic Modeling and Power Control of Time-Varying Wireless Communication Networks

Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) that varies from one observation instant to the next. This paper is concerned with dynamical modeling of time-varying wireless fading channels, their estimation and parameter identification, and optimal power control from received signal measurement data. The wireless channel is characterized using a stochastic state-space form and derived by approximating the time-varying DPSD of the channel. The expected maximization and Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Moreover, we investigate a centralized optimal power control algorithm based on predictable strategies and employing the estimated channel parameters and states. The proposed models together with the estimation and power control algorithms are tested using experimental measurement data and the results are presented.

Doppler Power Spectral Density↗
DOE OSTI2014

Predicting Student Success using Analytics in Course Learning Management Systems

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems, called Moodle. First, we have identified the data features useful for predicting student outcomes such as students scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.

Educational data mining↗
DOE OSTI2014

A Qualitative Readiness-Requirements Assessment Model for Enterprise Big-Data Infrastructure Investment

In the last three decades, there has been an exponential growth in the area of information technology providing the information processing needs of data-driven businesses in government, science, and private industry in the form of capturing, staging, integrating, conveying, analyzing, and transferring data that will help knowledge workers and decision makers make sound business decisions. Data integration across enterprise warehouses is one of the most challenging steps in the big data analytics strategy. Several levels of data integration have been identified across enterprise warehouses: data accessibility, common data platform, and consolidated data model. Each level of integration has its own set of complexities that requires a certain amount of time, budget, and resources to implement. Such levels of integration are designed to address the technical challenges inherent in consolidating the disparate data sources. In this paper, we present a methodology based on industry best practices to measure the readiness of an organization and its data sets against the different levels of data integration. We introduce a new Integration Level Model (ILM) tool, which is used for quantifying an organization and data system s readiness to share data at a certain level of data integration. It is based largely on the established and accepted framework provided in the Data Management Association (DAMA-DMBOK). It comprises several key data management functions and supporting activities, together with several environmental elements that describe and apply to each function. The proposed model scores the maturity of a system s data governance processes and provides a pragmatic methodology for evaluating integration risks. The higher the computed scores, the better managed the source data system and the greater the likelihood that the data system can be brought in at a higher level of integration.

Big data↗
DOE OSTI2014

Towards Adaptive Educational Assessments: Predicting Student Performance using Temporal Stability and Data Analytics in Learning Management Systems

Data-driven assessments and adaptive feedback are becoming a cornerstone research in educational data analytics and involve developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the students and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present our efforts in using data analytics that enable educationists to design novel data-driven assessment and feedback mechanisms. In order to achieve this objective, we investigate temporal stability of students grades and perform predictive analytics on academic data collected from 2009 through 2013 in one of the most commonly used learning management systems, called Moodle. First, we have identified the data features useful for assessments and predicting student outcomes such as students scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total Grade Point Average(GPA) at the same term they enrolled in the course. Second, time series models in both frequency and time domains are applied to characterize the progression as well as overall projections of the grades. In particular, the model analyzed the stability as well as fluctuation of grades among students during the collegiate years (from freshman to senior) and disciplines. Third, Logistic Regression and Neural Network predictive models are used to identify students as early as possible who are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. The time series analysis indicates that assessments and continuous feedback are critical for freshman and sophomores (even with easy courses) than for seniors, and those assessments may be provided using the predictive models. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy. Our results show that there are strong ties associated with the first few weeks for coursework and they have an impact on the design and distribution of individual modules.

adaptive learning↗
DOE OSTI2014

Design, Implementation, and Evaluation of a Hybrid DS/FFH Spread-Spectrum Radio Transceiver

In recent years there has been great interest in using hybrid spread-spectrum (HSS) techniques for commercial applications, particularly in the Smart Grid, in addition to their inherent uses in military communications. This is because HSS can accommodate high data rates with high link integrity, even in the presence of significant multipath effects and interfering signals. A highly useful form of this transmission technique for many types of command, control, and sensing applications is the specific code-related combination of standard direct-sequence modulation with "fast" frequency-hopping, denoted hybrid DS/FFH, wherein multiple frequency hops occur within a single data-bit time. In this paper, we present the efforts carried out at Oak Ridge National Laboratory toward exploring the design, implementation, and evaluation of a hybrid DS/FFH spread-spectrum radio transceiver using a single Field Programmable Gate Array (FPGA). The FPGA allows the various subsystems to quickly communicate with each other and thereby maintain tight synchronization. We also investigate various hopping sequences against robustness to interference and jamming. Experimental results are presented that show the receiver sensitivity, radio data-rate/bit-error evaluations, and jamming and interference rejection capabilities of the implemented hybrid DS/FFH spread-spectrum system under widely varying design parameters.

data rate↗
DOE OSTI2013

Modeling, Real-Time Estimation, and Identification of UWB Indoor Wireless Channels

Stochastic differential equations (SDEs) are used to model ultrawideband (UWB) indoor wireless channels. We show that the impulse responses for time-varying indoor wireless channels can be approximated in a mean-square sense as close as desired by impulse responses that can be realized by SDEs. The state variables represent the inphase and quadrature components of the UWB channel. The expected maximization and extended Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Both resolvable and nonresolvable multipath received signals are considered and represented as small-scaled Nakagami fading. The proposed models together with the estimation algorithm are tested using UWB indoor measurement data demonstrating the method’s viability and the results are presented.

Olama, Mohammed M.↗
DOE OSTI2013

On Optimal Input Design and Model Selection for Communication Channels

In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.

fast identification↗
DOE OSTI2013

Coexistence Analysis of Adjacent Long Term Evolution (LTE) Systems

As the licensing and deployment of Long term evolution (LTE) systems are ramping up, the study of coexistence of LTE systems is an essential topic in civil and military applications. In this paper, we present a coexistence study of adjacent LTE systems aiming at evaluating the effect of inter-system interference on system capacity and performance as a function of some of the most common mitigation techniques: frequency guard band, base station (BS) antenna coupling loss, and user equipment (UE) antenna spacing. A system model is constructed for two collocated macro LTE networks. The developed model takes into consideration the RF propagation environment, power control scheme, and adjacent channel interference. Coexistence studies are performed for a different combination of time/frequency division duplex (TDD/FDD) systems under three different guard-bands of 0MHz, 5MHz, and 10MHz. Numerical results are presented to advice the minimum frequency guard band, BS coupling loss, and UE antenna isolation required for a healthy system operation.

adjacent channel interference↗
DOE OSTI2013

Concept of Operations for Collaboration and Discovery from Big Data Across Enterprise Data Warehouses

The success of data-driven business in government, science, and private industry is driving the need for seamless integration of intra and inter-enterprise data sources to extract knowledge nuggets in the form of correlations, trends, patterns and behaviors previously not discovered due to physical and logical separation of datasets. Today, as volume, velocity, variety and complexity of enterprise data keeps increasing, the next generation analysts are facing several challenges in the knowledge extraction process. Towards addressing these challenges, data-driven organizations that rely on the success of their analysts have to make investment decisions for sustainable data/information systems and knowledge discovery. Options that organizations are considering are newer storage/analysis architectures, better analysis machines, redesigned analysis algorithms, collaborative knowledge management tools, and query builders amongst many others. In this paper, we present a concept of operations for enabling knowledge discovery that data-driven organizations can leverage towards making their investment decisions. We base our recommendations on the experience gained from integrating multi-agency enterprise data warehouses at the Oak Ridge National Laboratory to design the foundation of future knowledge nurturing data-system architectures.

Big Data↗
DOE OSTI2013

Security of Classic PN-Spreading Codes for Hybrid DS/FH Spread-Spectrum Systems

Hybrid direct sequence/frequency hopping (DS/FH) spread-spectrum communication systems have recently received considerable interest in commercial applications in addition to their use in military communications because they accommodate high data rates with high link integrity, even in the presence of significant multipath effects and interfering signals. The security of hybrid DS/FH systems strongly depends on the choice of PN-spreading code employed. In this paper, we examine the security, in terms of unicity distance, of linear maximal-length, Gold, and Kasami PN-spreading codes for DS, FH, and hybrid DS/FH spread-spectrum systems without additional encryption methods. The unicity distance is a measure of the minimum amount of ciphertext required by an eavesdropper to uniquely determine the specific key used in a cryptosystem and hence break the cipher. Numerical results are presented to compare the security of the considered PN-spreading codes under known-ciphertext attacks.

direct sequence↗