Voltage Stability Assessment and Enhancement in Power Systems

Voltage stability is a long standing issue in power systems and also is critical in the power system. This thesis aims to address the voltage stability problems. When wind generators reach maximum reactive power output, the bus voltage will operate near its steady-state stability limit. In order to avoid voltage instability, a dynamic L-index minimization approach is proposed by incorporating both wind generators and other reactive power resources. It then verifies the proposed voltage stability enhancement method using real data from load and wind generation in the IEEE 14 bus system. Additionally, power system is not necessary to always operate at the most voltage stable point as it requires high control efforts. Thus, we propose a novel L-index sensitivity based control algorithm using full Phasor measurement unit measurements for voltage stability enhancement. The proposed method uses both outputs of wind generators and additional reactive power compensators as control variables. The L-index sensitivity with respect to control variables is introduced. Based on these sensitivities, the control algorithm can minimise all the control efforts, while satisfying the predetermined L-index value. Additionally, a subsection control scheme is applied where both normal condition and weak condition are taken into account. It consists of the proposed L-index sensitivities based control algorithm and an overall L-index minimisation method. Threshold selection for the subsection control scheme is discussed and extreme learning machine is introduced for status fast classification to choose the method which has less power cost on the transmission line. Due to the high cost of PMUs, a voltage stability assessment method using partial Phasor measurement unit (PMU) measurements is proposed. Firstly, a new optimisation formulation is proposed that minimizes the number of PMUs considering the most sensitive buses. Then, extreme learning machine (ELM) is used for fast voltage estimation. In this way, the voltages at buses without PMUs can be rapidly obtained based on the PMUs measurements. Finally, voltage stability can be assessed by using L-index

Hadoop performance modeling and job optimization for big data analytics

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonBig data has received a momentum from both academia and industry. The MapReduce model has emerged into a major computing model in support of big data analytics. Hadoop, which is an open source implementation of the MapReduce model, has been widely taken up by the community. Cloud service providers such as Amazon EC2 cloud have now supported Hadoop user applications. However, a key challenge is that the cloud service providers do not a have resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user responsibility to estimate the require amount of resources for their job running in a public cloud. This thesis presents a Hadoop performance model that accurately estimates the execution duration of a job and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model employs Locally Weighted Linear Regression (LWLR) model to estimate execution time of a job and Lagrange Multiplier technique for resource provisioning to satisfy user job with a given deadline. The performance of the propose model is extensively evaluated in both in-house Hadoop cluster and Amazon EC2 Cloud. Experimental results show that the proposed model is highly accurate in job execution estimation and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model. In addition, the Hadoop framework has over 190 configuration parameters and some of them have significant effects on the performance of a Hadoop job. Manually setting the optimum values for these parameters is a challenging task and also a time consuming process. This thesis presents optimization works that enhances the performance of Hadoop by automatically tuning its parameter values. It employs Gene Expression Programming (GEP) technique to build an objective function that represents the performance of a job and the correlation among the configuration parameters. For the purpose of optimization, Particle Swarm Optimization (PSO) is employed to find automatically an optimal or a near optimal configuration settings. The performance of the proposed work is intensively evaluated on a Hadoop cluster and the experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings.Abdul Wali Khan University Marda

Synchrophasor Sensing and Processing Based Smart Grid Security Assessment for Renewable Energy Integration

With the evolution of energy and power systems, the emerging Smart Grid (SG) is mainly featured by distributed renewable energy generations, demand-response control and huge amount of heterogeneous data sources. Widely distributed synchrophasor sensors, such as phasor measurement units (PMUs) and fault disturbance recorders (FDRs), can record multi-modal signals, for power system situational awareness and renewable energy integration. An effective and economical approach is proposed for wide-area security assessment. This approach is based on wavelet analysis for detecting and locating the short-term and long-term faults in SG, using voltage signals collected by distributed synchrophasor sensors. A data-driven approach for fault detection, identification and location is proposed and studied. This approach is based on matching pursuit decomposition (MPD) using Gaussian atom dictionary, hidden Markov model (HMM) of real-time frequency and voltage variation features, and fault contour maps generated by machine learning algorithms in SG systems. In addition, considering the economic issues, the placement optimization of distributed synchrophasor sensors is studied to reduce the number of the sensors without affecting the accuracy and effectiveness of the proposed approach. Furthermore, because the natural hazards is a critical issue for power system security, this approach is studied under different types of faults caused by natural hazards. A fast steady-state approach is proposed for voltage security of power systems with a wind power plant connected. The impedance matrix can be calculated by the voltage and current information collected by the PMUs. Based on the impedance matrix, locations in SG can be identified, where cause the greatest impact on the voltage at the wind power plants point of interconnection. Furthermore, because this dynamic voltage security assessment method relies on time-domain simulations of faults at different locations, the proposed approach is feasible, convenient and effective. Conventionally, wind energy is highly location-dependent. Many desirable wind resources are located in rural areas without direct access to the transmission grid. By connecting MW-scale wind turbines or wind farms to the distributions system of SG, the cost of building long transmission facilities can be avoid and wind power supplied to consumers can be greatly increased. After the effective wide area monitoring (WAM) approach is built, an event-driven control strategy is proposed for renewable energy integration. This approach is based on support vector machine (SVM) predictor and multiple-input and multiple-output (MIMO) model predictive control (MPC) on linear time-invariant (LTI) and linear time-variant (LTV) systems. The voltage condition of the distribution system is predicted by the SVM classifier using synchrophasor measurement data. The controllers equipped with wind turbine generators are triggered by the prediction results. Both transmission level and distribution level are designed based on this proposed approach. Considering economic issues in the power system, a statistical scheduling approach to economic dispatch and energy reserves is proposed. The proposed approach focuses on minimizing the overall power operating cost with considerations of renewable energy uncertainty and power system security. The hybrid power system scheduling is formulated as a convex programming problem to minimize power operating cost, taking considerations of renewable energy generation, power generation-consumption balance and power system security. A genetic algorithm based approach is used for solving the minimization of the power operating cost. In addition, with technology development, it can be predicted that the renewable energy such as wind turbine generators and PV panels will be pervasively located in distribution systems. The distribution system is an unbalanced system, which contains single-phase, two-phase and three-phase loads, and distribution lines. The complex configuration brings a challenge to power flow calculation. A topology analysis based iterative approach is used to solve this problem. In this approach, a self-adaptive topology recognition method is used to analyze the distribution system, and the backward/forward sweep algorithm is used to generate the power flow results. Finally, for the numerical simulations, the IEEE 14-bus, 30-bus, 39-bus and 118-bus systems are studied for fault detection, identification and location. Both transmission level and distribution level models are employed with the proposed control strategy for voltage stability of renewable energy integration. The simulation results demonstrate the effectiveness of the proposed methods. The IEEE 24-bus reliability test system (IEEE-RTS), which is commonly used for evaluating the price stability and reliability of power system, is used as the test bench for verifying and evaluating system performance of the proposed scheduling approach

Data Consistency for Data-Driven Smart Energy Assessment

In the smart grid era, the number of data available for different applications has increased considerably. However, data could not perfectly represent the phenomenon or process under analysis, so their usability requires a preliminary validation carried out by experts of the specific domain. The process of data gathering and transmission over the communication channels has to be verified to ensure that data are provided in a useful format, and that no external effect has impacted on the correct data to be received. Consistency of the data coming from different sources (in terms of timings and data resolution) has to be ensured and managed appropriately. Suitable procedures are needed for transforming data into knowledge in an effective way. This contribution addresses the previous aspects by highlighting a number of potential issues and the solutions in place in different power and energy system, including the generation, grid and user sides. Recent references, as well as selected historical references, are listed to support the illustration of the conceptual aspects

Impact of prominent synchrophasor estimation algorithms on power system stability assessment

The electricity network is a critical infrastructure and its reliability is of paramount importance for the functionality of many critical systems in the modern society. Power system stability is one of the imperative aspects that impacts the reliability of electrical networks, hence power system stability needs to be observed in real-time for secure and reliable operation of the power grids. Conventionally, supervisory control and data acquisition (SCADA) based wide-area monitoring systems (WAMS) have been used for this purpose, however, they are predominantly designed to detect static changes in steady-state stability. In contrast, modern wide-area power networks pose significant challenges such as presence of power electronic switching loads and inductive motor loads, asynchronous distributed generation and dynamic fluctuations in demand and supply. Synchrophasor based WAMS is the next generation WAMS technology and offers great advantages over traditional SCADA systems such as precise time synchronisation, universally accepted standardisation and extremely fast and robust phasor estimation. A strategically placed network of phasor measurement units (PMUs) enables full visibility of the entire power network. Time synchronised PMU data can then be transferred to a phasor data centre (PDC) using efficient communication algorithms where multi facet analysis, including realtime stability assessment, could be performed. Despite significant benefits of the synchrophasor technology, several factors have hindered the widespread adoption ofthe synchrophasor technology. This research addresses such contemporary issues. The first phase of this research details an empirical study of existing synchrophasor estimation algorithms (SEAs) and considers the need for a benchmark in terms of robustness. Synchrophasor research is heavily populated with studies presenting diverse SEAs. Interestingly, not many studies have attempted to develop a robust SEA based on the mathematical technique proposed in the original Institute of Electrical and Electronics Engineers (IEEE) standardisation (i.e. IEEE std. C37.118.1-2011), the quadrature demodulation (QD) technique. Therefore, a verifiable benchmark algorithm is not currently available. This research presents comprehensive synchrophasor estimation models developed based on the QD technique and is then presented as the benchmark SEA. Proposed models are tested against all compliance requirements stipulated in the latest IEEE standardisation. Furthermore, a detailed comparison of prominent synchrophasor models is conducted against the proposed benchmark models, to understand the impact of the SEAs on the overall phasor estimation. Results establish a clear link between the accuracy/latency of the phasor estimation and the accompanying synchrophasor algorithm. The second phase of this research involves testing and comparison of synchrophasor models on hardware platforms. Even though development of SEA has been a prominent research area, only a few of these studies have been verified and validated with field tested results. This is a significant barrier to the advent of improved SEAs beyond academic literature, especially in industrial applications. A laboratory scale, hardware based synchrophasor test platform is proposed where any synchrophasor algorithm can be tested for any test condition or fault signal. Key highlights of this section include; global position system (GPS) time synchronisation of synchrophasors and a sinusoidal pulse width modulation (SPWM) technique based scalable input system capable of generating measurement conditions emulating any fault condition. Results establish the superiority of the proposed benchmark algorithm and identify key implementation issues in hardware implementation of some of the prominent synchrophasor models. The final phase of this research develops a synchrophasor based WAMS by using a bottom-up approach to evaluate real-time stability of wide-area networks under practical power network fault conditions. As part of this research the analyses and the impact of SEAs on the overall stability assessment has been evaluated. Development and testing of PMUs, and stability studies are historically conducted in two disjointed silos. As a result, stability analysis is often conducted based on the assumption that the PMU data delivered to the PDC are accurate and instantaneous. On the other hand, SEAs are tested against the compliance criteria listed in the IEEE standardisation which do not involve any practical power network faults. This study attempts to dive into this unexplored territory. Performance in realtime voltage and frequency stability of prominent SEAs is evaluated by employing a strategically placed PMU network on two standard power networks simulation models. The IEEE 9-bus system and New England 39-bus system are considered and consists synchronous generation sources, dynamic load centres and transmission links. By modelling practical transient fault conditions such as short circuit faults, loss of generation and addition of load centres, the real-time voltage and frequency stability have been studied. A modified highest Lyapunov exponent (HLE) based real-time stability assessment algorithm (RSAA) is proposed to suit implementation in practical power networks. Despite the full compliance against the IEEE standardisation, tested algorithms produce significantly different outcomes in the stability assessment that may directly impact on the subsequent activation of protection systems and overall network stability. Results of this study point to interesting findings and establishes a clear link between the reliability and the performance of the underlining SEA. In conclusion, key findings of this research contribute to two prominent areas within the synchrophasor research; SEA development and testing, and real-time stability assessment. This research has established a strong link between these disjointed research fields, thereby enabling future advancements synchrophasor based stability monitoring and control systems

Data-Centric Situational Awareness and Management in Intelligent Power Systems

The rapid development of technology and society has made the current power system a much more complicated system than ever. The request for big data based situation awareness and management becomes urgent today. In this dissertation, to respond to the grand challenge, two data-centric power system situation awareness and management approaches are proposed to address the security problems in the transmission/distribution grids and social benefits augmentation problem at the distribution-customer lever, respectively. To address the security problem in the transmission/distribution grids utilizing big data, the first approach provides a fault analysis solution based on characterization and analytics of the synchrophasor measurements. Specifically, the optimal synchrophasor measurement devices selection algorithm (OSMDSA) and matching pursuit decomposition (MPD) based spatial-temporal synchrophasor data characterization method was developed to reduce data volume while preserving comprehensive information for the big data analyses. And the weighted Granger causality (WGC) method was investigated to conduct fault impact causal analysis during system disturbance for fault localization. Numerical results and comparison with other methods demonstrate the effectiveness and robustness of this analytic approach. As more social effects are becoming important considerations in power system management, the goal of situation awareness should be expanded to also include achievements in social benefits. The second approach investigates the concept and application of social energy upon the University of Denver campus grid to provide management improvement solutions for optimizing social cost. Social element - human working productivity cost, and economic element - electricity consumption cost, are both considered in the evaluation of overall social cost. Moreover, power system simulation, numerical experiments for smart building modeling, distribution level real-time pricing and social response to the pricing signals are studied for implementing the interactive artificial-physical management scheme

Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review

The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and disadvantages are discussed