A Belief Propagation Based Power Distribution System State Estimator

Hu, Ying; Kuh, Anthony; Yang, Tao; Kavcic, A.

doi:10.1109/mci.2011.941589

Cited by 83 publications

(67 citation statements)

References 24 publications

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“…Different methods, such as weighted least square (WLS) [34], Bayesian network [35], graph theory [36], and machine learning [37], are proposed for state estimation. (3) Volt/VAR management: voltage and reactive power management is essential for utilities to minimize power losses while maintaining an acceptable voltage profile along the distribution feeder under various loading conditions [38,39].…”

Section: Smart Metering Technologymentioning

confidence: 99%

Smart Distribution Systems

Jiang

Liu

2016

Energies

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Abstract:The increasing importance of system reliability and resilience is changing the way distribution systems are planned and operated. To achieve a distribution system self-healing against power outages, emerging technologies and devices, such as remote-controlled switches (RCSs) and smart meters, are being deployed. The higher level of automation is transforming traditional distribution systems into the smart distribution systems (SDSs) of the future. The availability of data and remote control capability in SDSs provides distribution operators with an opportunity to optimize system operation and control. In this paper, the development of SDSs and resulting benefits of enhanced system capabilities are discussed. A comprehensive survey is conducted on the state-of-the-art applications of RCSs and smart meters in SDSs. Specifically, a new method, called Temporal Causal Diagram (TCD), is used to incorporate outage notifications from smart meters for enhanced outage management. To fully utilize the fast operation of RCSs, the spanning tree search algorithm is used to develop service restoration strategies. Optimal placement of RCSs and the resulting enhancement of system reliability are discussed. Distribution system resilience with respect to extreme events is presented. Test cases are used to demonstrate the benefit of SDSs. Active management of distributed generators (DGs) is introduced. Future research in a smart distribution environment is proposed.

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Section: Smart Metering Technologymentioning

confidence: 99%

Smart Distribution Systems

Jiang

Liu

2016

Energies

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“…Since the state estimation algorithm is based on triangular factorization, the computational complexity is relatively low. The spatial-temporal correlation in the output of renewable energy sources can be captured via a belief propagation-based state estimation technique [29]. The belief propagation algorithm is similar to a message passing algorithm, which is used to compute marginal distributions.…”

Section: State Estimationmentioning

confidence: 99%

Stochastic Modeling and Optimization in a Microgrid: A Survey

2014

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The future smart grid is expected to be an interconnected network of small-scale and self-contained microgrids, in addition to a large-scale electric power backbone. By utilizing microsources, such as renewable energy sources and combined heat and power plants, microgrids can supply electrical and heat loads in local areas in an economic and environment friendly way. To better adopt the intermittent and weather-dependent renewable power generation, energy storage devices, such as batteries, heat buffers and plug-in electric vehicles (PEVs) with vehicle-to-grid systems can be integrated in microgrids. However, significant technical challenges arise in the planning, operation and control of microgrids, due to the randomness in renewable power generation, the buffering effect of energy storage devices and the high mobility of PEVs. The two-way communication functionalities of the future smart grid provide an opportunity to address these challenges, by offering the communication links for microgrid status information collection. However, how to utilize stochastic modeling and optimization tools for efficient, reliable and economic planning, operation and control of microgrids remains an open issue. In this paper, we investigate the key features of microgrids and provide a comprehensive literature survey on the stochastic modeling and optimization tools for a microgrid. Future research directions are also identified.

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“…The numerically ill-conditioned problem is successfully solved by the trust region method with quadratic regulation factorization, but the convergence problem still exists [11]. Recently, a belief propagation (BP)-based static state estimator for the IEEE 4-bus distribution system is proposed in [12]. However, the system states continuously change over time.…”

Section: A Related Workmentioning

confidence: 99%

Distributed State Estimation Using RSC Coded Smart Grid Communications

Rana

2015

IEEE Access

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Recently, the renewable distributed energy resources (DERs) have become more and more popular due to carbonfree energy sources and environment-friendly electricity generation. Unfortunately, these power generation patterns are mostly intermittent in nature and distributed over the electrical grid which creates challenging problems in the reliability of the smart grid. Thus, smart grid has a strong requisite for an efficient communication infrastructure to facilitate estimating the DER states. In contrast to the traditional methods of centralised state estimation, we propose a distributed approach to microgrid state estimation based on the concatenated coding structure. In this framework, the DER state is treated as a dynamic outer code and the recursive systematic convolutional (RSC) code is seen as a concatenated inner code for protection and redundancy in the system states. Furthermore, in order to properly monitor the intermittent energy source from any place, this paper proposes a distributed state estimation method. Particularly, the outputs of the local state estimation are treated as measurements which are fed into the master fusion station. At the end, the global state estimation can be obtained by combining local state estimations with corresponding weighting factors. The weighting factors can be calculated by inspiring the covariance intersection method. The simulation results show that the proposed method is able to estimate the system state properly.

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A Belief Propagation Based Power Distribution System State Estimator

Cited by 83 publications

References 24 publications

Smart Distribution Systems

Smart Distribution Systems

Stochastic Modeling and Optimization in a Microgrid: A Survey

Distributed State Estimation Using RSC Coded Smart Grid Communications

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