If the fault sources can be located in a timely and accurate manner when an unmanned aerial vehicle (UAV) has an early minor fault, a major accident can be avoided. In light of the early fault of the six-rotor UAV motors, this paper proposes a fusion fault diagnosis model based on Conformal Fourier Transform (CFT) and the improved Self-Organizing Feature Map (SOM) neural network. Firstly, to overcome the weakening of UAV fault information caused by external interference and solve the problem that the non-uniform distribution of the collected flight data affects the accuracy of the frequency domain processing, on the basis of changing the processing method of the Fourier integral kernel and using high-order numerical integration, an improved Conformal Fourier Transform (CFT) algorithm optimized by spectrum refinement strategy is proposed. Then this algorithm is used to further intensify the main features and build a clear fault state in a cycle processing method. Secondly, an improved SOM neural network is designed. We use the unweighted average distance (UPGMA) of the agglomerative hierarchical clustering algorithm to detect the clustering areas of each pattern category of the data samples, and the weight vectors of different clustering areas are used to randomly initialize the connection weights of SOM network, thereby improving the clustering speed and convergence effect. On this basis, CFT algorithm is integrated into the improved SOM network to realize the multi-module collaboration strategy and finally form a new fusion fault diagnosis model.

/pdf/fusion-fault-diagnosis-model-for-six-rotor-uavs-based-on-4beeco6erc.pdf

Fusion Fault Diagnosis Model for Six-Rotor UAVs Based on Conformal Fourier Transform and Improved Self-Organizing Feature Map

The modern bulk power system operation is complex and dynamic, with rapidly increasing inverter-based resources and active distribution systems. Therefore, high-speed monitoring is required to operate the power system reliably and efficiently. Transmission network topology processing (TNTP) is vital in power system control. Today’s TNTP is based on supervisory control and data acquisition (SCADA) system monitoring of relay signals (SMRS). Due to the slow data communication rate, SMRS cannot efficiently support the modern bulk power system’s energy management system (EMS) functions. In this study, a physics-based hierarchical TNTP (H-TNTP) approach based solely on node voltages and branch currents measurements is proposed utilizing artificial intelligence algorithms. H-TNTP includes the identification of substation configuration. The reliability of the H-TNTP is guaranteed by the design with inherent verification. If required, H-TNTP is capable of operating concurrently with the TNTP-SMRS. A power system with solar photovoltaic (PV) plants is utilized as a test system to illustrate the proposed H-TNTP approach. Results indicate that H-TNTP is fast with synchrophasor measurements. Furthermore, to demonstrate the application of the reliable and fast TNTP approach in EMSs, fast automatic generation control (AGC) during contingencies is studied. Typical results show that fast reconfiguration of AGC modes and dispatch factors leads to better frequency regulation.

An Efficient and Reliable Electric Power Transmission Network Topology Processing

The transmission network is the backbone of the power system. Fast power system network branch event identification is required to maintain security of the power system. Phasor measurement units (PMUs) provide synchronized voltage and current phasor measurements to a centralized phasor data concentrator (PDC). Thus, PMU measurements can be used for online branch event identification. A fast and efficient approach is required to ease the computational load. Learning vector quantization (LVQ) neural network algorithm is presented in this paper as a solution to identify branch events. The power system is highly distributed. Thus, a cellular computational network (CCN) is utilized to distribute a single large LVQ's computational load. The IEEE 12-bus benchmark power system is used to illustrate the CCN-LVQ approach. The power system is simulated on a realtime digital simulator. CCN-LVQ approach is shown to be fast, efficient and comparatively accurate for transmission network branch event identification.

LVQ Neural Network for Online Identification of Power System Network Branch Events

The complexity of the power system has increased due to recent grid modernization and active distribution systems. As a result, monitoring and controlling modern power systems have become challenging. Dynamic security assessment (DSA) in power systems is a critical operational situational awareness (OpSA) tool for the energy control center (ECC). State-of-the-art (SOTA) DSA has been based on traditional state estimation utilizing the supervisory control and data acquisition (SCADA) / phasor measurement units (PMU) and transmission network topology processing (TNTP) based on SCADA monitoring of relay signals (TNTP-SMRS). Due to the slow data rates of SCADA, these applications cannot efficiently support an online DSA tool. Furthermore, an inaccurate network model based on TNTP-SMRS can lead to erroneous DSA. In this paper, a distributed dynamic security assessment (D-DSA) based on multi-level distributed linear state estimation (D-LSE) and efficient and reliable hierarchical transmission network topology processing utilizing synchrophasor network (H-TNTP-PMU) has been proposed. The tool can be used in real-time operation at the ECC of modern power systems. D-DSA architecture comprises three levels, namely Level 1 - component level security assessment (substations and transmission lines), Level 2 - area level security assessment, and Level 3 - network level security assessment. D-DSA concurrently evaluates all available substations’ security in the substation security assessment (SSA) and all available transmission lines’ security in the transmission line security assessment (TSA). Under the area security assessment (ASA), all SSA and TSA in each area are separately integrated to assess the area SSI (ASI-SSI) and TSI (ASI-TSI). Subsequently, each area’s area-level security index (ASI) is calculated by fusing ASI-SSI and ASI-TSI. At the network level security assessment, network SSI (NSI-SSI) and TSI (NSI-TSI) are estimated by fusing all ASI-SSIs and ASI-TSI, respectively. Network level security index (NSI) is estimated by fusing the NSI-SSI and NSI-TSI in network security assessment (NSA). Typical results of D-DSA are presented for two test systems, the modified two-area four-machine power system model and the IEEE 68 bus power system model. Results indicate that the proposed D-DSA can complete the assessment accurately at the PMU data frame rate, enabling online security assessment regardless of the network size.

Distributed Dynamic Security Assessment for Modern Power System Operational Situational Awareness

SUMMARYAt present, there is active implementation of digitization policy. This leads to the active introduction of digital substations of the third architecture.The control center of any digital substation is the dispatch control panel and the SCADA system. For the dispatcher to interact with SCADA, the latter must have a human-machine interface (hereinafter HMI). Currently, HMI development is done manually in the SCADA editor or another graphic editor. This approach has several disadvantages: a lengthy process of coordination and approval, the presence of errors caused by the human factor. Thus, manual design and creation of HMI screen forms are accompanied by errors and also entail resource and time expenditures related to the lengthy stages of project documentation coordination.In this article, the authors propose a method that automatically generates screen forms of electrical schemes for the SCADA system based on SCD and CIM files of the substation’s electronic documentation. The use of the automated generation approach of HMI electrical schemes for digital substation’s SCADA systems allows minimizing the influence of the human factor in design, as well as reducing the development, coordination, and project approval time. Moreover, the proposed method is highly versatile and flexible, allowing its integration into any SCADA system.The article presents: functional and non-functional requirements for the developed method of automated HMI creation and its prototype, prototype operation scenarios when performing certain functions, method algorithms, and prototype architecture. In particular, requirements are formulated for input data in terms of SCD and CIM configurations and output data representing ready-made screen forms in XML and SVG formats.The article provides algorithms for generating screen forms of electrical schemes of the main substation scheme (overview) and schemes of switchgears of one voltage class. It also provides algorithms for determining the types of switchgears, the placement of symbolic graphic designations on the schemes, and the relative placement of Switchgears on the main substation scheme.Object models of input and output data of single-line electrical schemes have been developed. The object model of the input data of single-line electrical schemes considered the possibility of forming a scheme in the absence of one of the required input data files. The proposed output data structure allows, using an adapter, to integrate HMI into various SCADA systems used in digital substation.

Method of Automated Generation of Mnemonic Diagrams for Electric Circuits for the HMI of SCADA Systems of a Digital Substation

The electric power grid infrastructure is the most critically important man-made system in use today, particularly since all other infrastructures depend on the reliability of a robust electrical infrastructure. The smart grid transformation enables the real-time situational awareness through synchronized measurements. The communication network capable of bi-directional communication is responsible for the full connectivity of all the measurement and control units. In the power system, the transmission network is the bridge between the bulk generation and distribution system. The reliability of the power system is highly dependent on the successful operation of the geographically distributed transmission network and its components. Power system network branch outages, which may occur due to number of reasons, may result in either partial or complete blackout of the power system. The smart grid is a cyber-physical system. The cyber-physical systems are vulnerable to cyber and physical attacks. In this paper transmission network branch outage identification using phasor measurement units is investigated. Real-time digital simulator based simulation platform has been used. Admittance matrix based approach and neural network approach are investigated and performances evaluated.

Online Identification of Power System Network Branch Events

Online identification of power system network branches is critical in modern electric power system operation. Availability of phasor measurement units (PMUs) can be used to identify branch events. Due to complexity of the power system, a distributed cellular computational network (CCN) is proposed. Comparison of centralized and distributed neural network based power system network branch events identification is studied. IEEE 12-bus benchmark power system is simulated on real-time digital simulator platform for this study. CCN based distributed neural network approach is computationally efficient compared to centralized approach.

Distributed Identification of Power System Network Branch Events

State estimation (SE) is an important energy management system application for power system operations. Linear state estimation (LSE) is a variant of SE based on linear relationships between state variables and measurements. LSE estimates system state variables, including bus voltage magnitudes and angles in an electric power transmission network, using a network model derived from the topology processor and measurements. Phasor measurement units (PMUs) enable the implementation of LSE by providing synchronized high-speed measurements. However, as the size of the power system increases, the computational overhead of the state-of-the-art (SOTA) LSE grows exponentially, where the practical implementation of LSE is challenged. This paper presents a distributed linear state estimation (D-LSE) at the substation and area levels using a hierarchical transmission network topology processor (H-TNTP). The proposed substation-level and area-level D-LSE can efficiently and accurately estimate system state variables at the PMU rate, thus enhancing the estimation reliability and efficiency of modern power systems. Network-level LSE has been integrated with H-TNTP based on PMU measurements, thus enhancing the SOTA LSE and providing redundancy to substation-level and area-level D-LSE. The implementations of D-LSE and enhanced LSE have been investigated for two benchmark power systems, a modified two-area four-machine power system and the IEEE 68 bus power system, on a real-time digital simulator. The typical results indicate that the proposed multilevel D-LSE is efficient, resilient, and robust for topology changes, bad data, and noisy measurements compared to the SOTA LSE.

Multilevel Distributed Linear State Estimation Integrated with Transmission Network Topology Processing

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