دانلود کتاب برآورد وضعیت سیستم توزیع برق

Electrical power systems are experiencing huge transformations in present time. New renewable energy sources with intermittent behavior and connected to the system by inverters, both centralized or distributed, are assuming a predominant role in the generation expansion, creating new challenges for the optimal dispatch and control of the interconnected systems. The process of digitalization introduced by the deployment of sophisticated information processing and communication systems, associated with new instrumentation and telemetry devices, has been offering new possibilities for supervision and control. Also, new characteristics of the load introduced by demand response and transports electrification have opened up new opportunities to better match production and consumption. State estimation has been accompanying the evolution of power systems since it was introduced in the early 1970s. Its importance has long been recognized as a fundamental component of the energy management systems. As the power system evolutes, it also must be improved to accommodate the systems transformations. The role of state estimation was initially aimed to track the evolution of the slow time varying “static state” resulting from the variations in load followed by the corresponding adjustments in generation. It was initially totally dedicated to the transmission system, particularly oriented to avoid catastrophic events in the power system, like the 1965 blackout occurred in the northeastern region of the USA. It was based on information obtained alongside the transmission system by the socalled SCADA system, a high latency system formed by remote units and dedicated telecommunication channels, supervised from a control center running dedicated and specialized hardware. Since its early days up to the beginning of this century, little has changed in its basic concepts except for a natural evolution on computation and telecommunication hardware as well as algorithmic improvements. New developments in state estimation methods have been reported in the literature in relation to the modern power system features. A main development has been in the application of state estimation to the distribution system. This power system segment was the most impacted by the new developments as it is the area where most to the modern technologies has been implemented. The main deployment of the Smart Grid technology, as well of Distributed Energy Resources, has been concentrated in the distribution segment, creating the conditions and the necessity of a more accurate tracking of the operating conditions of this part of the grid. The information gathered by Smart Meters and other sensors installed in the distribution level has opened up opportunities to improve the existing distribution state estimation algorithms and the development of completely new ones. Some of these developments deal with particular requirements of the distribution systems like the need for three-phase modeling, the different nature of the high, medium, and low voltage grids, and most importantly, the lack of redundancy in the real time information. In the transmission system, the spread use of Phasor Measurement Units (PMU) has proportioned the establishment of the wide area monitoring, protection, and control system, with a much accurate condition for the system situational awareness but introducing the challenge of mixing measurements with significant difference in sampling rates. Also, the high sampling rate available in PMUs offers the opportunity to development of highly effective state estimation algorithms able to incorporate the temporal evolution of the states, leading to the possibility of estimation of the power system dynamic behavior. Also, developments of artificial intelligence, particularly machine leaning methods, have been introducing Data Driven Methods to state estimation, initially applied to the pre-filtering process, but with potential to be extended to other phases of the state estimation process. Last, but not least, developments in high-performance computing have induced the research in Multiarea State Estimation to take advantage of the distributed and parallel computing environment. This book represents an important contribution to this new era of power system state estimation. It presents a thorough review of several new developments in the state estimation theory and practice and proposes new developments, some of them still in the research stages. The main emphasis of the book is in modeling and alternative solution methods for distribution system state estimation. Both conventional and newly proposed approaches to three-phase modeling and solution algorithms, as well as the treatment of new kind of information, like PMU and Smart Meter data, are extensively described.