Intelligent Power Control of Inverter Air Conditioners in Power Systems: A Brain Emotional Learning-Based Approach

Inverter air-conditioning (IAC) units have been proved to be effective in frequency regulation by providing flexible capacities. This paper proposes a brain emotional learning (BEL)-based controller to provide the IACs with control signals to be efficiently involved in the frequency regulation in power systems. The BEL-based controller can learn quick-auto, making it appropriate in systems facing uncertainty. To assess the BEL controller performance in realistic conditions, the uncertainties as a consequence of variations in system parameters and load level are considered. The goal is to use the BEL controller to increase the IAC units' ability to track regulation signals accurately in uncertain circumstances. The controller is compared to a fuzzy-PI control, a proportional control scheme, a model predictive control and a linear quadratic regulator control. A delay-dependent stability criterion is used to calculate the highest time delay in the IACs response under which the system maintains stability. In addition, this paper presents an BEL-based coordinator to coordinate the IACs and traditional generation units for compensating considerable frequency variations caused by the time delays. Case studies are accomplished on a multi-area power system in MATLAB/Simulink environment. Eventually, real-time verifications by OPAL-RT real-time digital simulator on the simulated power system are executed to assess the control method.

Automated summary

This paper proposes a brain emotional learning (BEL)-based controller for inverter air-conditioning (IAC) units to efficiently participate in frequency regulation in power systems. The controller is suitable for systems facing uncertainty and can quickly adapt to changing conditions. By comparing it with other control schemes, the effectiveness of the BEL controller in accurately tracking regulation signals under uncertain circumstances is demonstrated.