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Abstract: This paper deals with automatic generation control (AGC) of a three unequal area hydrothermal system. Reheat turbines in thermal areas and electric governor in hydro area are considered. Appropriate generation rate constraints are considered in the areas. Bacterial foraging (BF) technique is used to simultaneously optimize the integral gains (KIi ) and speed regulation parameter (Ri ) keeping frequency bias fixed at frequency response characteristics. The integral controller in this case is termed as BFIC. The performance of a multilayer perception neural network (MLPNN) controller using reinforcement learning is evaluated for the system. In this reinforcement learning, the weights are dynamically adjusted online using backpropagation algorithm with error being the area control error (ACE). The performance of the MLPNN controller is compared with that of BFIC. Also, the performance of MLPNN controller over a wide range of system loading conditions and step load perturbations is compared with BFIC. Investigations clearly reveal the superior performance of MLPNN controller over BFIC. Sensitivity analysis subject to wide changes in system loading, inertia constant (H) and size and location of step load perturbation is carried out to investigate the robustness of the controller with the optimum KIi and Ri obtained at nominal condition. [Copyright &y& Elsevier]

DC distribution networks and micro grids are gaining importance enabling the integration of hybrid distributed generation, energy storage units, modern loads including electric vehicles and different micro grid topologies, thus permitting the consequent development of DC Distribution Systems (DCDS). For maximum utilization of distribution network without compromising the security and reliability of the supply system, determination of critical loading is crucial. With this objective, this paper proposes a modified DC continuation power flow based approach for determining the loadability limit of the active DCDS. DC-DC converters play an important role in bus voltage regulation and system loadability. Impact of these converters must be accounted in any analysis and planning of active DCDS. In this paper, two different mathematical models of lossless DC-DC converters are developed to account them in power flow model of the system. The proposed approach can be applied to both radial and meshed DCDS. Impacts of integration of converters in the system are illustrated by results of two realistic DCDS and results are compared with the corresponding AC Distribution Systems (ACDS). [ABSTRACT FROM AUTHOR]

Highlights: [•] Neuro-fuzzy scheme to investigate the winding insulation paper (WIP) deterioration. [•] To investigate the deterioration condition of the WIP in oil-immerged power transformer. [•] New technique also diagnoses those samples which are out of IEEE and IEC standard range. [•] Testing results are better as compare to IEEE and IEC methods. [ABSTRACT FROM AUTHOR]

State-estimation plays a vital role to monitor, observe and understand the combined heat and electric network. In this paper, a hybrid framework is presented to accurately estimate the system states of electric distribution network and heat network, using the limited non-redundant measurements obtained from supervisory control and data acquisition and advanced metering infrastructure systems. The presented hybrid framework involves two steps, namely, the state-forecasting and the state-estimation. The state-forecasting uses a deep neural network to forecast the system states at every fifteen minutes interval, while these forecasted states are further used by the hybrid estimator, which uses a robust extended Kalman filter to estimate the system states with help of both datasets corresponding to supervisory control and data acquisition and advanced metering infrastructure systems, at hourly interval. The proposed framework does not completely rely on the system model at different instants. The effectiveness of the method is validated through thorough comparisons with simulation studies carried out using the Barry Island test system, United Kingdom. Satisfactory performance is observed even with the presence of bad data in the measurements. • A framework to estimate states of electric and heat networks with SCADA and AMI data • A method of forecasting the CHEN states using only a limited SCADA dataset • A robust Kalman filter for accurate estimation under gross errors in data. • Test results that validate the method on Barry Island CHEN system. • Comparative results over the state-of-art WLS and ANN based estimators. [ABSTRACT FROM AUTHOR]