Modemless Multiple Access Communications Over Powerlines for DC Microgrid Control

Angjelichinoski, Marko; Stefanović, Čedomir; Popovski, Petar

doi:10.1007/978-3-319-51376-8_3

Cited by 2 publications

(7 citation statements)

References 19 publications

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“…The expressions (35) and (36) can be verified to be asymptotically tight; it can be shown that if Algorithm 1 converges to the global optimum, the MSE matrix is of the same analytical form as (35) and (36), but evaluated atθ −n . Conversely, expressions (35) and (36) prove the asymptotic efficiency of Algorithm 1.…”

Section: E Performancementioning

confidence: 99%

“…11 depicts the performance of J-SISE for each of the constituent vectors of θ −n , i.e., g −n , d and ψ against the corresponding lower bounds, for N = 6 DERs. We have evaluated the lower bounds using both, the constrained CRLB (34) and expression (35) from Proposition 4, and they both yield numerically identical results. Empty markers correspond to the initial estimate that initializes Algorithm 1, obtained via (32), while filled markers correspond toθ −n after Algorithm 1 converges.…”

Section: B J-sise Performancementioning

confidence: 99%

“…We conclude that, identifying the individual components of the loads with satisfactory performance might require excessive (even prohibitive) training epoch durations to suppress the noise. However, in many upper layer applications, detailed knowledge on the individual load component demands is not necessary and knowing only the total bus demand d n = d ca n + d cc n + d cp n is sufficient [17], [35]; in such case, an estimate of the total load demand vector d = [d 1 , . .…”

Section: B J-sise Performancementioning

confidence: 99%

“…Substituting the above in (99) gives expression (35). To bound the MSE matrix of vec(V), we use (95), i.e., the fact that vec(V) is a transformed version of θ and apply the corresponding CRLB formula, i.e.…”

Section: Appendix F Proof Of Proposition IVmentioning

confidence: 99%

“…The controllers collect the local measurements of the state sequence and modulate them into the perturbation signals, see Section V-C. In other words, the relation between the primary control perturbation signals and the induced state deviations is interpreted as the input-output relation of an implicit communication channel [31]- [35], through which the controllers exchange their local observations. Hence, the training sequences are used both for generating multiple states and communicating the local state observations.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

See 4 more Smart Citations

Decentralized DC Microgrid Monitoring and Optimization via Primary Control Perturbations

Angjelichinoski

Scaglione

Popovski

et al. 2018

IEEE Trans. Signal Process.

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We treat the emerging power systems with direct current (DC) MicroGrids, characterized with high penetration of power electronic converters. We rely on the power electronics to propose a decentralized solution for autonomous learning of and adaptation to the operating conditions of the DC Mirogrids; the goal is to eliminate the need to rely on an external communication system for such purpose. The solution works within the primary droop control loops and uses only local bus voltage measurements. Each controller is able to estimate (i) the generation capacities of power sources, (ii) the load demands, and (iii) the conductances of the distribution lines. To define a well-conditioned estimation problem, we employ decentralized strategy where the primary droop controllers temporarily switch between operating points in a coordinated manner, following amplitude-modulated training sequences. We study the use of the estimator in a decentralized solution of the Optimal Economic Dispatch problem. The evaluations confirm the usefulness of the proposed solution for autonomous MicroGrid operation.The proposed solution is illustrated in Fig. 1. We consider a generic DC MG model with multiple buses, described in Sections III and IV. We assume that the MG does not have access to reliable external communication resources. The physical state of DC MGs is characterized by the steady state bus voltages. We introduce a parameter vector θ that collects all system variables whose values are determined by exogenous influences; this includes the generation capacities of the DERs, the load demands and the distribution network topology, i.e., the conductance matrix, see Section IV-A. Using the power balance equation, we represent the bus voltages thorough a non-linear and implicit model, parametrized by θ, see Section III-B. Evidently, θ varies with time; to respond to its variations on different time scales, the DC MG is governed by a hierarchical control system, comprising primary and upper control layer. The primary control is decentralized: several controllers regulate the bus voltages, using only local feedbacks without exchanging any information with peer controllers. They are very fast and capable of responding to high frequency variations in θ. Popular primary controller in DC MGs is the Voltage Source Converter (VSC) with voltage droop control, which is reminiscent to the widespread frequency droop control in AC systems, but defined over the DC voltage; it is therefore standard practice to refer to it simply as droop controller [2], [3]. The upper control layer, on the other hand, responds to less frequent changes in θ that affect the global behavior of the system; examples include changes of the load/generation profile, faults, attacks, etc. Its main role is to adapt the system to the new conditions by computing updated optimal control references for the primary controllers; all upper layer control applications require full/partial knowledge of θ to determine the control references that adequately reflect the new conditions [15]...

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Section: E Performancementioning

confidence: 99%

Section: B J-sise Performancementioning

confidence: 99%