Summary In this paper, a neural network controller is proposed to retrieve the voltage balancing conditions in three‐phase power systems. The neural network is suggested to calculate the required set of firing angles for the thyristor‐controlled reactor accurately to balance the three‐load voltages quickly. The proposed controller is fed by different parameters within different feeding techniques, namely, root mean square (RMS) values of the three load voltages, RMS value of the space vector signal calculated from the three load voltages, and the RMS values of both the three load voltages and their associated space vector. The intentions of the proposed techniques are to combine between reducing the number of measured parameters and providing the controller with qualitative data about system status. The influence of the measured parameters on the neural network performance is examined by calculating the regression coefficients through several test cases. Accordingly, only the effective parameters are utilized to reduce the neural network complexity and to enhance the controller response time. Additionally, the calculations of the controller input parameters are made in terms of space vector cycle, which is half of system sinusoidal cycle. Consequently, the calculation time is reduced significantly. The Aqaba‐Qatrana‐South Amman power system is considered and modeled as a real case study. In addition, several test cases have been conducted to test and validate the ability of the proposed neural network controller in retrieving the voltage balance conditions precisely and quickly. The results have revealed the ability of the proposed neural network controller to calculate the firing angles quickly within 10 milliseconds and achieve very low voltage unbalance factor.

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Voltage-unbalance is one of the power quality deficiencies that degrades electrical power systems performance. In this work, voltage unbalance problem is tackled through two stages; evaluation using a novel performance index and mitigation using a thyristor-controlled reactor (TCR) compensator with artificial intelligent (AI) based models. Unlike standard performance indices that rely on voltages' root mean square (RMS) values, the proposed index depends on the space vector (SV) signal amplitude for voltage unbalance evaluation. This signal depends on the instantaneous values of the three-phase voltages and has twice the system frequency. Therefore, the proposed index entitled as space vector unbalance factor (SVUF) reflects the amount of voltage unbalance and reduces the time necessary for evaluation by half. Subsequently, advanced models based on several algorithms are proposed to generate the required firing angles for TCR compensator to restore voltage balance, including radial basis functions networks (RBFNs), hybrid-RBFNs (H-RBFNs), polynomials (PNs), and simplified neural networks (NNs). Models' structure, prediction capability, and response time are analyzed. Results show that the time required for voltage unbalance mitigation is reduced. Moreover, the models used to generate the firing angles are simplified significantly while maintaining high accuracy.

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