The Forecasting Track of Power Grid Operation State Based on Risk Assessment

2014 ◽  
Vol 1008-1009 ◽  
pp. 454-460
Author(s):  
Qi Rui Chen ◽  
Da Hai You ◽  
Cheng Long ◽  
Yang Zou ◽  
Jin Hu ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fuzzy Clustering ◽  
Fuzzy Inference ◽  
Power Grid ◽  
Risk Index ◽  
External Environment ◽  
Time Operation ◽  
Real Time Operation ◽  
State Of Power ◽  
Grid Operation

As the interconnected power grid becomes increasingly complicated and external environment is changeable, the difficulty of power grid dispatching is increased. Thus, the forecasting track of power grid operation state based on risk assessment can be used to predict the operation trend of power grid state and provide a reference for real-time operation state and guide power grid dispatching. Based on overall power grid risk index system, the operation trend for a period in the future is predicted to diagnose the state of power grid by using fuzzy inference, fuzzy clustering and AHP. In this paper, Ningxia Power Grid is simulated as an example to describe the forecasting track of its operation state in the next 100 minutes. The results present that the track can be used to analyze the causes, which increase or decrease the risk degree of overall power grid, then major leading factors are specifically analyzed. And the track can be also used as guidance for dispatching operators to take measures. Furthermore, the track is proved to be reasonable.

scholarly journals Fuzzy logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

2018 ◽  
Author(s):  
Abdussalam Salama ◽  
Reza Saatchi ◽  
Derek Burke
Keyword(s):  
Computer Networks ◽  
Adaptive Sampling ◽  
Fuzzy Inference ◽  
Information Access ◽  
Inference System ◽  
Large Computer ◽  
Transmission Parameters ◽  
Time Operation ◽  
Real Time Operation ◽  
Health Applications

Electronic-health applications rely on large computer networks to facilitate patients' information access and to communicate various types of medical data. To examine the effectiveness of these networks, the traffic parameters need to be analysed. Due to quantity of the information carrying packets, examining each packet's transmission parameters individually is not practical, especially when a real time operation is needed. Sampling allows a subset of packets that accurately represents the original traffic to be formed. In this study an adaptive sampling method based on regression and fuzzy inference system was developed. It dynamically updates the number of packets sampled by responding to the traffic variations. Its performance was found to be superior to the conventional non-adaptive sampling methods.

scholarly journals Risk Assessment for the Power Grid Dispatching Process Considering the Impact of Cyber Systems

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1084 ◽  
Author(s):  
Biyun Chen ◽  
Haoying Chen ◽  
Yiyi Zhang ◽  
Junhui Zhao ◽  
Emad Manla
Keyword(s):  
Risk Assessment ◽  
Power Grid ◽  
Risk Index ◽  
Risk Process ◽  
Test System ◽  
Assessment Model ◽  
Risk Assessments ◽  
Cyber Physical System ◽  
Failure Model ◽  
The Impact

Power grid dispatching is a high-risk process, and its execution depends on an available cyber system. However, the effects of cyber systems have not caught enough attention in current research on risk assessments in dispatching processes, which may cause optimistic risk results. In order to solve this problem, this paper proposes a risk assessment model that considers the impact of a cyber system on power grid dispatching processes. Firstly, a cyber-physical switchgear state model that integrates the reliability states of both cyber system functions and switchgears is proposed, based on the transition of switchgear states in the dispatching process. Then, the potential effects of each operating step on power grid states are analyzed considering the failure model of cyber-physical system (CPS) components. The risk probabilities and consequences of the power grid states are calculated to quantify the risk index. Finally, the workings and effectiveness of this model are illustrated using the IEEE Reliability Test System-1979.

scholarly journals Research on Identification of Power Grid Weakness Based on Bayesian Inference

2021 ◽  
Author(s):  
Jiang Hu ◽  
Wei Li ◽  
Wenxia Liu ◽  
Xianggang He ◽  
Yu Zhang
Keyword(s):  
Bayesian Network ◽  
Power Transmission ◽  
Weak Link ◽  
Power Grid ◽  
Distribution Factor ◽  
Grid Data ◽  
Maximum Probability ◽  
Time Operation ◽  
Probability Table ◽  
Real Time Operation

With the gradual reform and development of the power grid, it is of great significance to study how to effectively identify and evaluate the weak links of the power grid for the actual planning, construction, and operation of the power grid. This paper analyzed the power grid’s historical component data and real-time operation state parameters. We established a weak link identification model based on Bayesian reasoning. Firstly, we constructed the node branch Bayesian network according to the network topology relationship. The power transmission distribution factor is modified according to the historical operation load of the grid components, and the conditional probability table is calculated based on the grid structure; finally, we used the maximum possible explanation algorithm in the Bayesian network. The weakness degree of all components in the network is calculated, and the maximum probability weak link sequence is obtained. The correctness and effectiveness of the proposed method are verified by IEEE 39 bus simulation and regional power grid data.

scholarly journals Exploring 3D Human Action Recognition Using STACOG on Multi-View Depth Motion Maps Sequences

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3642
Author(s):  
Mohammad Farhad Bulbul ◽  
Sadiya Tabussum ◽  
Hazrat Ali ◽  
Wenli Zheng ◽  
Mi Young Lee ◽  
...  
Keyword(s):  
Action Recognition ◽  
Depth Map ◽  
Human Action Recognition ◽  
Human Action ◽  
Collaborative Representation ◽  
Auto Correlation ◽  
Time Operation ◽  
Real Time Operation ◽  
Benchmark Datasets ◽  
Depth Motion Maps

This paper proposes an action recognition framework for depth map sequences using the 3D Space-Time Auto-Correlation of Gradients (STACOG) algorithm. First, each depth map sequence is split into two sets of sub-sequences of two different frame lengths individually. Second, a number of Depth Motion Maps (DMMs) sequences from every set are generated and are fed into STACOG to find an auto-correlation feature vector. For two distinct sets of sub-sequences, two auto-correlation feature vectors are obtained and applied gradually to L2-regularized Collaborative Representation Classifier (L2-CRC) for computing a pair of sets of residual values. Next, the Logarithmic Opinion Pool (LOGP) rule is used to combine the two different outcomes of L2-CRC and to allocate an action label of the depth map sequence. Finally, our proposed framework is evaluated on three benchmark datasets named MSR-action 3D dataset, DHA dataset, and UTD-MHAD dataset. We compare the experimental results of our proposed framework with state-of-the-art approaches to prove the effectiveness of the proposed framework. The computational efficiency of the framework is also analyzed for all the datasets to check whether it is suitable for real-time operation or not.

scholarly journals Environmental Risk Assessment of Petroleum Activities in Surface Sediments, Suez Gulf, Egypt

2021 ◽  
Vol 9 (5) ◽  
pp. 473
Author(s):  
Magda M. Abou El-Safa ◽  
Mohamed Gad ◽  
Ebrahem M. Eid ◽  
Ashwaq M. Alnemari ◽  
Mohammed H. Almarshadi ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Heavy Metal ◽  
Surface Sediment ◽  
Surface Sediments ◽  
Surf Zone ◽  
Risk Index ◽  
Negative Relationship ◽  
Anthropogenic Sources ◽  
Suez Gulf

The present study focuses on the risk assessment of heavy metal contamination in aquatic ecosystems by evaluating the current situation of heavy metals in seven locations (North Amer El Bahry, Amer, Bakr, Ras Gharib, July Water Floud, Ras Shokeir, and El Marageen) along the Suez Gulf coast that are well-known representative sites for petroleum activities in Egypt. One hundred and forty-six samples of surface sediments were carefully collected from twenty-seven profiles in the intertidal and surf zone. The hydrochemical parameters, such as pH and salinity (S‰), were measured during sample collection. The mineralogy study was carried out by an X-ray diffractometer (XRD), and the concentrations of Al, Mn, Fe, Cr, Cu, Co, Zn, Cd, and Pb were determined using inductively coupled plasma mass spectra (ICP-MS). The ecological risks of heavy metals were assessed by applying the contamination factor (CF), enrichment factor (EF), geoaccumulation index (Igeo), pollution load index (PLI), and potential ecological risk index (RI). The mineralogical composition mainly comprised quartz, dolomites, calcite, and feldspars. The average concentrations of the detected heavy metals, in descending order, were Al > Fe > Mn > Cr > Pb > Cu > Zn > Ni > Co > Cd. A non-significant or negative relationship between the heavy metal concentration in the samples and their textural grain size characteristics was observed. The coastal surface sediment samples of the Suez Gulf contained lower concentrations of heavy metals than those published for other regions in the world with petroleum activities, except for Al, Mn, and Cr. The results for the CF, EF, and Igeo showed that Cd and Pb have severe enrichment in surface sediment and are derived from anthropogenic sources, while Al, Mn, Fe, Cr, Co, Ni, Cu, and Zn originate from natural sources. By comparison, the PLI and RI results indicate that the North Amer El Bahry and July Water Floud are considered polluted areas due to their petroleum activities. The continuous monitoring and assessment of pollutants in the Suez Gulf will aid in the protection of the environment and the sustainability of resources.

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