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# Neural Network Control of Shunt Active Filters in MATLAB

Neural Network Control of Shunt Active Filters in MATLAB

Introduction

Shunt Active Filters are essential components in power systems for mitigating the effects of non-linear loads, which can cause disturbances such as harmonics. In this blog post, we will delve into the implementation of a neural network controller for a Shunt Active Filter using MATLAB.

Understanding Non-Linear Loads and Shunt Active Filters

Non-linear loads, such as those created by three-phase rectifiers with RL circuits, introduce non-linearity into power systems due to the characteristics of semiconductor diodes. These non-linear loads can disrupt the sinusoidal nature of source currents, leading to issues like the consumption of reactive power from the grid.

To address this, Shunt Active Filters are employed. These filters consist of three-phase inverters and capacitors connected to the grid in parallel. By injecting compensating current into the grid, Shunt Active Filters help maintain sinusoidal source currents despite non-linear loads.

### The Need for Control Algorithms

Controlling Shunt Active Filters is crucial for effective compensation. Control algorithms convert source voltages and currents into alpha-beta quantities, calculate real and reactive power, and generate compensating currents. Traditional Proportional-Integral (PI) controllers are commonly used for this purpose.

### Introducing Neural Network Control

Instead of traditional PI controllers, neural network controllers offer advantages in terms of flexibility and adaptability. A neural network is trained to generate compensating currents based on reference voltages and errors. Training involves collecting data, creating input-output pairs, and training the network using MATLAB's neural network fitting tool.

### Simulation and Results

Simulating the Shunt Active Filter with neural network control reveals promising results. The neural network effectively compensates for non-linear loads, maintaining source currents close to sinusoidal. Comparing performance metrics like Total Harmonic Distortion (THD) with traditional PI controllers shows reduced distortion with neural network control, indicating improved system efficiency.

### Conclusion

Implementing neural network control for Shunt Active Filters in MATLAB showcases the potential for advanced control techniques in power systems. By harnessing the power of artificial intelligence, such as neural networks, we can enhance the performance and efficiency of power systems, paving the way for a more sustainable energy future.