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Various sensors distributed across different parts of the electric power grid provide measurements to the control center operator for situational awareness of the system. Voltage transformer, current transformer, relay and phasor measurement units (PMU) are types of sensors for power system monitoring. The utilities monitor the operating condition of their system by processing the measurements received from these various sensors using a state estimator. A state estimator refines these measurements, compensates for any lost data and provides a snapshot of the power system. The operator at the control center does further analysis using energy management system tools based on the most recent data and required state of the system. The electric power grid is vulnerable to blackouts caused by physical disturbances, human errors and external disasters. These disturbances can also cause loss of data, sensor failure or communication link failure. This research work focuses on comparing state estimation algorithms with loss of measurement data. The measurements are assumed to be lost as clustered and scattered data sets. Weighted Least Square (WLS), Least Absolute Value (LAV) and Iteratively Reweighted Least Squares (IRLS) implementation of Weighted Least Absolute Value (WLAV) algorithms are compared for state estimation with clustered and scattered loss of data. These algorithms are tested on a six bus, IEEE 30 bus and 137 bus utility test cases. The test results indicate the best possible algorithm in several considered scenarios based on an error index. Additionally, phasor measurements data are included in two of the state estimation algorithms to study their ability to mitigate the loss of measurement data.
Various sensors distributed across different parts of the electric power grid provide measurements to the control center operator for situational awareness of the system. Voltage transformer, current transformer, relay and phasor measurement units (PMU) are types of sensors for power system monitoring. The utilities monitor the operating condition of their system by processing the measurements received from these various sensors using a state estimator. A state estimator refines these measurements, compensates for any lost data and provides a snapshot of the power system. The operator at the control center does further analysis using energy management system tools based on the most recent data and required state of the system. The electric power grid is vulnerable to blackouts caused by physical disturbances, human errors and external disasters. These disturbances can also cause loss of data, sensor failure or communication link failure. This research work focuses on comparing state estimation algorithms with loss of measurement data. The measurements are assumed to be lost as clustered and scattered data sets. Weighted Least Square (WLS), Least Absolute Value (LAV) and Iteratively Reweighted Least Squares (IRLS) implementation of Weighted Least Absolute Value (WLAV) algorithms are compared for state estimation with clustered and scattered loss of data. These algorithms are tested on a six bus, IEEE 30 bus and 137 bus utility test cases. The test results indicate the best possible algorithm in several considered scenarios based on an error index. Additionally, phasor measurements data are included in two of the state estimation algorithms to study their ability to mitigate the loss of measurement data.
After the deregulation of the power industry, power systems are required to be operated efficiently and economically in today's strongly competitive environment. In order to achieve these objectives, it is crucial for power system control centers to accurately monitor the system operating state. State estimation is an essential tool in an energy management system (EMS). It is responsible for providing an accurate and correct estimate for the system operating state based on the available measurements in the power system. A robust state estimation should have the capability of keeping the system observable during different contingencies, as well as detecting and identifying the gross errors in measurement set and network topology. However, this capability relies directly on the system network configuration and measurement locations. In other words, a reliable and redundant measurement system is the primary condition for a robust state estimation. This dissertation is focused on the possible benefits to state estimation of using synchronized phasor measurements to improve the measurement system. The benefits are investigated with respect to the measurement redundancy, bad data and topology error processing functions in state estimation. This dissertation studies how to utilize the phasor measurements in the traditional state estimation. The optimal placement of measurement to realize the maximum benefit is also considered and practical algorithms are designed. It is shown that strategic placement of a few phasor measurement units (PMU) in the system can significantly increase measurement redundancy, which in turn can improve the capability of state estimation to detect and identify bad data, even during loss of measurements. Meanwhile, strategic placement of traditional and phasor measurements can also improve the state estimation's topology error detection and identification capability, as well as its robustness against branch outages. The proposed procedures and algorithms are illustrated and demonstrated with different sizes of test systems. And numerical simulations verify the gained benefits of state estimation in bad data processing and topology error processing.
State Estimation plays a significant role in power systems secure operation. It performs real time monitoring of the entire system and provides the system states to other security applications. Recently, the Phasor Measurement Units (PMUs) have been invented and deployed to power systems to provide both phasor magnitudes and phase angles. This research focuses on enhancing power system state estimation and parameter error identification through innovative use of phasor measurements. The first part of the dissertation focuses on improving network parameter error identification through innovative use of phasor measurements. Previous work has shown that the parameter errors in certain topologies could not be detected or identified without incorporating phasor measurements. This dissertation firstly investigates a computationally efficient algorithm to identify all such topologies for a given system. Then a strategic phasor measurement placement is proposed to ensure detectability and identifiability of certain network parameter errors. In addition, this method is reformulated and extended to detect and identify isolated power islands after disturbances. Another way to improve parameter error identification is to use multiple measurement scans instead of the normal single measurement scan. This dissertation investigates an alternative approach using multiple measurement scans. It addresses limitations for parameter error in certain topologies without investing new measurements. The second part of the dissertation concentrates on interoperability of PMUs in state estimation. Incorporating phasor measurements into existing Weighted Least Squares (WLS) state estimation brings up the interoperability issue about how to choose the right measurement weights for different types of PMUs. This part develops an auto tuning algorithm which requires no initial information about the phasor measurement accuracies. This algorithm is applied to tune the state estimator to update the weights of different types of PMUs in order to have a consistent numerically stable estimation solution. Furthermore, the impact of this tuning method on bad measurement detection is investigated. All these methods have been tested in IEEE standard systems to show their performance.
The world-wide application of Phasor Measurement Units (PMUs) brings great benefit to power system state estimation. The synchronised measurements from PMUs can increase estimation accuracy, synchronise states among different systems, and provide greater applicability of state estimation in the transient condition. However, the integration of synchronised measurements with state estimation can introduce efficiency problems due to the substantial burden of data. The research is divided into two parts: finding a solution to cope with the computational efficiency problem and developing a transient state estimation algorithm based on synchronised measurements from PMUs. The computational efficiency problems constitute important considerations in the operation of state estimation. To improve the low computational efficiency, two distributed algorithms are proposed in Chapters 4 and 5. In these two algorithms, the modelling, structure, and solution are described, and the corresponding procedures of bad data processing are presented. Numerical results on the IEEE 30-bus, 118-bus and 300-bus systems can verify the effectiveness of the two proposed algorithms. A novel transient state estimation algorithm based on synchronised measurements is proposed in Chapter 6. Considering the scanning cycle and sampling rate of PMU measurements, the proposed algorithm can estimate transient states in a practical way. The performance of the proposed algorithm is demonstrated in a transient simulation on the IEEE 14-bus system.
The Phasor Measurement Units (PMUs) are high speed sensing devices which can measure changes in voltage, current, frequency, and rate of change of frequency in the power grid. The measurement accuracy and performance of the PMU mainly depends on their internal instrumentation and estimation algorithms. The purpose of this thesis is to provide a concise reference for instrumentation inside the PMU and evaluate some of the widely accepted estimation algorithms, in light if the recent IEEE standards on synchrophasor measurement. There are two classes of PMUs, (i) P-Class and (ii) M-Class, depending on their primary utility of action in operations. We discuss the implementation of different P & M class estimation algorithms and present a comparative performance assessment. We also evaluate the dynamic performance of PMU estimation algorithms in presence of grid disturbances, specifically faults. At the end, the thesis discusses the challenges in phasor and frequency estimation, recommends the most appropriate techniques and summarizes future work.
The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.
State Estimation in Electric Power Systems: A Generalized Approach provides for the first time a comprehensive introduction to the topic of state estimation at an advanced textbook level. The theory as well as practice of weighted least squares (WLS) is covered with significant rigor. Included are an in depth analysis of power flow basics, proper justification of Stott's decoupled method, observability theory and matrix solution methods. In terms of practical application, topics such as bad data analysis, combinatorial bad data analysis and multiple snap shot estimation are covered. The book caters both to the specialist as well as the newcomer to the field. State estimation will play a crucial role in the emerging scenario of a deregulated power industry. Many market decisions will be based on knowing the present state of the system accurately. State Estimation in Electric Power Systems: A Generalized Approach crystallizes thirty years of WLS state estimation theory and practice in power systems and focuses on techniques adopted by state estimation developers worldwide. The book also reflects the experience of developing industrial-grade state estimation software that is used in the USA, South America, and many other places in world.
This book presents selected articles from INDIA SMART GRID WEEK (ISGW 2017), which is the third edition of the Conference cum Exhibition on Smart Grids and Smart Cities, organized by India Smart Grid Forum from 07-10 March 2017 at Manekshaw Centre, Dhaula Kuan, New Delhi, India. ISGF is a public private partnership initiative of the Ministry of Power, Govt. of India with the mandate of accelerating smart grid deployments across the country. This book gives current scenario updates of Indian power sector business. It also highlights various disruptive technologies for power sector business.