In the present scenario of microgrid system, conversion of electrical energy has initiated a challenge to maintain the power quality within a satisfactory range. It can be influenced by the voltage deviation, sag/swell, unbalancing, frequency, total harmonic distortion (THD) and power factor as per nature of local loads and the condition of distributed energy resources (DERs). The relationship between power quality and the set of these variables are non-linear in nature. The existing literature show that the above mentioned parameters are not considered simultaneously for the assessment and controlling of power quality in PV based AC microgrid. To minimize the effect of these variables, a novel artificial neural network (ANN) based control approach has been proposed which can control the power quality as per IEEE/IEC standards. The proposed method has shown fast, smooth and stable operation while the performance of the same is verified with that of the proportional–integral (PI) and fuzzy-PI controllers using Matlab-Simulink software. The small size microgrid model is tested with the effect of line impedance and communication delay for the assessment of power quality parameters. This model is extended to a large size realistic microgrid structure for the feasibility of control methodology. The realistic microgrid structure is verified under the analysis of line impedance, communication delay, demand response and off-nominal conditions. The proposed control methodology is validated in a realistic microgrid structure and simulation results are presented to show the performance of proposed controller under different test conditions to identify an ANN library.

在微电网系统的当前情况下，电能的转换引发了将电能质量保持在令人满意的范围内的挑战。根据本地负载的性质和分布式能源的条件，它可能会受到电压偏差，骤降/骤升，不平衡，频率，总谐波失真（THD）和功率因数的影响。电能质量和这些变量集之间的关系本质上是非线性的。现有文献表明，在基于PV的AC微电网中，不能同时考虑上述参数用于评估和控制电能质量。为了最小化这些变量的影响，已经提出了一种基于新型人工神经网络（ANN）的控制方法，该方法可以按照IEEE / IEC标准控制电能质量。所提出的方法显示了快速，平稳和稳定的操作，同时使用Matlab-Simulink软件通过比例积分（PI）和模糊PI控制器验证了该方法的性能。使用线阻抗和通信延迟的影响测试了小型微电网模型，以评估电能质量参数。为了控制方法的可行性，该模型被扩展到大尺寸的实际微电网结构。通过对线路阻抗，通信延迟，需求响应和偏离标称条件的分析，可以验证实际的微电网结构。所提出的控制方法在真实的微电网结构中得到了验证，并给出了仿真结果，以显示所提出的控制器在不同测试条件下的性能，以识别ANN库。使用Matlab-Simulink软件通过比例积分（PI）和模糊PI控制器的性能验证了其平稳，稳定的运行性能。使用线阻抗和通信延迟的影响测试了小型微电网模型，以评估电能质量参数。为了控制方法的可行性，该模型被扩展到大尺寸的实际微电网结构。通过对线路阻抗，通信延迟，需求响应和偏离标称条件的分析，可以验证实际的微电网结构。所提出的控制方法在实际的微电网结构中得到了验证，并给出了仿真结果，以显示所提出的控制器在不同测试条件下的性能，以识别ANN库。使用Matlab-Simulink软件通过比例积分（PI）和模糊PI控制器的性能验证了其平稳，稳定的运行性能。使用线阻抗和通信延迟的影响测试了小型微电网模型，以评估电能质量参数。为了控制方法的可行性，该模型被扩展到大尺寸的实际微电网结构。通过对线路阻抗，通信延迟，需求响应和偏离标称条件的分析，可以验证实际的微电网结构。所提出的控制方法在真实的微电网结构中得到了验证，并给出了仿真结果，以显示所提出的控制器在不同测试条件下的性能，以识别ANN库。