Reza Arghandeh

Høgskulen på Vestlandet | HVL · Department of Computing, Mathematics and Physics

The harmonics generated by DER devices can cause distortion in power system voltages and currents. In addition to the power quality issues, loss, and component failures harmonics can have an economic impact on distribution networks. In this paper the authors analyze harmonics produced from multiple sources such as solar, wind and energy storage sys...

Roadways are critical infrastructure in our society, providing services for people through and between cities. However, they are prone to closures and disruptions, especially after extreme weather events like hurricanes. At the same time, traffic flow data are a fundamental type of information for any transportation system. In this paper, we tackle...

p>This paper proposes a framework consisting of a forepart precursor and the Scenario Fan Problem (SFP) for long-term hydropower scheduling, using shape-based feature extraction. The domain feature of the forepart precursor is scenario decomposition and scenario selection. Then, the selected scenarios will be the input of SFP, based on the feature...

p>This paper proposes a framework consisting of a forepart precursor and the Scenario Fan Problem (SFP) for long-term hydropower scheduling, using shape-based feature extraction. The domain feature of the forepart precursor is scenario decomposition and scenario selection. Then, the selected scenarios will be the input of SFP, based on the feature...

After decades of research, automatic synthetic aperture radar (SAR)-optical registration remains an unsolved problem. SAR and optical satellites utilize different imaging mechanisms, resulting in imagery with dissimilar heterogeneous characteristics. Transforming and translating these characteristics into a shared domain has been the main challenge...

Infrastructures such as roadways, power lines, and communications networks play a critical role in our society. However, they are also susceptible to failures, especially those caused by extreme events, quickly affecting large geographical areas. Predicting where and when these failures will occur with high confidence is very difficult because of t...

It is essential to have accurate and reliable daily-inflow forecasting to improve short-term hydropower scheduling. This paper proposes a Causal multivariate Empirical mode Decomposition (CED) framework as a complementary pre-processing step for a day-ahead inflow forecasting problem. The idea behind CED is combining physics-based causal inference...

Transportation systems are vulnerable to catastrophic storms while their recovery is vital for returning a community to its pre-storm state. Therefore, performing an accurate damage evaluation and identifying the patterns based on the strengths of these extreme weather events are essential for emergency professionals. A critical problem associated...

Knowing vegetation type in an area is crucial for several applications, including ecology, land use management, and infrastructure risk assessment. In combination with recent advancements in image processing, remote sensing technology has been used to perform fast vegetation type estimation and reduce the need for intensive and time-consuming field...

Disasters Such As Hurricanes, Earthquakes, Wildfires, Etc. Are Felt Most Acutely At local and regional levels. These events have exposed weaknesses in how well-prepared infrastructure operators are to keep their services and provide resilient responses. Outages and service disruptions are largely due to the inability of the affected city infrastruc...

This paper proposes a novel distribution line parameter estimation method, driven by the probabilistic data fusion of the distributed phasor measurement unit (D-PMU) and the advanced measurement infrastructure. The synchronized and high-precision D-PMU is utilized to tackle the challenge risen by the a-synchronization of smart meters. Corresponding...

Transportation systems are vulnerable to hurricanes and yet their recovery plays a critical role in returning a community to its pre-hurricane state. Vegetative debris is among the most significant causes of disruptions on transportation infrastructure. Therefore, identifying the driving factors of hurricane-caused debris generation can help clear...

A significant issue for fault classification in power distribution systems is limited fault data for training classifiers to identify power failure types for remediation. Measurement data from power systems are mostly unlabeled without specified fault types, and labeled data with confirmed fault types are very limited, posing challenges to training...

Hurricanes affect millions of people in the U.S. every year and cause billion-dollar economic losses. Florida is one of the states in the U.S. which is vulnerable to hurricanes and major infrastructure damages were reported due to these extreme events such as Hurricane Hermine (2016). This study aims to assess the impacts of Hermine on the transpor...

During extreme weather events like hurricanes, trees can cause significant challenges for the local communities with roadway closures or power outages. Local responders must act quickly with information regarding the extent and severity of hurricane damage to better manage recovery procedures following natural disasters. This paper proposes an appr...

Catastrophic weather has significantly battered the U.S. Gulf Coast in recent years and exposed critical deficiencies in the resilience across communities and organizations. These deficiencies compel the devising of strategies to identify critical infrastructure components that require more attention with regard to building resilience. This article...

Vegetation Management is a significant preventive maintenance expense in many power transmission and distribution companies. Traditional Vegetation Management operational practices have proven ineffective and are rapidly becoming obsolete due to the lack of frequent inspection of vegetation and environmental states. The rise of satellite imagery da...

Smart grids, which benefit from the deployment of new technologies such as renewable energy sources, electric vehicles, SCADA and PMU systems and etc., are becoming increasingly complex and uncertain, that can affect security and reliability of these systems. Power variation in generation units and loads demand causes variation in buses voltage tha...

Management of the electrical grid has an importance on the sustainability and reliability of the electrical energy supply. In the process, it is still crucial that power quality (PQ) is evaluated as part of any grid management master plan. This article provides a novel approach for classifying PQ disturbances such as voltage sag, swell, interruptio...

DESCRIPTION The distribution-level phasor measurement unit (D-PMU) is a measurement device that measures the synchronized voltage and current values of electric power distribution networks. The synchronized data achieved by D-PMUs supports situational awareness, diagnostic, and control applications in the grid. In this chapter, we review the state...

Hurricanes affect thousands of people annually, with devastating consequences such as loss of life, vegetation and infrastructure. Vegetation losses such as downed trees and infrastructure disruptions such as toppled power lines often lead to roadway closures. These disruptions can be life threatening for the victims. Emergency officials, therefore...

This paper presents a spatiotemporal feature learning method for cause identification of electromagnetic transient events in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurements and using the convolutional neural network as the spatiotemporal feature representation along with soft...

The present paper aims at determining the most influential features to be extracted from smart meter data to facilitate machine learning-based classification of non-residential buildings. Smart meter-driven remote estimation of the chosen characteristics (the buildings’ performance class, use type, and operation group) is significantly helpful in b...

The COVID-19 outbreak and ensuing social distancing behaviors resulted in substantial reduction on traffic, making this a unique experiment on observing the air quality. Such an experiment is also supplemental to the smart city concept as it can help to identify whether there is a delay on air quality improvement during or after a sharp decline on...

This study proposes a novel machine learning‐based methodology to estimate the air‐conditioning (AC) load from the hourly smart meter data. The commonly employed approaches for disaggregating the share of the AC load from the total consumption are either using data obtained from dedicated sensors or high‐frequency data that cannot be provided by co...

This study is focused on real-time topology detection (TD) problems in the power distribution system. The advent of distribution phasor measurement units offers additional opportunities to use deep learning methods for accurate TD of the distribution system. In this study, a new concept named the kernel-node-map is presented, and then a novel topol...

Many local governments have started using smartphone applications to more effectively inform and communicate with citizens. This trend is of interest, as cities can only be smart if they are responsive to their citizens. In this paper, the intention to use such a mobile application among adult residents (n = 420) of a mid-sized city in the southeas...

This study examined the role of channel synchronicity (synchronous versus asynchronous) in shaping perceptions of citizens (n=467) of a mid-sized city in the Southeastern U.S. regarding the speed and quality of information received from their local government during and immediately after two hurricanes. We employed a mixed-methods approach combinin...

Hurricanes lead to substantial infrastructure system damages, such as roadway closures and power outages, in the US annually, especially in states like Florida. As such, this paper aimed to assess the impacts of Hurricane Hermine (2016) and Hurricane Michael (2018) on the City of Tallahassee, the capital of Florida, via exploratory spatial and stat...

Operational practices of power distribution systems are impacted by the increasing connection of renewable energy resources, electric vehicles, energy storage systems and deployment of demand-response mechanisms. The synchronized, low-latency, and high-resolution measurements that are provided by distribution-level phasor measurement units (D-PMUs)...

Smart cities can be viewed as large-scale Cyber-Physical Systems (CPS) where different sensors and devices record the cyber and physical indicators of the city systems. The collected data are used for improving urban life by offering services such as accurate electric load forecasting, and more efficient traffic management. Traditional monitoring f...

This paper proposes a Multi-task Logistic Low-Ranked Dirty Model (MT-LLRDM) for fault detection in power distribution networks by using the distribution Phasor Measurement Unit (PMU) data. The MT-LLRDM improves the fault detection accuracy by utilizing the similarities in the fault data streams among multiple locations across a power distribution n...

Short-term household electricity load forecasting is important for utility companies to ensure reliable power supplies. Traditional methods for load forecasting relied on historical records from one single data source and have limitations with insufficient or missing data. Recently, an emerging family of machine learning algorithms, multi-task lear...

This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine l...

This paper presents a low cost end-to-end and open-source urban sensor node, called UrbanBox, which is based on the concept of the Internet of Things (IoT). Through the sensing platform made by multiple UrbanBox nodes, users can collect, visualize, and store the real-time data from urban environments. The platform can serve as a tool for developing...

Resilience is mostly considered as a single dimension attribute of a system. Most of the recent works on resilience treat it as a single dimension attribute of a system or study the different dimensions of the resilience separately without considering its multi-domain nature. In this paper, we propose an advanced causal inference approach combined...

This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurement, and using the convolutional neural network (CNN) as the spatiotemporal feature repr...

In the research of smart buildings, human activity recognition is an important cornerstone for numerous emerging applications. Although several sensing techniques have been proposed for human activity identification, they require either the user instrumentation or additional infrastructure, that are inconvenient, privacy-intrusive and expensive. To...

Roadway closures magnify the adverse effects of disasters on people since any type of such disruption increases the emergency response travel time (ERTT), which is of central importance for the safety and survival of the affected people. Especially in the State of Florida, high winds due to hurricanes, such as the Hurricane Hermine, lead to notable...

Data mining is the process of turning raw data into useful information. Data mining has been employed in many different data-rich industries, including banking, healthcare, manufacturing, and telecommunications. With the additions of thousands of PMUs to the nation’s power grid, the power systems industry has the data necessary to take advantage of...

Urban mobility is a multidimensional characteristic of cities experienced as layers of interconnected infrastructures, places, people, and information. Therefore, the study of networks such as electricity and transportation systems should go beyond an individual network and merge with other networks. This paper proposes the bundled causality engine...

Florida's emergency relief operations were significantly affected by recent hurricanes such as Hermine and Irma that caused massive roadway and power system distributions. During these recent devastating hurricanes, the problems associated with providing accessibility and safety became even more challenging, especially for those vulnerable communit...

Hurricane Hermine was the first hurricane to make landfall in Florida since Hurricane Wilma in 2005, and was the first hurricane to directly hit Apalachee Bay since Hurricane Alma in 1966. As a result, Hermine left 100,000 residents without power in the City of Tallahassee, the capital of Florida, knocking out trees, power lines and shutting down s...

This paper proposes a novel causality analysis approach called the Causal Markov Elman Network (CMEN) to characterize the interdependency among heterogeneous time-series in multi-network systems. The CMEN performance, which comprises of inputs filtered by Markov property, successfully characterizes various multivariate dependencies in an urban envi...

This paper presents a shape preserving incremental learning algorithm that employs a novel shape-based metric called the Fisher-Rao Amplitude-Phase Distance (FRAPD) metric. The combined amplitude and phase distance metric is achieved on a function space from the Fisher-Rao elastic registration. We utilize an exhaustive search method for selecting t...

Natural disasters have devastating effects on the infrastructure and disrupt every aspect of daily life in the regions they hit. To alleviate problems caused by these disasters, first an impact assessment is needed. As such, this paper focuses on a two-step methodology to identify the impact of Hurricane Hermine on the City of Tallahassee, the capi...

With the unprecedented advancement of sensing technology, smart city applications now have access to rich measurement data related to system dynamics, states, and the behavior of its users. However, classic data analysis or machine learning tools ignore some unique characteristics of the multi-stream measurement data, in particular, the co-existenc...

In this study we consider the problem of outlier detection with multiple co-evolving time series data. To capture both the temporal dependence and the inter-series relatedness, a multi-task non-parametric model is proposed, which can be extended to data with a broader exponential family distribution by adopting the notion of Bregman divergence. Alb...

With the advent of Distribution PhasorMeasurement Units (D-PMUs) andMicro-Synchrophasors (Micro-PMUs), the situational awareness in power distribution systems is going to the next level using time-synchronization. However, designing, analyzing, and testing of such accuratemeasurement devices are still challenging. Due to the lack of available knowl...

The purpose of this white paper is to provide an introductory reference for industry and academic practitioners interested in exploring the use of synchrophasors and other time-synchronized measurements for supporting power distribution system planning, operation, and research. This paper is motivated by the belief that effective measurement and an...

This chapter starts with a brief discussion on classical supervised and unsupervised learning paradigms. The focus is not to give an extensive review of the field, which is impossible due to its many ramifications, but rather to equip the readers with fundamental ideas and popular approaches for regression, classification, dimension reduction, etc....

Historically, with mostly radial power distribution and one-way power flow, it was only necessary to evaluate the envelope of design conditions, e.g., peak loads or fault currents, rather than continually observe the operating state. But the growth of distributed energy resources introduces variability, uncertainty, and opportunities to recruit div...

This paper proposes a novel method for topology detection in distribution networks called the Time-Series Signature Verification Method for Topology Detection (TSV-Top). The TSV-Top analyzes data from phasor measurement units (PMU or μPMU) installed on power distribution feeders. The TSVTop relies on the fact that measurement data time series from...

Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and...

The dissertation is based on the meaning of Power System Resilience. Resilience needs to be assessed by identifying the system, analyzing its vulnerabilities and delivering effective operations with real time control. Novel contribution focuses on Power System Resilience Measurement, based on load prioritization, constraint parameters, types and num...

This paper presents an alternative approach to power system computations, Graph Trace Analysis (GTA), and applies this approach to solving the power flow problem. GTA is derived from the Generic Programming Paradigm of computer science, and uses topology iterators to move through components in a model and perform calculations. The implementation of...