A cloud-based smart metering infrastructure for distribution grid services and automation

© 2017 The Authors The evolution of the power systems towards the smart grid paradigm is strictly dependent on the modernization of distribution grids. To achieve this target, new infrastructures, technologies and applications are increasingly required. This paper presents a smart metering infrastructure that unlocks a large set of possible services aimed at the automation and management of distribution grids. The proposed architecture is based on a cloud solution, which allows the communication with the smart meters from one side and provides the needed interfaces to the distribution grid services on the other one. While a large number of applications can be designed on top of the cloud, in this paper the focus will be on a real-time distributed state estimation algorithm that enables the automatic reconfiguration of the grid. The paper will present the key role of the cloud solution for obtaining scalability, interoperability and flexibility, and for enabling the integration of different services for the automation of the distribution system. The distributed state estimation algorithm and the automatic network reconfiguration will be presented as an example of coordinated operation of different distribution grid services through the cloud

Power System State Estimation In Large-Scale Networks

Power system state estimation constitutes one of the critical functions that are executed at the control centers. Its optimal performance is required in order to operate the power system in a safe, secure and economic manner. State estimators (SE) process the available measurements by taking into account the information about the network model and parameters. The quality of estimated results will depend on the measurements, the assumed network model and its parameters. Hence. SE requires to use various techniques to ensure validity of the results and to detect and identify sources of errors. The Weighted Least Squares (WLS) method is the most popular technique of SE. This thesis provides solutions to enhance the WLS algorithm in order to increase the performance of SE. The gain and the Jacobian matrices associated with the basic algorithm require large storage and have to be evaluated at every iteration, resulting in more computation time. The elements of the SE Jacobian matrix are processed one-by-one based on the available measurements, and the Jacobian matrix, H is updated suitably, avoiding all the power flow equations. thus simplifying the development of the Jacobian. The results obtained proved that the suggested method takes lesser computational time compared with the available NRSE method, particularly when the size of the network becomes larger. The uncertainty in analog measurements could occur in a real time system. Thus, the higher weighting factor or wrongly assigned weighting factor to the measurement could lead to flag the measurements as bad. This thesis describes a pre-screening process to identify the bad measurements and the measurement weights before performing the WLS estimation technique employed in SE. The autoregressive (AR) techniques. Burg and Modified Covariance (MC), are used to predict the data and at the same time filtering the logical weighting factors that have been assigned to the identified bad measurements. The results show that AR methods managed to accurately predict the data and filter the weigthage factors for the bad measurements. Also the WLS algorithm is modified to include Unified Power Flow Controller (UPFC) parameters. The developed methods are successfully tested on IEEE standard systems and the Sabah Electricy Sdn. Bhd. (SESB) system without and with UPFC. The developed program is suitable either to estimate the UPFC controller parameters or to estimate these parameter values in order to achieve the given control specifications in addition to the power system state variables

Temporally adaptive monitoring procedures with applications in enterprise cyber-security

Due to the perpetual threat of cyber-attacks, enterprises must employ and develop new methods of detection as attack vectors evolve and advance. Enterprise computer networks produce a large volume and variety of data including univariate data streams, time series and network graph streams. Motivated by cyber-security, this thesis develops adaptive monitoring tools for univariate and network graph data streams, however, they are not limited to this domain. In all domains, real data streams present several challenges for monitoring including trend, periodicity and change points. Streams often also have high volume and frequency. To deal with the non-stationarity in the data, the methods applied must be adaptive. Adaptability in the proposed procedures throughout the thesis is introduced using forgetting factors, weighting the data accordingly to recency. Secondly, methods applied must be computationally fast with a small or fixed computation burden and fixed storage requirements for timely processing. Throughout this thesis, sequential or sliding window approaches are employed to achieve this. The first part of the thesis is centred around univariate monitoring procedures. A sequential adaptive parameter estimator is proposed using a Bayesian framework. This procedure is then extended for multiple change point detection, where, unlike existing change point procedures, the proposed method is capable of detecting abrupt changes in the presence of trend. We additionally present a time series model which combines short-term and long-term behaviours of a series for improved anomaly detection. Unlike existing methods which primarily focus on point anomalies detection (extreme outliers), our method is capable of also detecting contextual anomalies, when the data deviates from persistent patterns of the series such as seasonality. Finally, a novel multi-type relational clustering methodology is proposed. As multiple relations exist between the different entities within a network (computers, users and ports), multiple network graphs can be generated. We propose simultaneously clustering over all graphs to produce a single clustering for each entity using Non-Negative Matrix Tri-Factorisation. Through simplifications, the proposed procedure is fast and scalable for large network graphs. Additionally, this methodology is extended for graph streams. This thesis provides an assortment of tools for enterprise network monitoring with a focus on adaptability and scalability making them suitable for intrusion detection and situational awareness.Open Acces

A Distributed Dynamic State Estimator Using Cellular Computational Network

The proper operation of smart grid largely depends on the proper monitoring of the system. State estimation is a core computation process of the monitoring unit. To keep the privacy of the data and to avoid the unexpected events of the system, it needs to be made fast, distributed, and dynamic. The traditional Weighted Least Squares (WLS) estimator is neither scalable, nor distributed. Increase in the size of the system increases the computation time significantly. The estimator can be made faster in different ways. One of the major solutions can be its parallel implementation. As the WLS estimator is not completely parallelizable, the dishonest Gauss Newton method is analyzed in this dissertation. It is shown that the method is fully parallelizable that yields a very fast result. However, the convergence of the dishonest method is not analyzed in the literature. Therefore, the nature of convergence is analyzed geometrically for a single variable problem and it is found that the method can converge on a higher range with higher slopes. The effects of the slopes on multi-variable cases are demonstrated through simulation. On the other hand, a Cellular Computational Network (CCN) based framework is analyzed for making the system distributed and scalable. Through analysis, it is shown that the framework creates an independent method for state estimation. To increase the accuracy, some heuristic methods are tested and a Genetic Algorithm (GA) based solution is incorporated with the CCN based solution to build a hybrid estimator. However, the heuristic methods are time-consuming and they do not exploit the advantage of the dynamic nature of the states. With the high data-rate of phasor measurement units, it is possible to extract the dynamic natures of the states. As a result, it is also possible to make efficient predictions about them. Under this situation, a predictor can be incorporated with the estimation process to detect any unwanted changes in the system. Though it is not a part of the power system to date, it can be a tool that can enhance the reliability of the grid. To implement the predictors, a special type of neural network named Elman Recurrent Neural Network (ERNN) is used. In this dissertation, a distributed dynamic estimator is developed by integrating an ERNN based predictor with a dishonest method based estimator. The ERNN based predictor and the dishonest method based estimator are each implemented at the cell level of a CCN framework. The estimation is a weighted combination of the dishonest module and the predictor module. With this three-stage distributed computation system, it creates an efficient dynamic state estimator. The proposed distributed method keeps the privacy and speed of the estimation process and enhances the reliability of the system. It fulfills the requirements of the deregulated energy market. It is also expected to meet the future needs of the smart grid

Multi-agent system based active distribution networks

This thesis gives a particular vision of the future power delivery system with its main requirements. An investigation of suitable concepts and technologies which creates a road map forward the smart grid has been carried out. They should meet the requirements on sustainability, efficiency, flexibility and intelligence. The so called Active Distribution Network (ADN) is introduced as an important element of the future power delivery system. With an open architecture, the ADN is designed to integrate various types of networks, i.e., MicroGrid or Autonomous Network, and different forms of operation, i.e., islanding or interconnection. By enabling an additional local control layer, these so called cells are able to reconfigure, manage local faults, support voltage regulation, or manage power flow. Furthermore, the Multi-Agent System (MAS) concept is regarded as a potential technology to cope with the anticipated challenges of future grid operation. Analysis of benefits and challenges of implementing MAS shows that it is a suitable technology for a complex and highly dynamic operation and open architecture as the ADN. By taking advantages of the MAS technology, the AND is expected to fully enable distributed monitoring and control functions. This MAS-based ADN focuses mainly on control strategies and communication topologies for the distribution systems. The transition to the proposed concept does not require an intensive physical change to the existing infrastructure. The main point is that inside the MAS-based ADN, loads and generators interact with each other and the outside world. This infrastructure can be built up of several cells (local areas) that are able to operate autonomously by an additional agent-based control layer. The ADN adapts a MAS hierarchical control structure in which each agent handles three functional layers of management, coordination, and execution. In the operational structure, the ADN addresses two main function parts: Distributed State Estimation (DSE) to analyze the network topology, compute the state estimation, and detect bad data; and Local Control Scheduling (LCS) to establish the control set points for voltage coordination and power flow management. Under the distributed context of the controls, an appropriate method for DSE is proposed. The method takes advantage of the MAS technology to compute iteratively the local state variables through neighbor data measurements. Although using the classical Weighted Least Square (WLS) as a core, the proposed algorithm based on an agent environment distributes drastically computation burden to subtasks of state estimation with only two interactive buses and an interconnection line in between. The accuracy and complexity of the proposed estimation are investigated through both off-line and on-line simulations. Distributed and parallel working of processors improves significantly the computation time. This estimation is also suitable for a meshed configuration of the ADN, which includes more than one interconnection between each pair of the cells. Depending on the availability of a communication infrastructure, it is able to work locally inside the cells or globally for the whole ADN. As a part of the LCS, the voltage control function is investigated in both steady-state and dynamic environments. The autonomous voltage control within each network area (cell) can be deployed by a combination of active and reactive power support of distributed generation (DG). The coordinated voltage control defines the optimal tap setting of the on-load tap changer (OLTC) while comparing amounts of control actions in each area. Based on the sensitivity factors, these negotiations are thoroughly supported in the distributed environment of the MAS platform. To verify the proposed method, both steady-state and dynamic simulations are developed. Simulation results show that the proposed function helps to integrate more DG while mitigating voltage violation effectively. The optimal solution can be reached within a small number of calculation iterations. It opens a possibility to apply the proposed method as an on-line application. Furthermore, a distributed approach for the power flow management function is developed. By converting the power network to a represented graph, the optimal power flow is understood as the well-known minimum cost flow problem. Two fundamental solutions for the minimum cost flow, i.e., the Successive Shortest Path (SSP) algorithm and the Cost-Scaling Push-Relabel (CS-PR) algorithm, are introduced. The SSP algorithm is augmenting the power flow along the shortest path until reaching the capacity of at least one edge. After updating the flow, it finds another shortest path and augments the flow again. The CS-PR algorithm approaches the problem in a different way which is scaling cost and pushing as much flow as possible at each active node. Simulations of both meshed and radial test networks are developed to compare their performances in various network conditions. Simulation results show that the two methods can allow both generation and power flow controller devices to operate optimally. In the radial test network, the CS-PR needs less computation effort represented by a number of exchanged messages among the MAS platform than the SSP. Their performances in the meshed network are, however, almost the same. Last but not least, this novel concept of MAS-based AND is verified under a laboratory environment. The lab set-up separates some local network areas by using a three-inverter system. The MAS platform is created on different computers and is able to retrieve data from and to hardware components, i.e., the three-inverter system. In this set-up, a configuration of the power router is established in a combination of the three-inverter system with the MAS platform. Three control functions of the inverters, AC voltage control, DC bus voltage control, and PQ control, are developed in a Simulink diagram. By assigning suitable operation modes for the inverters, the set-up successfully experiments on synchronizing and disconnecting a cell to the rest of the grid. In the MAS platform, an obvious power routing strategy is executed to optimally manage power flow in the lab set-up. The results show that the proposed concept of the ADN with the power router interface works well and can be used to manage electrical networks with distributed generation and controllable loads, leading to active networks