Search Results (5)

Power transformers are the most important and expensive assets in high-voltage power systems. To ensure an adequate level of reliability throughout the transformer’s lifetime, its maintenance strategy must be well defined. When an incipient fault occurs in the transformer insulation, a gas concentration pattern, representative of the type of fault, is generated. Fault-identification methods use gas concentrations and their ratios to identify the type of fault. None of the traditional or new fault-identification methods attempt to detect transformer oil contamination from on-load tap changer (OLTC) gases. In this study, from dissolved gas analysis (DGA) samples of transformers identified as contaminated in a previous study, fault-identification methods based on graphical representations were used to observe the patterns of results. From such patterns, Duval’s triangle and pentagon methods were modified to include a new zone indicating oil contamination (OC) from OLTC gases. Finally, the proposed modifications were validated using 75 DGA samples extracted from previous studies that were identified as D1 or D2 faults or contaminated from OLTC. This validation showed that only 14.7% and 13.3% of the DGA samples fell within the new OC zone of the proposed triangle and pentagon, respectively. Full article

Considering the possible overvoltage caused by high-penetration photovoltaics (PVs) connected to the distribution networks (DNs), a cluster partition-based voltage control combined day-ahead scheduling and real-time control for distribution networks is proposed. Firstly, a community detection algorithm utilizing a coupling quality function is introduced to divide the PVs into clusters. Based on the cluster partition, day-ahead scheduling (DAS) is proposed with the objective of minimizing the operating costs of PVs, as well as the on-load tap changer (OLTC). In the real-time control, a second-order cone programming (SOCP) model-based real-time voltage control (RTVC) strategy is drawn up in each cluster to regulate the PV inverters, and this strategy can correct the day-ahead scheduling by modifications. The proposed strategy realizes the combination of day-ahead scheduling and real-time voltage control, and the optimization of voltage control can be greatly simplified. Finally, the proposed method is applied to a practical 10 kV feeder to verify its effectiveness. Full article

The increasing Photovoltaic (PV) penetration in residential Low Voltage (LV) networks is likely to result in a voltage rise problem. One of the potential solutions to deal with this problem is to adopt a distribution transformer fitted with an On-Load Tap Changer (OLTC). The control of the OLTC in response to local measurements reduces the need for expensive communication channels and remote measuring devices. However, this requires developing an advanced decision-making algorithm to estimate the existence of voltage issues and define the best set point of the OLTC. This paper presents a decentralized data-driven control approach to operate the OLTC using local measurements at a distribution transformer (i.e., active power and voltage at the secondary side of the transformer). To do so, Monte Carlo simulations are utilized offline to produce a comprehensive dataset of power flows throughout the distribution transformer and customers’ voltages for different PV penetrations. By the application of the curve-fitting technique to the resulting dataset, models to estimate the maximum and the minimum customers’ voltages are defined and embedded into the control logic to manage the OLTC in real time. The application of the approach to a real UK LV feeder shows its effectiveness in improving PV hosting capacity without the need for remote monitoring elements. Full article

Power transformers are considered to be the most important assets in power substations. Thus, their maintenance is important to ensure the reliability of the power transmission and distribution system. One of the most commonly used methods for managing the maintenance and establishing the health status of power transformers is dissolved gas analysis (DGA). The presence of acetylene in the DGA results may indicate arcing or high-temperature thermal faults in the transformer. In old transformers with an on-load tap-changer (OLTC), oil or gases can be filtered from the OLTC compartment to the transformer’s main tank. This paper presents a method for determining the transformer oil contamination from the OLTC gases in a group of power transformers for a distribution system operator (DSO) based on the application of the guides and the knowledge of experts. As a result, twenty-six out of the 175 transformers studied are defined as contaminated from the OLTC gases. In addition, this paper presents a methodology based on machine learning techniques that allows the system to determine the transformer oil contamination from the DGA results. The trained model achieves an accuracy of 99.76% in identifying oil contamination. Full article

Considering the possible overvoltage caused by the high proportion of photovoltaic systems (PVs) accessing distribution networks in the future, traditional centralized control methods will be too complex to satisfy the control response time demands. To solve this problem this paper presents a two-level voltage control method. At the day-ahead level, based on the PV-output and load-demand forecast, a community detection algorithm using an improved modularity index is introduced to divide the distribution network into clusters; a day-ahead optimal scheduling is drawn up on the basis of the network partition, and the objective is to minimize the operation costs of the distribution networks. At the real-time level, under the day-ahead optimal scheduling and network partition of the upper level, a real-time optimal voltage control algorithm is proposed based on the real-time operation data of the distribution networks, and the objective is to correct the day-ahead optimal scheduling through modifications. Thus, the algorithm realizes the combination of day-ahead scheduling and real-time control and achieves complete zonal voltage control for future distribution networks with high proportion of PVs. The proposed method can not only optimize the tap operation of an on-load tap changer (OLTC), improving the PV hosting capacity of the distribution network for a high proportion of PVs, but can also reduce the number of control nodes and simplify the control process to reduce the optimization time. The proposed approach is applied to a real, practical, 10 kV, 62-node feeder in Zhejiang Province of China to verify its feasibility and effectiveness. Full article