Wei Lee Woon

Optimal Protection Coordination for Microgrids With Grid-Connected and Islanded Capability

Microgrids can be operated either grid-connected to reduce system losses and for peak shaving or islanded to increase reliability and provide backup power during utility outage. Such dual configuration capability imposes challenges on the design of the protection system. Fault current magnitudes will vary depending on the microgrid operating mode. In this paper, a microgrid protection scheme that relies on optimally sizing fault current limiters and optimally setting directional overcurrent relays is proposed. The protection scheme is optimally designed taking into account both modes of operation (grid-connected and islanded). The problem has been formulated as a constrained nonlinear programming problem and is solved using the genetic algorithm with the static penalty constraint-handling technique. The proposed approach is tested on two medium-voltage networks: a typical radial distribution system and on the IEEE 30-bus looped power distribution system equipped with directly connected conventional synchronous generators.

A Bayesian Passive Islanding Detection Method for Inverter-Based Distributed Generation Using ESPRIT

In this paper, a new passive islanding detection method for grid-connected inverter-based distributed-generation (DG) systems is proposed. A statistical signal-processing algorithm known as estimation of signal parameters via rotational invariance techniques is used to extract new features from measurements of the voltage and frequency at the point of common coupling as islanding indicators. The new features are defined based on a damped-sinusoid model for power system voltage and frequency waveforms, and include modal initial amplitudes, oscillation frequencies, damping factors, and initial phases. A set of training cases generated on the IEEE 34-bus system was used to train a naïve-Bayes classifier that discriminates islanding and nonislanding events. Cross-validation was used to evaluate the performance of the proposed islanding detection method. The results show that by using the new features extracted from ESPRIT, the classifier is capable of discriminating islanding and nonislanding events with an accuracy close to 100%.

A Differential Sequence Component Protection Scheme for Microgrids With Inverter-Based Distributed Generators

The protection of a microgrid containing inverter- based distributed generators (IBDGs) presents several problems if traditional techniques which rely on the current (fuses and overcurrent relays) are used. A possible solution to these problems is the use of a new type of the relay which takes advantage of the enhanced processing techniques and communication infrastructure, both of which are recently becoming available for power networks application. This paper proposes a new communication-based protection scheme for isolated microgrids where a data mining approach is used to identify the relay settings and parameters. A feature selection technique is implemented to help identify the most relevant electrical features required for the fault detection and to establish the best communication strategy to use between relays. The proposed approach is tested using a MATLAB simulation of a facility scale isolated microgrid embedded with IBDGs. The results show that a differential protection scheme that relies on symmetrical components is the most effective strategy for protecting microgrids with IBDGs.

Asymmetric information distances for automated taxonomy construction

A novel method for automatically constructing taxonomies for specific research domains is presented. The proposed methodology uses term co-occurrence frequencies as an indicator of the semantic closeness between terms. To support the automated creation of taxonomies or subject classifications we present a simple modification to the basic distance measure, and describe a set of procedures by which these measures may be converted into estimates of the desired taxonomy. To demonstrate the viability of this approach, a pilot study on renewable energy technologies is conducted, where the proposed method is used to construct a hierarchy of terms related to alternative energy. These techniques have many potential applications, but one activity in which we are particularly interested is the mapping and subsequent prediction of future developments in the technology and research.

An ensemble model for day-ahead electricity demand time series forecasting

In this work, we try to solve the problem of day-ahead prediction of electricity demand using an ensemble forecasting model. Based on the Pattern Sequence Similarity (PSF) algorithm, we implemented five forecasting models using different clustering techniques: K-means model (as in original PSF), Self-Organizing Map model, Hierarchical Clustering model, K-medoids model, and Fuzzy C-means model. By incorporating these five models, we then proposed an ensemble model, named Pattern Forecasting Ensemble Model (PFEM), with iterative prediction procedure. We evaluated its performance on three real-world electricity demand datasets and compared it with those of the five forecasting models individually. Experimental results show that PFEM outperforms all those five individual models in terms of Mean Error Relative and Mean Absolute Error.

Measuring Innovation Using Bibliometric Techniques: The Case of Solar Photovoltaic Industry

In this paper, we use feature extraction and data analysis techniques for the elucidation of patterns and trends in technological innovation. In studying innovation, we focus on the role of public research institutions (research universities and national laboratories) in the development of new industries. More specifically, we are interested in measuring innovation through research collaborations between these institutions and the private sector. The proposed methods are primarily drawn from the field of bibliometrics – i.e. the analysis of information and trends in the publication of text documents, rather than the contents of these documents. In particular, we seek to explore the relationship between joint publication patterns and trends, R&D funding, technology development choices, and the viability and effectiveness of industry-university collaborations. To focus the discussions and to provide concrete examples of their applicability, this study will have an initial emphasis on the solar photovoltaic (PV) sector in the U.S., though the techniques and general approach devised here will be applicable to a broad range of industries, situations, and locations. Our analysis suggests that interesting information and conclusions can be derived from this line of analysis. The results obtained using our data extraction techniques allow us to identify early technology focus in different areas within solar PV technologies, and to determine potential technology pathways, which is critical for innovation policy in the renewable energy domain.

Artificial Neural Network-based electricity price forecasting for smart grid deployment

A deregulated electricity market is one of the keystones of up-and-coming smart grid deployments. In such a market, forecasting electricity prices is essential to helping stakeholders with the decision making process. Electricity price forecasting is an inherently difficult problem due to its special characteristics of dynamicity and nonstationarity. In our research, we use an Artificial Neural Network (ANN) model on carefully crafted input features for forecasting hourly electricity prices for the next 24 hours. The input features are selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. A wrapper method for feature selection is used in which the ANN model is continuously trained and updated in order to select the best feature set. The performance of the proposed method is evaluated and compared with the published results of the state-of-the-art Pattern Sequence-based Forecasting (PSF) method on the same data sets and our method is observed to provide superior results.

NLP-KAOS for Systems Goal Elicitation: Smart Metering System Case Study

This paper presents a computational method that employs Natural Language Processing (NLP) and text mining techniques to support requirements engineers in extracting and modeling goals from textual documents. We developed a NLP-based goal elicitation approach within the context of KAOS goal-oriented requirements engineering method. The hierarchical relationships among goals are inferred by automatically building taxonomies from extracted goals. We use smart metering system as a case study to investigate the proposed approach. Smart metering system is an important subsystem of the next generation of power systems (smart grids). Goals are extracted by semantically parsing the grammar of goal-related phrases in abstracts of research publications. The results of this case study show that the developed approach is an effective way to model goals for complex systems, and in particular, for the research-intensive complex systems.

Optimal Sizing of Thyristor-Controlled Impedance for Smart Grids With Multiple Configurations

Smart grids have become one of the important and challenging topics due to the numerous benefits it can bring to the power system. In this context, distributed generation (DG) is expected to play a significant role. The smart grid can have multiple configurations depending on the smart grid operating strategy and system conditions. In smart grids, DG could be operated either grid connected or islanded. Such flexible and variable configuration results in variable fault current levels which could impact the operation of the existing protective devices on the distribution system. In this paper, it is proposed to optimally size thyristor-controlled impedance (TCI) of both inductive and capacitive type to manage the fault current levels under different smart grid configurations. The salient benefit is to avoid damage and delayed operation of protective devices due to the variability in fault currents with synchronous-based DG. The problem is formulated as a nonlinear programming (NLP) problem and the optimum size and type of the TCI is determined using particle swarm optimization (PSO). Results show that by optimally locating and sizing TCI, fault current levels under various smart grid configurations can be managed and thus avoiding protective device coordination failure and damage.

Semantic distances for technology landscape visualization

This paper presents a novel approach to the visualization of research domains in science and technology. The proposed methodology is based on the use of bibliometrics; i.e., analysis is conducted using information regarding trends and patterns of publication rather than the actual content. In particular, we explore the use of term co-occurrence frequencies as an indicator of semantic closeness between pairs of terms. To demonstrate the utility of this approach, a number of visualizations are generated for a collection of renewable energy related keywords. As these keywords are regarded as manifestations of the associated research topics, we contend that the proposed visualizations can be interpreted as representations of the underlying technology landscape.