Power Grid Monitoring System Research Articles

Security Assessment of Coal Mine Power Grid Voltage Based on an Improved AHP-FCE

The voltage level of a coal mine power grid directly affects the starting and normal operation of the electric motor, but the current grid monitoring system cannot use real-time grid structure parameters and power flow distribution data to conduct early warning research on the running status and voltage level of the power grid, so it cannot eliminate hidden danger in the operation of the power grid. Therefore, it is necessary to accurately and effectively evaluate the voltage safety level of the coal mine power grid. In this paper, an improved AHP-FCE method is proposed to evaluate the voltage safety level of a coal mine power grid. By replacing the comparison judgment matrix with the interval judgment matrix, the objectivity of the weight vector is improved while retaining expert subjectivity. According to the real-time power flow distribution data of the power grid monitoring system, the current voltage safety level of the power grid is evaluated. The results show that the improved AHP-FCE method can accurately evaluate the current grid voltage level, analyze the voltage development trend, give early warnings regarding coal mine grid voltage operation, and improve the monitoring function of the power monitoring system.

Enabling Parallelized-QEMU for Hardware/Software Co-Simulation Virtual Platforms

Presently, the trend is to increase the number of cores per chip. This growth is appreciated in Multi-Processor System-On-Chips (MPSoC), composed of more cores in heterogeneous and homogeneous architectures in recent years. Thus, the difficulty of verification of this type of system has been great. The hardware/software co-simulation Virtual Platforms (VP) are presented as a perfect solution to address this complexity, allowing verification by simulation/emulation of software and hardware in the same environment. Some works parallelized the software emulator to reduce the verification times. An example of this parallelization is the QEMU (Quick EMUlator) tool. However, there is no solution to synchronize QEMU with the hardware simulator in this new parallel mode. This work analyzes the current software emulators and presents a new method to allow an external synchronization of QEMU in its parallelized mode. Timing details of the cores are taken into account. In addition, performance analysis of the software emulator with the new synchronization mechanism is presented, using: (1) a boot Linux for MPSoC Zynq-7000 (dual-core ARM Cortex-A9) (Xilinx, San Jose, CA, USA); (2) an FPGA-Linux co-simulation of a power grid monitoring system that is subsequently implemented in an industrial application. The results show that the novel synchronization mechanism does not add any appreciable computational load and enables parallelized-QEMU in hardware/software co-simulation virtual platforms.

Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

Companies that produce energy transmit it to any or all households via a power grid, which is a regulated power transmission hub that acts as a middleman. When a power grid fails, the whole area it serves is blacked out. To ensure smooth and effective functioning, a power grid monitoring system is required. Computer vision is among the most commonly utilized and active research applications in the world of video surveillance. Though a lot has been accomplished in the field of power grid surveillance, a more effective compression method is still required for large quantities of grid surveillance video data to be archived compactly and sent efficiently. Video compression has become increasingly essential with the advent of contemporary video processing algorithms. An algorithm’s efficacy in a power grid monitoring system depends on the rate at which video data is sent. A novel compression technique for video inputs from power grid monitoring equipment is described in this study. Due to a lack of redundancy in visual input, traditional techniques are unable to fulfill the current demand standards for modern technology. As a result, the volume of data that needs to be saved and handled in live time grows. Encoding frames and decreasing duplication in surveillance video using texture information similarity, the proposed technique overcomes the aforementioned problems by Robust Particle Swarm Optimization (RPSO) based run-length coding approach. Our solution surpasses other current and relevant existing algorithms based on experimental findings and assessments of different surveillance video sequences utilizing varied parameters. A massive collection of surveillance films was compressed at a 50% higher rate using the suggested approach than with existing methods.

Survey of machine learning methods for detecting false data injection attacks in power systems

Over the last decade, the number of cyber attacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, false data injection attacks (FDIAs) are a class of cyber-attacks against power grid monitoring systems. Adversaries can successfully perform FDIAs to manipulate the power system state estimation (SE) by compromising sensors or modifying system data. SE is an essential process performed by the energy management system towards estimating unknown state variables based on system redundant measurements and network topology. SE routines include bad data detection algorithms to eliminate errors from the acquired measurements, e.g. in case of sensor failures. FDIAs can bypass BDD modules to inject malicious data vectors into a subset of measurements without being detected, and thus manipulate the results of the SE process. To overcome the limitations of traditional residual-based BDD approaches, data-driven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation of sensor data due to their fast execution times and accurate results. This study provides a comprehensive review of the most up-to-date machine learning methods for detecting FDIAs against power system SE algorithms.

Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models

PurposeFor the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing. This paper proposes a fault classification algorithm based on Gaussian mixture model (GMM), which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.Design/methodology/approachThe algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy (MYT) decomposition under each normal distribution in the GMM. Then, the weight value of GMM is used to calculate weighted classification value of fault detection and separation, and by comparing the actual control limits with the classification result of GMM, the fault classification results are obtained.FindingsThe experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.Originality/valueThe proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.

A Machine Learning-based Approach to Build Zero False-Positive IPSs for Industrial IoT and CPS with a Case Study on Power Grids Security

Intrusion Prevention Systems (IPS), have long been the first layer of defense against malicious attacks. Most sensitive systems employ instances of them (e.g. Firewalls) to secure the network perimeter and filter out attacks or unwanted traffic. A firewall, similar to classifiers, has a boundary to decide which traffic sample is normal and which one is not. This boundary is defined by configuration and is managed by a set of rules which occasionally might also filter normal traffic by mistake. However, for some applications, any interruption of the normal operation is not tolerable e.g. in power plants, water distribution systems, gas or oil pipelines, etc. In this paper, we design a learning firewall that receives labelled samples and configures itself automatically by writing preventive rules in a conservative way that avoids false alarms. We design a new family of classifiers, called -classifiers, that unlike the traditional ones which merely target accuracy, rely on zero false-positive as the metric for decision making. First, we analytically show why naive modification of current classifiers like SVM does not yield acceptable results and then, propose a generic iterative algorithm to accomplish this goal. We use the proposed classifier with CART at its heart to build a firewall for a Power Grid Monitoring System. To further evaluate the algorithm, we additionally test it on KDD CUP'99 dataset. The results confirm the effectiveness of our approach.

Research on Transmission Lines Early Warning Technology Based on Deep Learning

At the present stage, high-voltage transmission lines are distributed in long distance chains, with large space span, complicated meteorological and geographical environment. The operating environment of the transmission lines is poor. Hence, manual patrol and maintenance are difficult. Various faults are very likely to affect the safe and stable operation of the power grid system. Therefore, this paper proposes a transmission lines early warning method based on Deep Learning. Through object detection technology of Deep Learning, intrusion objects in the monitoring screen are automatically identified, at same time, their positions and types are marked. The experimental results show that this method has high accuracy and is suitable for the current power grid monitoring system.

An energy-efficient query based on variable region for large-scale smart grid

The state-of-the-art query techniques in power grid monitoring systems focus on querying history data, which typically introduces an unwanted lag when the systems try to discover emergency situations. The monitoring data of large-scale smart grids are massive, dynamic and highly dimensional, so global query, the method widely adopted in continuous queries in Wireless Sensor Networks (WSN), is rendered not suitable for its high energy consumption. The situation is even worse with increasing application complexity. We propose an energy-efficient query technique for large-scale smart grids based on variable regions. This method can query an arbitrary region based on variable physical windows, and optimizes data retrieve paths by a key nodes selection strategy. According to the characteristics of sensing data, we introduce an efficient filter into the each query subtree to keep non-skyline data from being retrieved. Experimental results show that our method can efficiently return the overview situation of any query region. Compared to TAG and ESA, the average query efficiency of our approach is improved by 79% and 46%, respectively; the total energy consumption of regional query is decreased by 82% and 50%, respectively.

A dominance based rough set classification system for fault diagnosis in electrical smart grid environments

Nowadays, power grid monitoring systems are shifting towards more disseminating and distributive operations. The diagnosis of faults using knowledge discovery techniques has become an essential component of the process, accounting to the challenges faced by the electrical power monitoring systems. The system's operators employ various energy management techniques which play important roles in the overall management, reliability and operational sustainability of smart grid utilities. Sometimes, these systems are disrupted by events like a short circuit in the system or any external incursion that could pose a threat to the public safety as well as to the critical infrastructure of the grid. This paper aims at providing a robust system for fault diagnosis using the status of an intelligent electronic device and circuit breakers which can be tripped by any kind of fault. To protect the system from vulnerabilities and different kinds of faults, a multilayered fault estimation classifier, based on the Dominance based rough set is proposed. This technique provides an effective solution to the system's operator for the proper diagnosis of the faults and intrusions by classifying the state of the system. In addition to this, the operator can take preventative measures to protect the system from further damage.

Pragmatic multi-stage simulated annealing for optimal placement of synchrophasor measurement units in smart power grids

Conventional power grids across the globe are reforming to smart power grids with cutting edge technologies in real time monitoring and control methods. Advanced real time monitoring is facilitated by incorporating synchrophasor measurement units such as phasor measurement units (PMUs) to the power grid monitoring system. Several physical and economic constraints limit the deployment of PMUs in smart power grids. This paper proposes a pragmatic multi-stage simulated annealing (PMSSA) methodology for finding the optimal locations in the smart power grid for installing PMUs in conjunction with existing conventional measurement units (CMUs) to achieve a complete observability of the grid. The proposed PMSSA is much faster than the conventional simulated annealing (SA) approach as it utilizes controlled uphill and downhill movements during various stages of optimization. Moreover, the method of integrating practical phasor measurement unit (PMU) placement conditions like PMU channel limits and redundant placement can be easily handled. The efficacy of the proposed methodology has been validated through simulation studies in IEEE standard bus systems and practical regional Indian power grids.

A Reliable IEEE 802.15.4 Model for Cyber Physical Power Grid Monitoring Systems

Cyber physical systems (CPSs) can significantly improve the resiliency of the smart grid. In CPSs, real time and reliable monitoring require an accurate and stable model of the wireless sensor network (WSN)-based monitoring system. Furthermore, WSNs require strict quality of service (QoS) provisioning as the data generated by the monitored equipment is generally delay and reliability-sensitive. QoS provisioning in WSNs has been widely studied in the literature where most of the work addresses the issue by QoS-aware protocol design. However, analytical models that consider delay, throughput, and power consumption have not matured for CPSs. In this paper, we propose a Markov-based model for cluster-tree WSN topologies that enhances the stability of the WSNs. Cluster-tree deployments are particularly of interest to cyber-physical power grid monitoring systems since they are suitable for large-scale deployments. We perform an exhaustive performance evaluation using different traffic and network conditions in star and cluster-tree WSN topologies. Furthermore, we test the accuracy of our model by performing simulations in environments that are consistent with the analytical model.