Abstract
A distribution static compensator (DSTATCOM) is proposed in this study to improve the power quality, which includes the total harmonic distortion (THD) of the grid current and power factor (PF), of a mini grid with nonlinear and linear inductive loads. Moreover, the DC-link voltage regulation control of the DSTATCOM is essential especially under load variation conditions. Therefore, to improve the power quality and keep the DC-link voltage of the DSTATCOM constant under the variation of nonlinear and linear loads effectively, the traditional proportional-integral (PI) controller is substituted with a new online trained compensatory fuzzy neural network with an asymmetric membership function (CFNN-AMF) controller. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. Furthermore, the dimensions of the Gaussian membership functions are directly extended to AMFs for the optimization of the fuzzy rules and the upgrade of learning ability of the networks. In addition, the network structure and online learning algorithm of the proposed CFNN-AMF are introduced in detail. Finally, the effectiveness and feasibility of the DSTATCOM using the proposed CFNN-AMF controller to improve the power quality and maintain the constant DC-link voltage under the change of nonlinear and linear inductive loads have been demonstrated by some experimental results.
Highlights
Due to the increasing demand for electricity, limited supply of fossil fuels and the threats of climate change, distributed renewable energy sources has been extensively integrated into the existing power grid, especially at the distribution level [1,2]
To improve the DC-link voltage regulation control characteristics of the DSTATCOM under nonlinear and linear inductive loads variation conditions, an online trained compensatory fuzzy neural network (CFNN)-asymmetric MFs (AMFs) is proposed as a regulation controller to take the place of a traditional PI controller in the DSTATCOM
The intelligent CFNN-AMF controlled DSTATCOM for power quality improvement and DC-link voltage regulation control under load variation conditions is implemented in a personal computer (PC)-based control computer via Matlab and Simulink with 0.2 ms sampling time
Summary
Due to the increasing demand for electricity, limited supply of fossil fuels and the threats of climate change, distributed renewable energy sources has been extensively integrated into the existing power grid, especially at the distribution level [1,2]. A distributed generator system with a DSTATCOM using a composite observer based control technique to improve the power quality was proposed in [14]. Owing to the above advantages of both the CFNN and AMF, a compensatory fuzzy neural network with an asymmetric membership function (CFNN-AMF), is proposed in this study for the control of a DSTATCOM to improve the power quality and maintain constant DC-link voltage under load variation conditions. To improve the DC-link voltage regulation control characteristics of the DSTATCOM under nonlinear and linear inductive loads variation conditions, an online trained CFNN-AMF is proposed as a regulation controller to take the place of a traditional PI controller in the DSTATCOM. CFNN-AMF controller for the improvement of power quality and maintaining the constant DC-link voltage will be demonstrated with some experimental results.
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