Optimal phasor data concentrator installation for traffic reduction in smart grid wide-area monitoring systems

Conference Theme: the Power of Global CommunicationsSymposium on Selected Areas in CommunicationsAs one of the core components in wide-area monitoring systems (WAMS), phasor measurement units (PMUs) acquire highly accurate and time-synchronized phasor data at high frequency for smart grid monitoring, protection, and control. Despite the advantages of PMUs, they do generate much data and create a heavy burden on the communication network. One way of alleviating such burden is to install phasor data concentrators (PDC) across the power system to concentrate data generated by the PMUs. Although PDCs are expensive as well, this may still be a much cheaper and more practical option than building a high bandwidth network for WAMS. Therefore, it is very important to solve the optimal PDC installation problem so as to achieve a desired level of traffic reduction. This paper is the first to address this problem and we give solutions for the IEEE 14-bus, 30-bus, and 57-bus systems.published_or_final_versio

A review on protection issues in micro-grids embedded with distribution generations

© 2017 IEEE. According to recent developments, the application of distributed generations (DGs) has become popular especially in distribution systems. The high utilization of distributed generating resources in modern power systems can cause new challenges from protection coordination perspectives. Changing the distribution system structure from single-supply radial system to multi-source ring network, leads to the bidirectional power flow and also has a vital impact on protection coordination issues. In addition, micro-grids can be operated under grid-connected as well as islanded mode, and fault current is extensively different for these two operation modes. Therefore, traditional protection algorithms cannot be used in the advancement of power systems. In recent years, several research studies have been conducted to investigate the improvement of protection schemes in micro-grids. This paper presents a comprehensive review on protection problems resulting from micro-grids embedded with DGs, and discusses some alternate protection strategies

Optimal multistage PMU placement for wide-area monitoring

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A survey on smart grid communication system

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An integrated under frequency load shedding protection based on hybrid intelligent system

Recent blackouts, which are associated with severe technical and economic damages, show that current protection systems are not reliable enough when power system is in an emergency condition. This research attempts to address the issue by introducing a novel, integrated and optimized frequency modelling approach and Under Frequency Load Shedding (UFLS) protection for electric power systems. This system is capable to consider various aspects of the problem simultaneously in modern power systems. Furthermore, it takes advantage of a new multi-objective decision making approach considering all required criteria and risk indicators based on the related standards of power system operation. In this approach, a new frequency response modelling system, named Extended System Frequency Response (ESFR) model and new aggregated load modelling system are proposed. This approach does not only consider all factors which contribute to frequency performance of power system simultaneously, but also is capable to consider advanced components of electric power systems. This modelling system is designed in consistent with the new generation of advanced power system simulators. In the next step, Genetic Algorithm (GA) as an Artificial Intelligent (AI) method is used for designing an optimal and integrated UFLS system. The technical implementation of this step leads to the creation of a new methodology for coupling two software or simulators together. This approach is applied to create a junction between the advanced power system simulator and the GA provider. This method does not only decrease the simulation time dramatically, but also makes the remote communications possible between two or more software. Finally, an AI system, namely Artificial Neural Network (ANN), is used in a hybrid structure to execute the GA UFLS system design as an online Wide Area Protection (WAP) system. The results of the first step show the high capability of the proposed frequency response modelling system. The new approach of under frequency protection system design shows clear advantages over the conventional methods. Finally, the performance of ANN is promising as a new generation of intelligent WAP systems

Physics-Based and Data-Driven Analytics for Enhanced Planning and Operations in Power Systems with Deep Renewable Penetration

This dissertation is motivated by the lack of combined physics-based and data-driven framework for solving power system challenges that are introduced by the integration of new devices and new system components. As increasing number of stochastic generation, responsive loads, and dynamic measurements are involved in the planning and operations of modern power systems, utilities and system operators are in great need of new analysis framework that could combine physical models and measuring data together for solving challenging planning and operational problems. In view of the above challenges, the high-level objective of this dissertation is to develop a framework for integrating measurement data into large physical systems modeled by dynamical equations. To this end, the dissertation first identifies four critical tasks for the planning and operations of the modern power systems: the data collection and pre-processing, the system situational awareness, the decision making process, as well as the post-event analysis. The dissertation then takes one concrete application in each of these critical tasks as the example, and proposes the physics-based/data-driven approach for solving the challenging problems faced by this specific application. To this end, this dissertation focuses on solving the following specific problems using physics-based/data-driven approaches. First, for the data collection and pre-processing platform, a purely data-driven approach is proposed to detect bad metering data in the phasor measurement unit (PMU) monitoring systems, and ensure the overall PMU data quality. Second, for the situational awareness platform, a physics-based voltage stability assessment method is presented to improve the situational awareness of system voltage instabilities. Third, for the decision making platform, a combined physics-based and data-driven framework is proposed to support the decision making process of PMU-based power plant model validation. Forth, for the post-event analysis platform, a physics-based post-event analysis is presented to identify the root causes of the sub-synchronous oscillations induced by the wind farm integration. The above problems and proposed solutions are discussed in detail in Section 2 through Section 5. The results of this work can be integrated to address practical problems in modern power system planning and operations

Cascading Outages Detection and Mitigation Tool to Prevent Major Blackouts

Due to a rise of deregulated electric market and deterioration of aged power system infrastructure, it become more difficult to deal with the grid operating contingencies. Several major blackouts in the last two decades has brought utilities to focus on development of Wide Area Monitoring, Protection and Control (WAMPAC) systems. Availability of common measurement time reference as the fundamental requirement of WAMPAC system is attained by introducing the Phasor Measurement Units, or PMUs that are taking synchronized measurements using the GPS clock signal. The PMUs can calculate time-synchronized phasor values of voltage and currents, frequency and rate of change of frequency. Such measurements, alternatively called synchrophasors, can be utilized in several applications including disturbance and islanding detection, and control schemes. In this dissertation, an integrated synchrophasor-based scheme is proposed to detect, mitigate and prevent cascading outages and severe blackouts. This integrated scheme consists of several modules. First, a fault detector based on electromechanical wave oscillations at buses equipped with PMUs is proposed. Second, a system-wide vulnerability index analysis module based on voltage and current synchrophasor measurements is proposed. Third, an islanding prediction module which utilizes an offline islanding database and an online pattern recognition neural network is proposed. Finally, as the last resort to interrupt series of cascade outages, a controlled islanding module is developed which uses spectral clustering algorithm along with power system state variable and generator coherency information