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Vietnam’s economy has been growing rapidly in the last 20 years, leading to significant increases in energy consumption as well as in carbon emissions. Most electricity is consumed by loads of industry and construction due to the country’s socio-economic development strategy. An energy saving strategy cannot be achieved if the industry factories lack energy consumption data. The installation of energy monitoring systems can help to improve energy efficiency by supplying daily, monthly, and yearly energy consumption reports. Moreover, major energy-consuming enterprises in Vietnam must implement solutions for energy-efficient use as prescribed in the Law on Energy Efficient Use. Therefore, this study aimed to determine the impact of an energy monitoring system as an improvement solution for energy efficiency in a typical major energy-consuming enterprise in Vietnam. The study’s results, after six months, show that the total saved electricity after installing the power monitoring system was 191,923 kWh. The company saved approximately 19.584 USD and reduced emission to the environment by 139 tons of CO2. In addition, the return on investment time of power monitoring systems is about 14 months, while the annual energy costs of the factory can be reduced by about 9.62% per year. Therefore, power monitoring systems should be promoted in factories with different scales to control energy wastage in the domestic industry field.

This work is a survey of current trends in applications of PMUs. PMUs have the potential to solve major problems in the areas of power system estimation, protection, and stability. A variety of methods are being used for these purposes, including statistical techniques, mathematical transformations, probability, and AI. The results produced by the techniques reviewed in this work are promising, but there is work to be performed in the context of implementation and standardization. As the smart grid initiative continues to advance, the number of intelligent devices monitoring the power grid continues to increase. PMUs are at the center of this initiative, and as a result, each year more PMUs are deployed across the grid. Since their introduction, myriad solutions based on PMU-technology have been suggested. The high sampling rates and synchronized measurements provided by PMUs are expected to drive significant advancements across multiple fields, such as the protection, estimation, and control of the power grid. This work offers a review of contemporary research trends and applications of PMU technology. Most solutions presented in this work were published in the last five years, and techniques showing potential for significant impact are highlighted in greater detail. Being a relatively new technology, there are several issues that must be addressed before PMU-based solutions can be successfully implemented. This survey found that key areas where improvements are needed include the establishment of PMU-observability, data processing algorithms, the handling of heterogeneous sampling rates, and the minimization of the investment in infrastructure for PMU communication. Solutions based on Bayesian estimation, as well as those having a distributed architectures, show great promise. The material presented in this document is tailored to both new researchers entering this field and experienced researchers wishing to become acquainted with emerging trends.

Increasing demand for electricity and the modernization of power systems within competitive markets has induced power systems to operate close to their stability limits. Therefore, the continuous monitoring and control of power systems through voltage stability indices is urgently needed. This is the first-ever effort to examine more than 40 voltage stability indices based on their formulation, application, performance, and assessment measures. These indices are sorted based on a logical and chronological order considering the most recent indices to be applied worldwide. However, the generalizability of these indices in terms of multivariable objectives is limited. Despite its limitation, this study systematically reviews available indices in the literature within the past three decades to compile an integrated knowledge base with an up-to-date exposition. This is followed by a comparative analysis in terms of their similarity, functionality, applicability, formulation, merit, demerit, and overall performance. Also, a broad categorization of voltage stability indices is addressed. This study serves as an exhaustive roadmap of the issue and can be counted as a reference for planning and operation in the context of voltage stability for students, researchers, scholars, and practitioners.

Phasor measurement units (PMUs) are essential tools for monitoring, protection and control of power systems. The optimal PMU placement (OPP) problem refers to the determination of the minimal number of PMUs and their corresponding locations in order to achieve full network observability. This paper introduces a recursive Tabu search (RTS) method to solve the OPP problem. More specifically, the traditional Tabu search (TS) metaheuristic algorithm is executed multiple times, while in the initialisation of each TS the best solution found from all previous executions is used. The proposed RTS is found to be the best among three alternative TS initialisation schemes, in regard to the impact on the success rate of the algorithm. A numerical method is proposed for checking network observability, unlike most existing metaheuristic OPP methods, which are based on topological observability methods. The proposed RTS method is tested on the IEEE 14, 30, 57 and 118-bus test systems, on the New England 39-bus test system and on the 2383-bus power system. The obtained results are compared with other reported PMU placement methods. The simulation results show that the proposed RTS method finds the minimum number of PMUs, unlike earlier methods which may find either the same or even higher number of PMUs.

A method of measuring the power line wire sag using optical sensors that are insensitive to high electromagnetic fields was proposed. The advantage of this technique is that it is a non-invasive measurement of power line wire elongation using a unique optomechanical system. The proposed method replaces the sag of the power line wire with an extension of the control sample and then an expansion of the attached chirped fiber Bragg grating. This paper presents the results of the first measurements made on real aluminum-conducting steel-reinforced wire, frequently used for power line construction. It has been shown that the proper selection of the CFBG (chirped fiber Bragg grating) transducer and the appropriate choice of optical parameters of such a sensor will allow for high sensitivity of the line wire elongation and sag while reducing the sensitivity to the temperature. It has been shown that with a simple optomechanical system, a non-invasive measurement of the power line wire sag that is insensitive to temperature changes and the influence of high electromagnetic fields can be achieved.

Smart grid reliability and efficiency are critical for uninterrupted service, especially amidst growing demand and network complexity. Wide-Area Measurement Systems (WAMS) are valuable tools for mitigating faults and reducing fault-clearing time while simultaneously prioritizing cybersecurity. This review looks at smart grid WAMS implementation and its potential for cyber-physical power system (CPPS) development and compares it to traditional Supervisory Control and Data Acquisition (SCADA) infrastructure. While traditionally used in smart grids, SCADA has become insufficient in handling modern grid dynamics. WAMS differ through utilizing phasor measurement units (PMUs) to provide real-time monitoring and enhance situational awareness. This review explores PMU deployment models and their integration into existing grid infrastructure for CPPS and smart grid development. The review discusses PMU configurations that enable precise measurements across the grid for quicker, more accurate decisions. This study highlights models of PMU and WAMS deployment for conventional grids to convert them into smart grids in terms of the Smart Grid Architecture Model (SGAM). Examples from developing nations illustrate cybersecurity benefits in cyber-physical frameworks and improvements in grid stability and efficiency. Further incorporating machine learning, multi-level optimization, and predictive analytics can enhance WAMS capabilities by enabling advanced fault prediction, automated response, and multilayer cybersecurity.

With the rapid development of the internet, the increasing amount of malicious traffic poses a significant challenge to the network security of critical infrastructures, including power monitoring systems. As the core part of the power grid operation, the network security of power monitoring systems directly affects the stability of the power system and the safety of electricity supply. Nowadays, network attacks are complex and diverse, and traditional rule-based detection methods are no longer adequate. With the advancement of machine learning technologies, researchers have introduced them into the field of traffic detection to address this issue. Current malicious traffic detection methods mostly rely on single machine learning models, which face problems such as poor generalization, low detection accuracy, and instability. To solve these issues, this paper proposes a malicious traffic detection method based on multi-model fusion, using the stacking strategy to integrate models. Compared to single models, stacking enhances the model’s generalization and stability, improving detection accuracy. Experimental results show that the accuracy of the stacking model on the NSL-KDD test set is 96.5%, with an F1 score of 96.6% and a false-positive rate of 1.8%, demonstrating a significant improvement over single models and validating the advantages of multi-model fusion in malicious traffic detection.

Foundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest-change detection and artificial intelligence are discussed, and directions for research are suggested with particular emphasis on considerations of the future smart grid.

In the quest for more secure power grids, this paper delves into the vital role of power monitoring systems and the burgeoning field of safety risk prediction. Traditional prediction methodologies falter due to slow computation and lackluster accuracy. Enter the XGBoost algorithm, hailed for its stellar performance in various prediction scenarios, yet still ripe for improvement within complex power system data. By marrying Operational Technology (OT) with Information Technology (IT), we elevate the predictive prowess of the XGBoost model. Our investigation, grounded in the analysis of 900 sample datasets, unveils a model with enhanced precision in security risk evaluation. This refined model not only surpasses traditional XGBoost in accuracy—with increased instances of near-perfect predictions—but also excels in vital statistical measures: reducing Mean Absolute Percentage Error (MAPE), lowering Root Mean Square Error (RMSE), and boosting both prediction stability and sensitivity. The introduction of the WOA-XGBoost algorithm marks a significant leap forward in fortifying power monitoring systems’ security and predictive alertness.