Design and Implementation of Power Network Security Protection System Based on Internet of Things

With the progress of society, the power industry is also constantly developing. This puts forward higher requirements for the safety of our country’s power grid system. In order to ensure the stable and reliable operation of the power supply network and the economic rationality, stability and security, this article first elaborates the concept of network security and network security management, secondly, it analyzes the network security protection technology. Finally, it studies the design and implementation of a store network security protection system based on the Internet of Things.

Cable Temperature Alarm Threshold Setting Method Based on Convolutional Neural Network

Power cable is used more and more in the power network, and its significance to the safety and stability of the power network is increasingly prominent. Especially in the urban power grid, the high voltage cable is related to the normal production and life of the city. Because of the particularity of the laying environment, it is very difficult to find and eliminate the fault points once the cable faults occur, which seriously affects the reliability of the power grid. Currently, 25% of cable faults are caused by elevated cable temperature, so it is important to set the cable temperature alarm threshold accurately. In this paper, a method of setting temperature alarm threshold using convolutional neural network is proposed. Experiments show that this method is 60% more accurate than other methods.

Optimization of Energy Efficiency for Electric Power Distribution System Losses

This study evaluated the existing electric power network of Mile 2 Diobu zone, Port Harcourt distribution network which consists of four (4) 11kV distribution feeders namely; Ojoto, Nsukka, Udi and Silverbird. This work considered Ojoto and Nsukka Street distribution network for improved power quality. The three (3) 33/11kv injection substations are fed from 165 MVA transmission station (PH Town) at Amadi junction by Nzimiro. Collection and analysis of data collected from the injection substations that supply electricity to mile 2 Diobu, Port Harcourt was the first consideration. The distribution network was modeled in Electrical Transient Analyzer Program (ETAP) using Newton-Raphson Load Flow equations. The simulation result of the existing condition network shows that the network has low voltage profile problem on Nsukka network and overloading of distribution transformers on Ojoto networks. The following optimization techniques are applied: up-gradation of distribution transformers, and transformer load tap changer to improve the distribution network for Mile 2 Diobu, Port Harcourt electrical power network. The simulation result of the improved distribution network for Mile 2 Diobu, Port Harcourt power network shows that the voltage profile Nsukka network has improved within the statutory limit which is between 95.0 -105.0% and the loading of the distribution transformers on Ojoto and Nsukka networks are all below 70% required capacity. Keywords: Optimization, Energy Efficiency Distribution

Development of a Platform for Securing Interoperability between Components in a Carbon-Free Island Microgrid Energy Management System

The rigidity of information technology (IT) has been hindering the development of various businesses regarding energy management systems (EMSs) of power networks, although this area has become more diversified, resulting in changes of elements in the systems due to the introduction of renewable energy (RE) and the new energy industry. In order to effectively accommodate these changes, EMSs should be developed in a structure with a standard-based interface, which can secure interoperability between components in the EMS. In previous studies, the common information model (CIM) proposed by IEC TC57 has been utilized for developing EMSs of power networks, but there are gaps between the existing CIM and an information model for the EMSs of carbon-free island microgrids (MGs), which are a newly introduced form of power network covering multiple islands for reducing carbon emissions. This paper proposes a CIM-based software platform for a carbon-free island MG-EMS to efficiently operate the power network and secure interoperability between components in the MG-EMS. Concerning service restoration of the power network, use cases and business objects representing information exchanged between the components in the EMS are derived, and the existing CIM is extended based on the results of the gap analysis in order to provide necessary information on the MG-EMS. The validity of the proposed platform is verified by exchanging payloads between components in the MG-EMS based on the profile extracted from the extended CIM. Furthermore, the performance of the proposed platform regarding data size and speed of data exchange is presented. Based on the case study results, it is concluded that the proposed platform based on the extended CIM can exchange data between the components in the MG-EMS, achieving reasonable data size and speed of data exchange with the help of the interoperability between components in the carbon-free island MG-EMS.

Susceptibility Assessment for Landslide Initiated along Power Transmission Lines

The power network has a long transmission span and passes through wide areas with complex topography setting and various human engineering activities. They lead to frequent landslide hazards, which cause serious threats to the safe operation of the power transmission system. Thus, it is of great significance to carry out landslide susceptibility assessment for disaster prevention and mitigation of power network. We, therefore, undertake an extensive analysis and comparison study between different data-driven methods using a case study from China. Several susceptibility mapping results were generated by applying a multivariate statistical method (logistic regression (LR)) and a machine learning technique (random forest (RF)) separately with two different mapping-units and predictor sets of differing configurations. The models’ accuracies, advantages and limitations are summarized and discussed using a range of evaluation criteria, including the confusion matrix, statistical indexes, and the estimation of the area under the receiver operating characteristic curve (AUROC). The outcome showed that machine learning method is well suitable for the landslide susceptibility assessment along transmission network over grid cell units, and the accuracy of susceptibility models is evolving rapidly from statistical-based models toward machine learning techniques. However, the multivariate statistical logistic regression methods perform better when computed over heterogeneous slope terrain units, probably because the number of units is significantly reduced. Besides, the high model predictive performances cannot guarantee a high plausibility and applicability of subsequent landslide susceptibility maps. The selection of mapping unit can produce greater differences on the generated susceptibility maps than that resulting from the selection of modeling methods. The study also provided a practical example for landslide susceptibility assessment along the power transmission network and its potential application in hazard early warning, prevention, and mitigation.

Modelling of geomagnetically induced currents in the Czech transmission grid

We investigate the maximum expected magnitudes of the geomagnetically induced currents (GICs) in the Czech transmission power network. We compute a model utilising the Lehtinen–Pirjola method, considering the plane-wave model of the geoelectric field, and using the transmission network parameters kindly provided by the operator. We find that the maximum amplitudes expected in the nodes of the Czech transmission grid during the Halloween storm-like event are about 15 A. For the “extreme-storm” conditions with a 1-V/km geoelectric field, the expected maxima do not exceed 40 A. We speculate that the recently proven statistical correlation between the increased geomagnetic activity and anomaly rate in the power grid may be due to the repeated exposure of the devices to the low-amplitude GICs. Graphical Abstract