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.

In this paper, the problem of voltage unbalance in the three-phase power systems is examined. A fast detection technique (FDT) is proposed to detect the voltage unbalance precisely and speedily. The well-known detection methods require more than one cycle time to detect the unbalanced voltages, whereas the proposed technique detects the unbalanced situations speedily in a discrete manner. Reducing the time duration required to detect the unbalanced voltages will enhance the dynamic response of the control system used to balance these voltages. The FDT acquires the instantaneous values of the three load voltages, calculates the sum and the space vector for these voltages at each sample, and utilizes these parameters to detect the voltage unbalance accurately within a quarter of the cycle time. A proof-of-concept simulation model for a real power system has been built. The parameters of the aqaba-qatrana-south amman (AQSA) Jordanian power system are considered in the simulation model. Also, several test cases have been conducted to test and validate the capabilities of the proposed technique.

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Transmission lines in the desert are exposed to the desert environment, which includes sandstorms as one of its hallmarks. A conductive layer develops with prolonged sand deposition and the presence of moisture, ambient humidity, and dew. The ensuing leakage current causes surface discharge, which limits the life of the insulator and interrupting the power supply. The locations of power lines in the Egyptian Sinai desert, where sandstorms are known to occur frequently, are exposed to such a risk. In order to estimate the danger of insulator failure, this paper studies the flow of leakage current on porcelain insulators that have been contaminated with sand. This work relies on accurate data collected and published in a prior study regarding Sinai, which mainly focused on contaminating sand’s grain sizes. Porcelain insulator is simulated using finite element method to determine the leakage current that results on its contaminated surface. The probabilistic characteristics of the insulator’s leakage current are derived using Monte Carlo technique, allowing for the risk assessment of insulator failure. This assessment can be used to justify the suitability of using this kind of insulator in Sinai.

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