Contingency analysis (CA) is a well-known function in power system planning and operation. In accordance with CA results, the system operator dispenses information regarding static security of the power system (overloads and/or voltage outside tolerable limits). However, classic CA with remedial action schemes cannot distinguish safe operating regimes from potentially dangerous ones in terms of voltage (in)stability. In fact, voltage instability is considered as one of the major threats leading to power system insecurity. Therefore, in this study an enhanced contingency analysis (ECA) is presented where the classical CA is extended with static voltage analysis based on the modal analysis. The article presents a dedicated methodology for the proposed ECA tool, with special emphasis on the analysis of corrective measures provided by the system operator, intended for enhancing power system security (regulation transformer action, distributed generation and energy storage). Also the influence of the load model was analyzed by simulation and the main conclusions are presented. The study demonstrated the advantages that distributed generation resources and energy storage can provide in the context of voltage stability. Also, the simulations acknowledged the importance of correct load modeling, since over or under estimation of a certain load-type component can result in too optimistic or too pessimistic power system operation limits.

Photovoltaic (PV) system inverters usually operate at unitary power factor, injecting only active power into the system. Recently, many studies have been done analyzing potential benefits of reactive power provisioning, such as voltage regulation, congestion mitigation and loss reduction. This article analyzes possibilities for loss reduction in a typical medium voltage distribution system. Losses in the system are compared to the losses in the PV inverters. Different load conditions and PV penetration levels are considered and for each scenario various active power generation by PV inverters are taken into account, together with allowable levels of reactive power provisioning. As far as loss reduction is considered, there is very small number of PV inverters operating conditions for which positive energy balance exists. For low and medium load levels, there is no practical possibility for loss reduction. For high loading levels and higher PV penetration specific reactive savings, due to reactive power provisioning, increase and become bigger than additional losses in PV inverters, but for a very limited range of power factors.

With the steady increase in the use of renewable energy sources in the energy sector, new challenges arise, especially the unpredictability of these energy sources. This uncertainty complicates the management, planning, and development of energy systems. An effective solution to these challenges is short-term forecasting of the output of photovoltaic power plants. In this paper, a novel method for short-term production prediction was explored which involves continuous photography of the sky above the photovoltaic power plant. By analyzing a series of sky images, patterns can be identified to help predict future photovoltaic power generation. A hybrid model that integrates both a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) for short-term production forecasting was developed and tested. This model effectively detects spatial and temporal patterns from images and power output data, displaying considerable prediction accuracy. In particular, a 74% correlation was found between the model’s predictions and actual future production values, demonstrating the model’s efficiency. The results of this paper suggest that the hybrid CNN-LSTM model offers an improvement in prediction accuracy and practicality compared to traditional forecasting methods. This paper highlights the potential of Deep Learning in improving renewable energy practices, particularly in power prediction, contributing to the overall sustainability of power systems.

High impedance faults are rather difficult to detect, due to their inherent characteristics. However, in most cases high-impedance low-current faults evolve to low-impedance high-current faults, thus giving rise to life hazard situations and equipment damage. In this paper, a novel back up protection scheme for high impedance fault detection, based on synchrophasor measurement technology, is proposed. In fact, the presented detection method can be thought as the combination of a classic differential and a classic admittance relay. The protection device designated for high impedance fault detection, incorporating the proposed novel protection algorithm, permanently monitors transmission line’s shunt admittance thus being able to detect changes-increase in the resistive part of shunt admittance, namely the shunt conductance. Actually, a sudden rise of transmission line’s shunt conductance is a strong indicator of high impedance fault inception. Conducted computer simulations confirmed a high degree of selectivity and sensitivity of the proposed method. Moreover, the technical feasibility of the approach was tested considering real-world measurement data obtained for two 220 kV transmission lines. From the acquired data, measurement uncertainties of the monitored parameters in normal operating conditions, were calculated. The results demonstrate excellent sensitivity of the proposed method, even for the case of short transmission lines during low-loading conditions.

Transmission line parameters vary with environmental and operating conditions, thus introducing uncertainties in power flow studies and protection zone settings of relay protection equipment. Variations of line parameters are fundamental for the determination of static and dynamic line power ratings. Usually, nominal values are given without an associated confidence interval, and therefore an important piece of information is missing. With the proliferation of phasor measurement units (PMUs) that allows for synchronized measurements at different geographical positions, arouse the possibility for simple and efficient measurements of line parameters. This article analyzes uncertainties associated with the estimation of line parameters, based on the equivalent π model, valid for short and medium length transmission lines. This problem has been addressed in literature, but only from a theoretical perspective and obtained results were verified with simulations only. Here, measurements conducted on two 220 kV transmission lines are analyzed. It was found that correlation between voltage and current measurements must be taken into consideration when analyzing uncertainty propagation law. Only then statistical analysis of estimations match predictions obtained by applying the theoretical model. Accuracy limits were analyzed as well, but in this case some results were within calculated limits, while others did not follow predictions. Improvements in accuracy limits determination require the separation of environmental and operating condition variations from deviations due to measurement equipment, thus requiring additional information.