1,083 research outputs found

    Decentralized robust dynamic state estimation in power systems using instrument transformers

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    This paper proposes a decentralized method for estimation of dynamic states of a power system. The method remains robust to time-synchronization errors and high noise-levels in measurements. Robustness of the method has been achieved by incorporating internal angle in the dynamic model used for estimation and by decoupling the estimation process into two stages with continuous updation of measurement-noise variances. Additionally, the proposed estimation method does not need measurements obtained from phasor measurement units (PMUs); instead, it just requires analogue measurements of voltages and currents directly acquired from instrument transformers. This is achieved through statistical signal processing of analogue voltages and currents to obtain their magnitudes and frequencies, followed by application of unscented Kalman filtering for nonlinear estimation. The robustness and feasibility of the method have been demonstrated on a benchmark power system model

    Derivative-free Kalman filtering based approaches to dynamic state estimation for power systems with unknown inputs

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    This paper proposes a decentralized derivative-free dynamic state estimation method in the context of a power system with unknown inputs, to address cases when system linearisation is cumbersome or impossible. The suggested algorithm tackles situations when several inputs, such as the excitation voltage, are characterized by uncertainty in terms of their status. The technique engages one generation unit only and its associated measurements, and it remains totally independent of other system wide measurements and parameters, facilitating in this way the applicability of this process on a decentralized basis. The robust- ness of the method is validated against different contingencies. The impact of parameter errors, process and measurement noise on the unknown input estimation performance is discussed. This understanding is further supported through detailed studies in a realistic power system model

    Decentralized nonlinear control for power systems using normal forms and detailed models

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    This paper proposes a decentralized method for nonlinear control of oscillatory dynamics in power systems. The method is applicable for ensuring both transient stability as well as small-signal stability. The method uses an optimal control law which has been derived in the general framework of nonlinear control using normal forms. The model used to derive the control law is the detailed subtransient model of synchronous machines as recommended by IEEE. Minimal approximations have been made in either the derivation or the application of the control law. The developed method also requires the application of dynamic state estimation technique. As the employed control and estimation schemes only need local measurements, the method remains completely decentralized. The method has been demonstrated as an effective tool to prevent blackouts by simulating a major disturbance in a benchmark power system model and its subsequent control using the proposed method

    Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

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    This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community

    Decentralized estimation and control for power systems

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    This thesis presents a decentralized alternative to the centralized state-estimation and control technologies used in current power systems. Power systems span over vast geographical areas, and therefore require a robust and reliable communication network for centralized estimation and control. The supervisory control and data acquisition (SCADA) systems provide such a communication architecture and are currently employed for centralized estimation and control of power systems in a static manner. The SCADA systems operate at update rates which are not fast enough to provide appropriate estimation or control of transient or dynamic events occurring in power systems. Packet-switching based networked control system (NCS) is a faster alternative to SCADA systems, but it suffers from some other problems such as packet dropouts, random time delays and packet disordering. A stability analysis framework for NCS in power systems has been presented in the thesis considering these problems. Some other practical limitations and problems associated with real-time centralized estimation and control are computational bottlenecks, cyber threats and issues in acquiring system-wide parameters and measurements. The aforementioned problems can be solved by a decentralized methodology which only requires local parameters and measurements for estimation and control of a local unit in the system. The cumulative effect of control at all the units should be such that the global oscillations and instabilities in the power system are controlled. Such a decentralized methodology has been presented in the thesis. The method for decentralization is based on a new concept of `pseudo-inputs' in which some of the measurements are treated as inputs. Unscented Kalman filtering (UKF) is applied on the decentralized system for dynamic state estimation (DSE). An extended linear quadratic regulator (ELQR) has been proposed for the optimal control of each local unit such that the whole power system is stabilized and all the oscillations are adequately damped. ELQR requires DSE as a prerequisite. The applicability of integrated system for dynamic estimation and control has been demonstrated on a model 16-machine 68-bus benchmark system

    Decentralized operation and control of integrated transactive and physical grids

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    The objective of this research is to develop a decentralized grid architecture to manage the physical and transactive aspects of power systems. With exponentially decreasing prices of PV the adoption of volatile and non-dispatchable sources into the grid has increased. This has two key impacts. Physical phenomenon like congestion of power flow corridors and voltage volatility become more prevalent. Similarly, with increasing prosumers, a multi-agent system is created, with each asset owner wanting to transact power. Existing transactive and physical control solutions are centralized, rely on low-latency communications, often require detailed knowledge of network topologies and are often highly coupled. The proposed research showcases fast localized grid control solutions in the form of hybrid transformers to manage physical phenomenon like congestion and voltage volatility. Furthermore, a decentralized, communication-free and topology-agnostic real-time pricing mechanism is proposed to enable collective stabilization even under wide variations in available generation. Thus, an architecture is presented where the transactive and physical grid constraints are handled in a decoupled fashion while being integrated through the physics of the network.Ph.D
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