Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Smart Grid for the Smart City

Modern cities are embracing cutting-edge technologies to improve the services they offer to the citizens from traffic control to the reduction of greenhouse gases and energy provisioning. In this chapter, we look at the energy sector advocating how Information and Communication Technologies (ICT) and signal processing techniques can be integrated into next generation power grids for an increased effectiveness in terms of: electrical stability, distribution, improved communication security, energy production, and utilization. In particular, we deliberate about the use of these techniques within new demand response paradigms, where communities of prosumers (e.g., households, generating part of their electricity consumption) contribute to the satisfaction of the energy demand through load balancing and peak shaving. Our discussion also covers the use of big data analytics for demand response and serious games as a tool to promote energy-efficient behaviors from end users

Locally-Stable Macromodels of Integrated Digital Devices for Multimedia Applications

This paper addresses the development of accurate and efficient behavioral models of digital integrated circuits for the assessment of high-speed systems. Device models are based on suitable parametric expressions estimated from port transient responses and are effective at system level, where the quality of functional signals and the impact of supply noise need to be simulated. A potential limitation of some state-of-the-art modeling techniques resides in hidden instabilities manifesting themselves in the use of models, without being evident in the building phase of the same models. This contribution compares three recently-proposed model structures, and selects the local-linear state-space modeling technique as an optimal candidate for the signal integrity assessment of data links. In fact, this technique combines a simple verification of the local stability of models with a limited model size and an easy implementation in commercial simulation tools. An application of the proposed methodology to a real problem involving commercial devices and a data-link of a wireless device demonstrates the validity of this approac

Comparison between unipolar and bipolar single phase grid-connected inverters for PV applications

An inverter is essential for the interfacing of photovoltaic panels with the AC network. There are many possible inverter topologies and inverter switching schemes and each one will have its own relative advantages and disadvantages. Efficiency and output current distortion are two important factors governing the choice of inverter system. In this paper, it is argued that current controlled inverters offer significant advantages from the point of view of minimisation of current distortion. Two inverter switching strategies are explored in detail. These are the unipolar current controlled inverter and the bipolar current controlled inverter. With respect to low frequency distortion, previously published works provide theoretical arguments in favour of bipolar switching. On the other hand it has also been argued that the unipolar switched inverter offers reduced switching losses and generates less EMI. On efficiency grounds, it appears that the unipolar switched inverter has an advantage. However, experimental results presented in this paper show that the level of low frequency current distortion in the unipolar switched inverter is such that it can only comply with Australian Standard 4777.2 above a minimum output current. On the other hand it is shown that at the same current levels bipolar switching results in reduced low frequency harmonics

Comparison between unipolar and bipolar single phase grid-connected inverters for PV applications

An inverter is essential for the interfacing of photovoltaic panels with the AC network. There are many possible inverter topologies and inverter switching schemes and each one will have its own relative advantages and disadvantages. Efficiency and output current distortion are two important factors governing the choice of inverter system. In this paper, it is argued that current controlled inverters offer significant advantages from the point of view of minimisation of current distortion. Two inverter switching strategies are explored in detail. These are the unipolar current controlled inverter and the bipolar current controlled inverter. With respect to low frequency distortion, previously published works provide theoretical arguments in favour of bipolar switching. On the other hand it has also been argued that the unipolar switched inverter offers reduced switching losses and generates less EMI. On efficiency grounds, it appears that the unipolar switched inverter has an advantage. However, experimental results presented in this paper show that the level of low frequency current distortion in the unipolar switched inverter is such that it can only comply with Australian Standard 4777.2 above a minimum output current. On the other hand it is shown that at the same current levels bipolar switching results in reduced low frequency harmonics

Power system stability scanning and security assessment using machine learning

Future grids planning requires a major departure from conventional power system planning, where only a handful of the most critical scenarios is analyzed. To account for a wide range of possible future evolutions, scenario analysis has been proposed in many industries. As opposed to the conventional power system planning, where the aim is to find an optimal transmission and/or generation expansion plan for an existing grid, the aim in future grids scenario analysis is to analyze possible evolution pathways to inform power system planning and policy making. Therefore, future grids’ planning may involve large amount of scenarios and the existing planning tools may no longer suitable. Other than the raised future grids’ planning issues, operation of future grids using conventional tools is also challenged by the new features of future grids such as intermittent generation, demand response and fast responding power electronic plants which lead to much more diverse operation conditions compared to the existing networks. Among all operation issues, monitoring stability as well as security of a power system and action with deliberated preventive or remedial adjustment is of vital important. On- line Dynamic Security Assessment (DSA) can evaluate security of a power system almost instantly when current or imminent operation conditions are supplied. The focus of this dissertation are, for future grid planning, to develop a framework using Machine Learning (ML) to effectively assess the security of future grids by analyzing a large amount of the scenarios; for future grids operation, to propose approaches to address technique issues brought by future grids’ diverse operation conditions using ML techniques. Unsupervised learning, supervised learning and semi-supervised learning techniques are utilized in a set of proposed planning and operation security assessment tools

Transient stability assessment of hybrid distributed generation using computational intelligence approaches

Includes bibliographical references.Due to increasing integration of new technologies into the grid such as hybrid electric vehicles, distributed generations, power electronic interface circuits, advanced controllers etc., the present power system network is now more complex than in the past. Consequently, the recent rate of blackouts recorded in some parts of the world indicates that the power system is stressed. The real time/online monitoring and prediction of stability limit is needed to prevent future blackouts. In the last decade, Distributed Generators (DGs) among other technologies have received increasing attention. This is because DGs have the capability to meet peak demand, reduce losses, due to proximity to consumers and produce clean energy and thus reduce the production of CO₂. More benefits can be obtained when two or more DGs are combined together to form what is known as Hybrid Distributed Generation (HDG). The challenge with hybrid distributed generation (HDG) powered by intermittent renewable energy sources such as solar PV, wind turbine and small hydro power is that the system is more vulnerable to instabilities compared to single renewable energy source DG. This is because of the intermittent nature of the renewable energy sources and the complex interaction between the DGs and the distribution network. Due to the complexity and the stress level of the present power system network, real time/online monitoring and prediction of stability limits is becoming an essential and important part of present day control centres. Up to now, research on the impact of HDG on the transient stability is very limited. Generally, to perform transient stability assessment, an analytical approach is often used. The analytical approach requires a large volume of data, detailed mathematical equations and the understanding of the dynamics of the system. Due to the unavailability of accurate mathematical equations for most dynamic systems, and given the large volume of data required, the analytical method is inadequate and time consuming. Moreover, it requires long simulation time to assess the stability limits of the system. Therefore, the analytical approach is inadequate to handle real time operation of power system. In order to carry out real time transient stability assessment under an increasing nonlinear and time varying dynamics, fast scalable and dynamic algorithms are required. Transient Stability Assessment Of Hybrid Distributed Generation Using Computational Intelligence Approaches These algorithms must be able to perform advanced monitoring, decision making, forecasting, control and optimization. Computational Intelligence (CI) based algorithm such as neural networks coupled with Wide Area Monitoring System (WAMS) such as Phasor Measurement Unit (PMUs) have been shown to successfully model non-linear dynamics and predict stability limits in real time. To cope with the shortcoming of the analytical approach, a computational intelligence method based on Artificial Neural Networks (ANNs) was developed in this thesis to assess transient stability in real time. Appropriate data related to the hybrid generation (i.e., Solar PV, wind generator, small hydropower) were generated using the analytical approach for the training and testing of the ANN models. In addition, PMUs integrated in Real Time Digital Simulator (RTDS) were used to gather data for the real time training of the ANNs and the prediction of the Critical Clearing Time (CCT)

Design of an Adaptive Neurofuzzy Inference Control System for the Unified Power-Flow Controller

This paper presents a new approach to control the operation of the unified power-flow controller (UPFC) based on the adaptive neurofuzzy inference controller (ANFIC) concept. The training data for the controller are extracted from an analytical model of the transmission system incorporating a UPFC. The operating points' space is dynamically partitioned into two regions: 1) an inner region where the desired operating point can be achieved without violating any of the UPFC constraints and 2) an outer region where it is necessary to operate the UPFC beyond its limits. The controller is designed to achieve the most appropriate operating point based on the real power priority. In this study, the authors investigated and analyzed the effect of the system short-circuit level on the UPFC operating feasible region which defines the limitation of its parameters. In order to illustrate the effectiveness of the control algorithm, simulation and experimental studies have been conducted using the MATLAB/SIMULINK and dSPACE DS1103 data-acquisition board. The obtained results show a clear agreement between simulation and experimental results which verify the effective performance of the ANFIC controller