284,647 research outputs found
Load Frequency Control of Photovoltaic Generation-Integrated Multi-Area Interconnected Power Systems Based on Double Equivalent-Input-Disturbance Controllers
With the rapid increase of photovoltaic (PV) penetration and distributed grid access, photovoltaic generation (PVG)-integrated multi-area power systems may be disturbed by more uncertain factors, such as PVG, grid-tie inverter parameters, and resonance. These uncertain factors will exacerbate the frequency fluctuations of PVG integrated multi-area interconnected power systems. For such system, this paper proposes a load frequency control (LFC) strategy based on double equivalent-input-disturbance (EID) controllers. The PVG linear model and the multi-area interconnected power system linear model were established, respectively, and the disturbances were caused by grid voltage fluctuations in PVG subsystem and PV output power fluctuation and load change in multi-area interconnected power system. In PVG subsystems and multi-area interconnected power systems, two EID controllers add differently estimated equivalent system disturbances, which has the same effect as the actual disturbance, to the input channel to compensate for the impact of actual disturbances. The simulation results in MATLAB/Simulink show that the frequency deviation range of the proposed double EID method is 6% of FA-PI method and 7% of conventional PI method, respectively, when the grid voltage fluctuation and load disturbance exist. The double EID method can better compensate for the effects of external disturbances, suppress frequency fluctuations, and make the system more stable
Distributed Kalman Filters over Wireless Sensor Networks: Data Fusion, Consensus, and Time-Varying Topologies
Kalman filtering is a widely used recursive algorithm for optimal state estimation of linear stochastic dynamic systems. The recent advances of wireless sensor networks (WSNs) provide the technology to monitor and control physical processes with a high degree of temporal and spatial granularity. Several important problems concerning Kalman filtering over WSNs are addressed in this dissertation. First we study data fusion Kalman filtering for discrete-time linear time-invariant (LTI) systems over WSNs, assuming the existence of a data fusion center that receives observations from distributed sensor nodes and estimates the state of the target system in the presence of data packet drops. We focus on the single sensor node case and show that the critical data arrival rate of the Bernoulli channel can be computed by solving a simple linear matrix inequality problem. Then a more general scenario is considered where multiple sensor nodes are employed. We derive the stationary Kalman filter that minimizes the average error variance under a TCP-like protocol. The stability margin is adopted to tackle the stability issue. Second we study distributed Kalman filtering for LTI systems over WSNs, where each sensor node is required to locally estimate the state in a collaborative manner with its neighbors in the presence of data packet drops. The stationary distributed Kalman filter (DKF) that minimizes the local average error variance is derived. Building on the stationary DKF, we propose Kalman consensus filter for the consensus of different local estimates. The upper bound for the consensus coefficient is computed to ensure the mean square stability of the error dynamics. Finally we focus on time-varying topology. The solution to state consensus control for discrete-time homogeneous multi-agent systems over deterministic time-varying feedback topology is provided, generalizing the existing results. Then we study distributed state estimation over WSNs with time-varying communication topology. Under the uniform observability, each sensor node can closely track the dynamic state by using only its own observation, plus information exchanged with its neighbors, and carrying out local computation
Throughput-Distortion Computation Of Generic Matrix Multiplication: Toward A Computation Channel For Digital Signal Processing Systems
The generic matrix multiply (GEMM) function is the core element of
high-performance linear algebra libraries used in many
computationally-demanding digital signal processing (DSP) systems. We propose
an acceleration technique for GEMM based on dynamically adjusting the
imprecision (distortion) of computation. Our technique employs adaptive scalar
companding and rounding to input matrix blocks followed by two forms of packing
in floating-point that allow for concurrent calculation of multiple results.
Since the adaptive companding process controls the increase of concurrency (via
packing), the increase in processing throughput (and the corresponding increase
in distortion) depends on the input data statistics. To demonstrate this, we
derive the optimal throughput-distortion control framework for GEMM for the
broad class of zero-mean, independent identically distributed, input sources.
Our approach converts matrix multiplication in programmable processors into a
computation channel: when increasing the processing throughput, the output
noise (error) increases due to (i) coarser quantization and (ii) computational
errors caused by exceeding the machine-precision limitations. We show that,
under certain distortion in the GEMM computation, the proposed framework can
significantly surpass 100% of the peak performance of a given processor. The
practical benefits of our proposal are shown in a face recognition system and a
multi-layer perceptron system trained for metadata learning from a large music
feature database.Comment: IEEE Transactions on Signal Processing (vol. 60, 2012
Distributed State Estimation for Linear Systems
This paper studies a distributed state estimation problem for both
continuous- and discrete-time linear systems. A simply structured distributed
estimator is first described for estimating the state of a continuous-time,
jointly observable, input free, multi-channel linear system whose sensed
outputs are distributed across a fixed multi-agent network. The estimator is
then extended to non-stationary networks whose graphs switch according to a
switching signal with a fixed dwell time or a variable but with fixed average
dwell time, or switch arbitrarily under appropriate assumptions. The estimator
is guaranteed to solve the problem, provided a network-widely shared gain is
sufficiently large. As an alternative to sharing a common gain across the
network, a fully distributed version of the estimator is thus studied in which
each agent adaptively adjusts a local gain though the practicality of this
approach is subject to a robustness issue common to adaptive control. A
discrete-time version of the distributed state estimation problem is also
studied, and a corresponding estimator is proposed for time-varying networks.
For each scenario, it is explained how to construct the estimator so that its
state estimation errors all converge to zero exponentially fast at a fixed but
arbitrarily chosen rate, provided the network's graph is strongly connected for
all time. This is accomplished by appealing to the ``split-spectrum'' approach
and exploiting several well-known properties of invariant subspace. The
proposed estimators are inherently resilient to abrupt changes in the number of
agents and communication links in the inter-agent communication graph upon
which the algorithms depend, provided the network is redundantly strongly
connected and redundantly jointly observable.Comment: 17 pages, 8 figures. arXiv admin note: substantial text overlap with
arXiv:1903.0548
Decoding the `Nature Encoded\u27 Messages for Wireless Networked Control Systems
Because of low installation and reconfiguration cost wireless communication has been widely applied in networked control system (NCS). NCS is a control system which uses multi-purpose shared network as communication medium to connect spatially distributed components of control system including sensors, actuator, and controller. The integration of wireless communication in NCS is challenging due to channel unreliability such as fading, shadowing, interference, mobility and receiver thermal noise leading to packet corruption, packet dropout and packet transmission delay. In this dissertation, the study is focused on the design of wireless receiver in order to exploit the redundancy in the system state, which can be considered as a `nature encoding\u27 for the messages. Firstly, for systems with or without explicit channel coding, a decoding procedures based on Pearl\u27s Belief Propagation (BP), in a similar manner to Turbo processing in traditional data communication systems, is proposed to exploit the redundancy in the system state. Numerical simulations have demonstrated the validity of the proposed schemes, using a linear model of electric generator dynamic system. Secondly, we propose a quickest detection based scheme to detect error propagation, which may happen in the proposed decoding scheme when channel condition is bad. Then we combine this proposed error propagation detection scheme with the proposed BP based channel decoding and state estimation algorithm. The validity of the proposed schemes has been shown by numerical simulations. Finally, we propose to use MSE-based transfer chart to evaluate the performance of the proposed BP based channel decoding and state estimation scheme. We focus on two models to evaluate the performance of BP based sequential and iterative channel decoding and state estimation. The numerical results show that MSE-based transfer chart can provide much insight about the performance of the proposed channel decoding and state estimation scheme.
In this dissertation, the study is focused on the design of wireless receiver in order to exploit the redundancy in the system state, which can be considered as a `nature encoding\u27 for the messages. Firstly, for systems with or without explicit channel coding, a decoding procedures based on Pearl\u27s Belief Propagation (BP), in a similar manner to Turbo processing in traditional data communication systems, is proposed to exploit the redundancy in the system state. Numerical simulations have demonstrated the validity of the proposed schemes, using a linear model of electric generator dynamic system.
Secondly, we propose a quickest detection based scheme to detect error propagation, which may happen in the proposed decoding scheme when channel condition is bad. Then we combine this proposed error propagation detection scheme with the proposed BP based channel decoding and state estimation algorithm. The validity of the proposed schemes has been shown by numerical simulations.
Finally, we propose to use MSE-based transfer chart to evaluate the performance of the proposed BP based channel decoding and state estimation scheme. We focus on two models to evaluate the performance of BP based sequential and iterative channel decoding and state estimation. The numerical results show that MSE-based transfer chart can provide much insight about the performance of the proposed channel decoding and state estimation scheme
Stabilization over power-constrained parallel Gaussian channels
This technical note is concerned with state-feedback stabilization of multi-input systems over parallel Gaussian channels subject to a total power constraint. Both continuous-time and discrete-time systems are treated under the framework of H2 control, and necessary/sufficient conditions for stabilizability are established in terms of inequalities involving unstable plant poles, transmitted power, and noise variances. These results are further used to clarify the relationship between channel capacity and stabilizability. Compared to single-input systems, a range of technical issues arise. In particular, in the multi-input case, the optimal controller has a separation structure, and the lower bound on channel capacity for some discrete-time systems is unachievable by linear time-invariant (LTI) encoders/decoder
Stabilization of Linear Systems Over Gaussian Networks
The problem of remotely stabilizing a noisy linear time invariant plant over
a Gaussian relay network is addressed. The network is comprised of a sensor
node, a group of relay nodes and a remote controller. The sensor and the relay
nodes operate subject to an average transmit power constraint and they can
cooperate to communicate the observations of the plant's state to the remote
controller. The communication links between all nodes are modeled as Gaussian
channels. Necessary as well as sufficient conditions for mean-square
stabilization over various network topologies are derived. The sufficient
conditions are in general obtained using delay-free linear policies and the
necessary conditions are obtained using information theoretic tools. Different
settings where linear policies are optimal, asymptotically optimal (in certain
parameters of the system) and suboptimal have been identified. For the case
with noisy multi-dimensional sources controlled over scalar channels, it is
shown that linear time varying policies lead to minimum capacity requirements,
meeting the fundamental lower bound. For the case with noiseless sources and
parallel channels, non-linear policies which meet the lower bound have been
identified
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