This paper presents the genetic algorithm (GA) and particle swarm optimization (PSO) based frequency regulation for a wind‐based microgrid (MG) using reactive power balance loop. MG, operating from squirrel cage induction generator (SCIG), is employed for exporting the electrical power from wind turbines, and it needs reactive power which may be imported from the grid. Additional reactive power is also required from the grid for the load, directly coupled with such a distributed generator (DG) plant. However, guidelines issued by electric authorities encourage MGs to arrange their own reactive power because such reactive power procurement is defined as a local area problem for power system studies. Despite the higher cost of compensation, static synchronous compensator (STATCOM) is a fast‐acting FACTs device for attending to these reactive power mismatches. Reactive power control can be achieved by controlling reactive current through the STATCOM. This can be achieved with modification in current controller scheme of STATCOM. STATCOM current controller is designed with reactive power load balance for the proposed microgrid in this paper. Further, gain values of the PI controller, required in the STATCOM model, are selected first with classical methods. In this classical method, iterative procedures which are based on integral square error (ISE), integral absolute error (IAE), and integral square of time error (ISTE) criteria are developed using MATLAB programs. System performances are further investigated with GA and PSO based control techniques and their acceptability over classical methods is diagnosed. Results in terms of converter frequency deviation show how the frequency remains under the operating boundaries as allowed by IEEE standards 1159:1995 and 1250:2011 for integrating renewable‐based microgrid with grid. Real and reactive power management and load current total harmonic distortions verify the STATCOM performance in MG. The results are further validated with the help of recent papers in which frequency regulation is investigated for almost similar power system models. The compendium for this work is as following: (i) modelling of wind generator‐based microgrid using MATLAB simulink library, (ii) designing of STATCOM current controller with PI controller, (iii) gain constants estimation using classical, GA and PSO algorithm through a developed m codes and their interfacing with proposed simulink model, (v) dynamic frequency responses for proposed grid connected microgrid during starting and load perturbations, (vi) verification of system performance with the help of obtained real and reactive power management between STATCOM and grid, and (vii) validation of results with available literature.
Condition Monitoring and Fault Diagnosis of Generators in Power Networks
In this paper, a novel hierarchical signal processing
methodology is proposed for generator condition monitoring
and fault diagnosis based on raw electrical waveform data in
power networks, which can often be measured by strategically located
waveform sensors. The impact of generator short circuit
faults on strategically located electrical waveform sensors in
power networks are firstly investigated and validated in Matlab
Simulink. Based on the large set of electrical waveform data
produced by Matlab Simulink, a hierarchical algorithm is then
designed to locate fault site location and monitor the condition of
generators in power networks. Finally, the proposed methodology
is validated in 14-bus IEEE standard power network under
different scenarios (e.g, one generator fault, two-generator-fault,
various aging levels, etc). Our results show that we can locate
fault site location and monitor the aging condition of generators
in power networks. Compared to traditional condition monitoring
and fault diagnosis based on generator sensors, our proposed
methodology can monitor a large number of generators based
on a limited number of waveform sensors, which promises to
reduce the cost of the maintenance and improve the reliability
of the power grid.
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- Award ID(s):
- 1725636
- NSF-PAR ID:
- 10095638
- Date Published:
- Journal Name:
- IEEE Power & Energy Society General Meeting
- ISSN:
- 1944-9925
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
null (Ed.)With more and more renewable energy resources integrated into the power grid, the system is losing inertia because power electronics-based generators do not provide natural inertia. The low inertia will cause the microgrid to be more sensitive to disturbance and thus a small load change may result in a severe deviation in frequency. Based on the basic VSG algorithm, which is to mimic the characteristic of the traditional synchronous generator, the frequency can be controlled to a stable value faster and more smoothly when there is a fluctuation in the PV power generation and/or load change. However, characteristic of the VSG depends on the system structure in consideration of multiple generations, such as Synchronous Generator (SG), PV and Battery Energy Storage System (BESS), which greatly increases the complexity of applying VSG in practical power system. Furthermore, with BESS-VSG, Maximum Power Point (MPP) operation of PV is guaranteed. In addition, an adaptive VSG method is developed for a microgrid system, and the corresponding simulation in Matlab/Simulink shows the effectiveness of the adaptive VSG method.more » « less
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DC microgrids incorporate several converters for distributed energy resources connected to different passive and active loads. The complex interactions between the converters and components and their potential failures can significantly affect the grids' resilience and health; hence, they must be continually assessed and monitored. This paper presents a machine learning-assisted prognostic health monitoring (PHM) and diagnosis approach, enabling progressive interactions between the converters at multiple nodes to dynamically examine the grid's (or micro-grid's) health in real time. By measuring the resulting impedance at the power converters' terminals at various grid nodes, a neural network-based classifier helps detect the grid's health condition and identify the potential fault-prone zones, along with the type and location of the fault type in the grid topology. For a faulty grid, a Naive Bayes and a support vector machine (SVM)-based classifiers are used to locate and identify the faulty type, respectively. A separate neural network-based regression model predicts the source power delivered and the loads at different terminals in a healthy grid network. The proposed concepts are supported by detailed analysis and simulation results in a simple four-terminal DC microgrid topology and a standard IEEE 5 Bus system.more » « less
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