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"Electrical networks"
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Direct Current Algorithm for Protection Relays of 6–35 kV Electric Networks
The goal of this research work is to study the issues of protecting the electric equipment and cable and overhead transmission lines of 6–35 kV electric networks from overvoltage and earth fault as well as search for possibilities of upgrading the protection of these networks during operation. The methodology of this study is based on combining the system-level analysis of the operating principles of distributing networks of energy-consuming facilities with the analytical survey of the backbone aspects of the relay protection of the specified electric networks from overvoltage and earth faults. This protection is based on using the developed algorithm that allows the separation of direct current from the zero sequence current of the damaged outgoing feeder during an earth fault. Experimental findings on the Converter’s operation, which amplifies signals from the Zero-Sequence Current Transformer (ZSCT) of an outgoing feeder and extracts direct current (Idc), reveal its significance in SPEF protection for 6–35 kV networks. Idc is consistently present during various SPEF modes, and a four-to-sixfold multiplier circuit optimizes accuracy and sensitivity. In conclusion, the Idc algorithm’s advantage lies in its comprehensive coverage of ZSC harmonics, enhancing sensitivity and reliability, making it suitable for centralized protection relays. With a simple circuit design and immunity to electromagnetic interference, the Converter addresses the need for advanced protection measures in energy facilities’ electric systems.
Journal Article
Electrical networks and the grove algebra
We study the ring of regular functions on the space of planar electrical networks, which we coin the grove algebra. This algebra is an electrical analog of the Plücker ring studied classically in invariant theory. We develop the combinatorics of double groves to study the grove algebra, and find a quadratic Gröbner basis for the grove ideal.
Journal Article
Digital Audio Tampering Detection Based on Deep Temporal–Spatial Features of Electrical Network Frequency
In recent years, digital audio tampering detection methods by extracting audio electrical network frequency (ENF) features have been widely applied. However, most digital audio tampering detection methods based on ENF have the problems of focusing on spatial features only, without effective representation of temporal features, and do not fully exploit the effective information in the shallow ENF features, which leads to low accuracy of audio tamper detection. Therefore, this paper proposes a new method for digital audio tampering detection based on the deep temporal–spatial feature of ENF. To extract the temporal and spatial features of the ENF, firstly, a highly accurate ENF phase sequence is extracted using the first-order Discrete Fourier Transform (DFT), and secondly, different frame processing methods are used to extract the ENF shallow temporal and spatial features for the temporal and spatial information contained in the ENF phase. To fully exploit the effective information in the shallow ENF features, we construct a parallel RDTCN-CNN network model to extract the deep temporal and spatial information by using the processing ability of Residual Dense Temporal Convolutional Network (RDTCN) and Convolutional Neural Network (CNN) for temporal and spatial information, and use the branch attention mechanism to adaptively assign weights to the deep temporal and spatial features to obtain the temporal–spatial feature with greater representational capacity, and finally, adjudicate whether the audio is tampered with by the MLP network. The experimental results show that the method in this paper outperforms the four baseline methods in terms of accuracy and F1-score.
Journal Article
Enhancing Renewable Energy Integration: A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks
In the contemporary era, the global expansion of electrical grids is propelled by various renewable energy sources (RESs). Efficient integration of stochastic RESs and optimal power flow (OPF) management are critical for network optimization. This study introduces an innovative solution, the Gaussian Bare-Bones Levy Cheetah Optimizer (GBBLCO), addressing OPF challenges in power generation systems with stochastic RESs. The primary objective is to minimize the total operating costs of RESs, considering four functions: overall operating costs, voltage deviation management, emissions reduction, voltage stability index (VSI) and power loss mitigation. Additionally, a carbon tax is included in the objective function to reduce carbon emissions. Thorough scrutiny, using modified IEEE 30-bus and IEEE 118-bus systems, validates GBBLCO’s superior performance in achieving optimal solutions. Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios: total cost with valve point effects, total cost with emission and carbon tax, total cost with prohibited operating zones, active power loss optimization, voltage deviation optimization and enhancing voltage stability index (VSI). GBBLCO outperforms conventional techniques in each scenario, showcasing rapid convergence and superior solution quality. Notably, GBBLCO navigates complexities introduced by valve point effects, adapts to environmental constraints, optimizes costs while considering prohibited operating zones, minimizes active power losses, and optimizes voltage deviation by enhancing the voltage stability index (VSI) effectively. This research significantly contributes to advancing OPF, emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima. GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems, offering a promising solution for the evolving needs of renewable energy-integrated power grids.
Journal Article
Zeroing Neural Network Based on Neutrosophic Logic for Calculating Minimal-Norm Least-Squares Solutions to Time-Varying Linear Systems
This paper presents a dynamic model based on neutrosophic numbers and a neutrosophic logic engine. The introduced neutrosophic logic/fuzzy adaptive Zeroing Neural Network dynamic is termed NSFZNN and represents an improvement over the traditional Zeroing Neural Network (ZNN) design. The model aims to calculate the matrix pseudo-inverse and the minimum-norm least-squares solutions of time-varying linear systems. The improvement of the proposed model emerges from the advantages of neutrosophic logic over fuzzy and intuitionistic fuzzy logic in solving complex problems associated with predictions, vagueness, uncertainty, and imprecision. We use neutrosphication, de-fuzzification, and de-neutrosophication instead of fuzzification and de-fuzzification exploited so far. The basic idea is based on the known advantages of neutrosophic systems compared to fuzzy systems. Simulation examples and engineering applications on localization problems and electrical networks are presented to test the efficiency and accuracy of the proposed dynamical system.
Journal Article
Practical distributed voltage control method for efficient and equitable intervention of distributed devices
Growing penetrations of distributed photovoltaic (PV) generation in low-voltage electrical networks are raising new challenges for electricity industry operation. Voltage rise is a particular concern and new distributed voltage management techniques have been proposed in the literature. In this study, a novel distributed voltage control method is presented. The method is designed to be used by both PV systems and controllable loads and uses both a voltage and a power set point to manage control. These set points, along with a voltage sensitivity measure, are then used to control PV system generation and load-shedding. The objective of the control is to keep voltage levels within operational limits with reasonable accuracy. The method is practical; local measurements of net-power and voltage are used with no additional communication infrastructure required. The proposed method is compared to a power set point only and a voltage set point only control method. Results show the proposed method improves on both methods in terms of both voltage accuracy and the equity of intervention.
Journal Article
Improved adaptive gaining-sharing knowledge algorithm with FDB-based guiding mechanism for optimization of optimal reactive power flow problem
Optimal reactive power flow (ORPF) is of great importance for the electrical reliability and economic operation of modern power systems. The integration of distributed generations (DGs) and two-terminal high voltage direct current (HVDC) systems into electrical networks has further complicated the ORPF problem. Due to the high computational complexity of the ORPF problem, a powerful and robust optimization algorithm is required to solve it. This paper proposes a powerful metaheuristic algorithm namely fitness-distance balance-based adaptive gaining-sharing knowledge (FDBAGSK). In the performance evaluation, 39 IEEE CEC benchmark functions are used to compare FDBAGSK with the original AGSK algorithm. Moreover, the proposed algorithm is applied to perform the ORPF task in modified IEEE 30- and IEEE 57-bus test systems. The effectiveness of the FDBAGSK method was tested for the optimization of three non-convex objectives: active power loss, voltage deviation and voltage stability index. The ORPF results obtained from the FDBAGSK algorithm are compared with other optimization algorithms in the literature. Given that all results are together, it has been observed that FDBAGSK is an effective method that can be used in solving global optimization and constrained real-world engineering problems.
Journal Article
An audio tampering detection framework for low SNR conditions based on modified CZT
Extracting the electric network frequency (ENF) from digital audio signals is a crucial means of forensic evidence. However, ENF signal extraction is susceptible to noise, making it challenging to establish a reliable matching relationship with the reference frequency database, especially under low signal-to-noise ratio (SNR) conditions. To solve this problem, an ENF extraction and tampering detection framework (ENF-ETD) for low SNR conditions is proposed in this article. Firstly, a modified Chirp Z-transform (MCZT) method is proposed to extract the ENF signal in digital audio. Subsequently, by comparing with the actual grid frequency, the Pearson correlation coefficient (PCC) and Euclidean distance (ED) are used to evaluate the accuracy of ENF estimation and determine whether the audio has been tampered with. Finally, the simulations and hardware-based experiments verify the proposed ENF-ETD framework’s effectiveness in noise immunity and digital audio tampering detection.
•To improve the representation ability of ENF components, a modified Chirp Z-transform (MCZT) method is proposed.•An ENF extraction and tampering detection (ENF-ETD) framework for low SNR digital audio signal is proposed.•Simulations are conducted to analyze the performance of the MCZT method for ENF extraction under different SNR conditions.•The different experimental results show that the framework has superior performance compared with some advanced methods.
Journal Article
A review of condition monitoring techniques and diagnostic tests for lifetime estimation of power transformers
Power transformers are a key component of electrical networks, and they are both expensive and difficult to upgrade in a live network. Many utilities monitor the condition of the components that make up a power transformer and use this information to minimize the outage and extend the service life. Routine and diagnostic tests are currently used for condition monitoring and appraising the ageing and defects of the core, windings, bushings and tap changers of power transformers. To accurately assess the remaining life and failure probability, methods have been developed to correlate results from different routine and diagnostic tests. This paper reviews established tests such as dissolved gas analysis, oil characteristic tests, dielectric response, frequency response analysis, partial discharge, infrared thermograph test, turns ratio, power factor, transformer contact resistance, and insulation resistance measurements. It also considers the methods widely used for health index, lifetime estimation, and probability of failure. The authors also highlight the strengths and limitations of currently available methods. This paper summarizes a wide range of techniques drawn from industry and academic sources and contrasts them in a unified frame work.
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