150,082 research outputs found
A Framework for Phasor Measurement Placement in Hybrid State Estimation via Gauss-Newton
In this paper, we study the placement of Phasor Measurement Units (PMU) for
enhancing hybrid state estimation via the traditional Gauss-Newton method,
which uses measurements from both PMU devices and Supervisory Control and Data
Acquisition (SCADA) systems. To compare the impact of PMU placements, we
introduce a useful metric which accounts for three important requirements in
power system state estimation: {\it convergence}, {\it observability} and {\it
performance} (COP). Our COP metric can be used to evaluate the estimation
performance and numerical stability of the state estimator, which is later used
to optimize the PMU locations. In particular, we cast the optimal placement
problem in a unified formulation as a semi-definite program (SDP) with integer
variables and constraints that guarantee observability in case of measurements
loss. Last but not least, we propose a relaxation scheme of the original
integer-constrained SDP with randomization techniques, which closely
approximates the optimum deployment. Simulations of the IEEE-30 and 118 systems
corroborate our analysis, showing that the proposed scheme improves the
convergence of the state estimator, while maintaining optimal asymptotic
performance.Comment: accepted to IEEE Trans. on Power System
Nonlinear constrained and saturated control of power electronics and electromechanical systems
Power electronic converters are extensively adopted for the solution of timely issues, such
as power quality improvement in industrial plants, energy management in hybrid electrical
systems, and control of electrical generators for renewables. Beside nonlinearity, this systems
are typically characterized by hard constraints on the control inputs, and sometimes
the state variables. In this respect, control laws able to handle input saturation are crucial
to formally characterize the systems stability and performance properties. From a practical
viewpoint, a proper saturation management allows to extend the systems transient
and steady-state operating ranges, improving their reliability and availability.
The main topic of this thesis concern saturated control methodologies, based on modern
approaches, applied to power electronics and electromechanical systems. The pursued
objective is to provide formal results under any saturation scenario, overcoming the
drawbacks of the classic solution commonly applied to cope with saturation of power converters,
and enhancing performance. For this purpose two main approaches are exploited
and extended to deal with power electronic applications: modern anti-windup strategies,
providing formal results and systematic design rules for the anti-windup compensator, devoted
to handle control saturation, and “one step” saturated feedback design techniques,
relying on a suitable characterization of the saturation nonlinearity and less conservative
extensions of standard absolute stability theory results.
The first part of the thesis is devoted to present and develop a novel general anti-windup
scheme, which is then specifically applied to a class of power converters adopted for power
quality enhancement in industrial plants. In the second part a polytopic differential inclusion
representation of saturation nonlinearity is presented and extended to deal with a
class of multiple input power converters, used to manage hybrid electrical energy sources.
The third part regards adaptive observers design for robust estimation of the parameters
required for high performance control of power systems
An intelligent controlling method for battery lifetime increment using state of charge estimation in PV-battery hybrid system
In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly
MmWave Massive MIMO Based Wireless Backhaul for 5G Ultra-Dense Network
Ultra-dense network (UDN) has been considered as a promising candidate for
future 5G network to meet the explosive data demand. To realize UDN, a
reliable, Gigahertz bandwidth, and cost-effective backhaul connecting
ultra-dense small-cell base stations (BSs) and macro-cell BS is prerequisite.
Millimeter-wave (mmWave) can provide the potential Gbps traffic for wireless
backhaul. Moreover, mmWave can be easily integrated with massive MIMO for the
improved link reliability. In this article, we discuss the feasibility of
mmWave massive MIMO based wireless backhaul for 5G UDN, and the benefits and
challenges are also addressed. Especially, we propose a digitally-controlled
phase-shifter network (DPSN) based hybrid precoding/combining scheme for mmWave
massive MIMO, whereby the low-rank property of mmWave massive MIMO channel
matrix is leveraged to reduce the required cost and complexity of transceiver
with a negligible performance loss. One key feature of the proposed scheme is
that the macro-cell BS can simultaneously support multiple small-cell BSs with
multiple streams for each smallcell BS, which is essentially different from
conventional hybrid precoding/combining schemes typically limited to
single-user MIMO with multiple streams or multi-user MIMO with single stream
for each user. Based on the proposed scheme, we further explore the fundamental
issues of developing mmWave massive MIMO for wireless backhaul, and the
associated challenges, insight, and prospect to enable the mmWave massive MIMO
based wireless backhaul for 5G UDN are discussed.Comment: This paper has been accepted by IEEE Wireless Communications
Magazine. This paper is related to 5G, ultra-dense network (UDN), millimeter
waves (mmWave) fronthaul/backhaul, massive MIMO, sparsity/low-rank property
of mmWave massive MIMO channels, sparse channel estimation, compressive
sensing (CS), hybrid digital/analog precoding/combining, and hybrid
beamforming. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=730653
Robust Decentralized State Estimation and Tracking for Power Systems via Network Gossiping
This paper proposes a fully decentralized adaptive re-weighted state
estimation (DARSE) scheme for power systems via network gossiping. The enabling
technique is the proposed Gossip-based Gauss-Newton (GGN) algorithm, which
allows to harness the computation capability of each area (i.e. a database
server that accrues data from local sensors) to collaboratively solve for an
accurate global state. The DARSE scheme mitigates the influence of bad data by
updating their error variances online and re-weighting their contributions
adaptively for state estimation. Thus, the global state can be estimated and
tracked robustly using near-neighbor communications in each area. Compared to
other distributed state estimation techniques, our communication model is
flexible with respect to reconfigurations and resilient to random failures as
long as the communication network is connected. Furthermore, we prove that the
Jacobian of the power flow equations satisfies the Lipschitz condition that is
essential for the GGN algorithm to converge to the desired solution.
Simulations of the IEEE-118 system show that the DARSE scheme can estimate and
track online the global power system state accurately, and degrades gracefully
when there are random failures and bad data.Comment: to appear in IEEE JSA
System configuration, fault detection, location, isolation and restoration: a review on LVDC Microgrid protections
Low voltage direct current (LVDC) distribution has gained the significant interest of research due to the advancements in power conversion technologies. However, the use of converters has given rise to several technical issues regarding their protections and controls of such devices under faulty conditions. Post-fault behaviour of converter-fed LVDC system involves both active converter control and passive circuit transient of similar time scale, which makes the protection for LVDC distribution significantly different and more challenging than low voltage AC. These protection and operational issues have handicapped the practical applications of DC distribution. This paper presents state-of-the-art protection schemes developed for DC Microgrids. With a close look at practical limitations such as the dependency on modelling accuracy, requirement on communications and so forth, a comprehensive evaluation is carried out on those system approaches in terms of system configurations, fault detection, location, isolation and restoration
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