Neural network approach to voltage and reactive power control in power systems

Energy management engineers are focusing their interest in tapping maximum profit for their system from substation automation (SSA)/distribution automation (DA). Volt/Var control through fixed/switched capacitors, transformer taps and voltage set points are at different levels of research and implementation. A neural network based solution for voltage-VAR control is proposed with the aim to reduce the real power loss flowing in a power system and subsequently improve the voltage profile. The module consists of two networks. The first network determines the control parameters i.e., generator voltage, transformer taps and shunt capacitance for minimal power loss when the loads at the load buses are specified as inputs. With the obtained parameters, a load flow program is run and power loss is noted and the system is checked for voltage violations. In case of voltage violations, the voltages are fed to the second network, which gives dQ at different buses for voltage violation minimization. These modules are successfully tested for different load patterns on a six-bus system.