Optimal placement of phasor measurement units using the Advanced Matrix Manipulation algorithm

Includes abstract.Includes bibliographical references.This thesis investigates the problem of the Optimal Placement scheme of Phasor Measurement Units in electrical power systems for State Estimation to facilitate improved monitoring and control of the system parameters. The research work done for this thesis begins with review of Supervisory Control and Data Acquisition systems (SCADA). SCADA-based systems are currently employed for condition monitoring and control of industrial and utility electrical power systems. For utility power networks, the main problem with voltage and current phasor data captured by SCADA systems is that they are not synchronised with respect to each other in a present-time or Real-time framework. This implies that both magnitude and phase angle of the measured phasors tend to get affected by slow data flow provided by SCADA to the points of utilization and also by differences in time instants of data capture. These factors inhibit theefficiency and quality of the power system monitoring and control. “Phasor Measurement Unit” (PMU) is a relatively new technology that, when employed in power networks, offers real-time synchronised measurements of the voltages at buses and currents along the lines that connect them. This is accomplished by using a GPS based monitoring system which facilitates time synchronisation of measurements and unlike SCADA, makes the measured data available in Real-Time format. SCADA is not able to provide Real-time data due to the low speeds at which RTUs (Remote Terminal Units) provide data. Availability of time-stamped phasor measurements makes PMUs preferable for power system monitoring and control applications such as State Estimation, Instability Prediction Analysis, Real-time Monitoring of the system conditions, Islanding Detection, System Restoration and Bad Data Detection

Restoration of an active MV distribution grid with a battery ESS: A real case study

In order to improve power system operation, Battery Energy Storage Systems (BESSs) have been installed in high voltage/medium voltage stations by Distribution System Operators (DSOs) around the world. Support for restoration of MV distribution networks after a blackout or HV interruption is among the possible new functionalities of BESSs. With the aim to improve quality of service, the present paper investigates whether a BESS, installed in the HV/MV substation, can improve the restoration process indicators of a distribution grid. As a case study, an actual active distribution network of e-distribuzione, the main Italian DSO, has been explored. The existing network is located in central Italy. It supplies two municipalities of approximately 10,000 inhabitants and includes renewable generation plants. Several configurations are considered, based on: the state of the grid at blackout time; the BESS state of charge; and the involvement of Dispersed Generation (DG) in the restoration process. Three restoration plans (RPs) have been defined, involving the BESS alone, or in coordination with DG. A MATLAB®/Simulink® program has been designed to simulate the restoration process in each configuration and restoration plan. The results show that the BESS improves restoration process quality indicators in different simulated configurations, allowing the operation in controlled island mode of parts of distribution grids, during interruptions or blackout conditions. The defined restoration plans set the priority and the sequence of controlled island operations of parts of the grid to ensure a safe and better restoration. In conclusion, the results demonstrate that a BESS can be a valuable element towards an improved restoration procedure

Stochastic Model for Power Grid Dynamics

We introduce a stochastic model that describes the quasi-static dynamics of an electric transmission network under perturbations introduced by random load fluctuations, random removing of system components from service, random repair times for the failed components, and random response times to implement optimal system corrections for removing line overloads in a damaged or stressed transmission network. We use a linear approximation to the network flow equations and apply linear programming techniques that optimize the dispatching of generators and loads in order to eliminate the network overloads associated with a damaged system. We also provide a simple model for the operator's response to various contingency events that is not always optimal due to either failure of the state estimation system or due to the incorrect subjective assessment of the severity associated with these events. This further allows us to use a game theoretic framework for casting the optimization of the operator's response into the choice of the optimal strategy which minimizes the operating cost. We use a simple strategy space which is the degree of tolerance to line overloads and which is an automatic control (optimization) parameter that can be adjusted to trade off automatic load shed without propagating cascades versus reduced load shed and an increased risk of propagating cascades. The tolerance parameter is chosen to describes a smooth transition from a risk averse to a risk taken strategy...Comment: framework for a system-level analysis of the power grid from the viewpoint of complex network

Decision aid function for restoration of transmission power systems after a blackout

This thesis, based on a project realised in cooperation with Électricité de France (EDF), proposes a new concept for a Decision Aid Function FOr Restoration (DAFFOR) of transmission power systems after a blackout. DAFFOR is an interactive computer tool which provides the operators in power system control centres with guidance concerning the actions to execute during the restoration, in real-time conditions. In other words, it takes into account the real-time state of the power system, including the unforeseen events that may happen during the restoration. Since time is a limiting factor and the decision making is a highly combinatorial problem, a knowledge-based system is proposed in order to solve it. The restoration process can be decomposed into two main stages. The first one, skeleton creation, consists of starting the production units and connecting some transmission devices in order to energize a strong network. The second stage, load pickup, aims to supply the consumers. In DAFFOR, EDF's strategy for the first restoration stage has been implemented, and a new strategy for the load pickup stage has been proposed and implemented in the form of rules. The above restoration strategies represent DAFFOR's knowledge, which has been enhanced with a number of heuristics. DAFFOR consists of two kernels: the Reasoning kernel and the Real Time Update kernel. The Reasoning kernel has the task of assisting the operator during the restoration process and is the interactive guidance part of DAFFOR. It can either suggest a control action to execute on the power system to the operators or assess a control action provided by the operators. The control action is suggested with respect to operating limits (over- and under-voltages, frequency excursions and overloads) and according to knowledge (restoration strategy and heuristics). The feasibility of an action is tested within an internal dynamic simulator, which also takes into account the time necessary to physically execute an action (e.g., telephone a person in the field). The Reasoning kernel can adapt its operation via data generated by the Real Time Update (RTUpd) kernel. The RTUpd kernel steadily reads real-time power system data from System Control and Data Acquisition (SCADA) function and those entered by the operators (if unavailable from SCADA). It generates a coherent data set, which is the only real-time information available to the Reasoning kernel, and the message which indicates to the Reasoning kernel how to continue its operation. In addition to the real-time data, the RTUpd kernel has two feedback inputs internal to DAFFOR: a coherent data set generated in the previous data processing by the RTUpd kernel itself, and a simulated data set generated by the Reasoning kernel (i.e., its internal dynamic simulator). With these three inputs, the RTUpd kernel generates the current image of the power system, and identifies unforeseen events. Thanks to the RTUpd kernel, the Reasoning kernel may keep up with the dynamic evolution of the power system. The stand-alone prototype of DAFFOR has been tested with data provided by EDF, and shown very good efficiency. At present, it is about to be coupled with the EDF's operator training simulator in order to test its real-time functionality. This work also proposes an original method aimed at the determination of a strategy for the load pickup stage. A genetic algorithm has been developed which generates the optimized sequences of manoeuvres for different initial states of the power system for the second restoration stage. It uses the dynamic simulator as its evaluation function. The obtained results have shown that some additional manipulations should be done in order to deduce generic rules for the load pickup strategy. At present, the obtained sequences are classified in a decision tree, which permits the most adequate sequence for the initial state to be chosen