Development of cost-effective phasor measurement unit for wide area monitoring system applications

Sustained growth in the demand with unprecedented investments in the transmission infrastructure resulted in narrow operational margins for power system operators across the globe. As a result, power networks are operating near to stability limits. This has demanded the electrical utilities to explore new avenues for control and protection of wide area systems. Present supervisory control and data acquisition/energy management systems (SCADA/EMS) can only facilitate steady state model of the network, whereas synchrophasor measurements with GPS time stamp from wide area can provide dynamic view of power grid that enables supervision, and protection of power network and allow the operator to take necessary control/remedial measures in the new regime of grid operations. Construction of phasor measurement unit (PMU) that provide synchrophasors for the assessment of system state is widely accepted as an essential component for the successful execution of wide area monitoring system (WAMS) applications. Commercial PMUs comes with many constraints such as cost, proprietary hardware designs and software. All these constraints have limited the deployment of PMUs at high voltage transmission systems alone. This paper addresses the issues by developing a cost-effective PMU with open-source hardware, which can be easily modified as per the requirements of the applications. The proposed device is tested with IEEE standards

Real-time compression of IEC 61869-9 sampled value data

Fast-acting, yet cost-effective, communications is critical for smarter grid monitoring, protection, and control. This paper demonstrates a new approach for the real-time compression of Sampled Value (SV) data based on the IEC 61869-9 recommendations. This approach applies simple compression rules, yet yields excellent compression performance---typically compressing data to less than half of the original size. This leads to a significant and beneficial reduction in encoding time (in the merging unit producing the SV data) and decoding time (at the end application), as well as the main benefit of reduced Ethernet transmission times resulting from the reduced frame size. As well as reducing the absolute bandwidth requirements in typical applications, this has system-wide benefits due to reducing Ethernet queuing delays and the consequent network jitter. The approach has been validated on a real-time platform to accurately measure all contributions to the end-to-end delay. This work will help enable low-latency and bandwidth-sensitive applications involving the SV protocol, such as phasor measurement units and wide-area protection

Key performance aspects of an LTE FDD based Smart Grid communications network

The Smart Grid will enable a new era of electricity generation, transmission, distribution and consumption driven by efficiency, reliability, flexibility and environmental concerns. A key component of the Smart Grid is a communications infrastructure for data acquisition, monitoring, control and protection. In this paper, we evaluate the key performance aspects of an LTE Release 8 FDD network as the wide area communications network for Smart Grid applications. We develop analytical results for latency and channel utilization and discuss the implications for Smart Grid traffic sources, particularly the fact that system capacity is likely to be control channel limited. We also develop an OPNET based discrete event simulation model for a PMU based fault monitoring system using LTE FDD as its communication medium and use it to validate the analytical findings. In particular, we demonstrate how uplink data plane latencies of less than 10ms can only be achieved using small application layer packets. These findings can be used to understand how to best deploy an LTE FDD network in a Smart Grid environment and also in the development of new radio resource management algorithms that are tailored specifically to Smart Grid traffic sources

Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator

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