Application of Machine Learning Methods for Asset Management on Power Distribution Networks

This study aims to study the different kinds of Machine Learning (ML) models and their working principles for asset management in power networks. Also, it investigates the challenges behind asset management and its maintenance activities. In this review article, Machine Learning (ML) models are analyzed to improve the lifespan of the electrical components based on the maintenance management and assessment planning policies. The articles are categorized according to their purpose: 1) classification, 2) machine learning, and 3) artificial intelligence mechanisms. Moreover, the importance of using ML models for proper decision making based on the asset management plan is illustrated in a detailed manner. In addition to this, a comparative analysis between the ML models is performed, identifying the advantages and disadvantages of these techniques. Then, the challenges and managing operations of the asset management strategies are discussed based on the technical and economic factors. The proper functioning, maintenance and controlling operations of the electric components are key challenging and demanding tasks in the power distribution systems. Typically, asset management plays an essential role in determining the quality and profitability of the elements in the power network. Based on this investigation, the most suitable and optimal machine learning technique can be identified and used for future work. Doi: 10.28991/ESJ-2022-06-04-017 Full Text: PD

Data-driven Protection of Transformers, Phase Angle Regulators, and Transmission Lines in Interconnected Power Systems

This dissertation highlights the growing interest in and adoption of machine learning approaches for fault detection in modern electric power grids. Once a fault has occurred, it must be identified quickly and a variety of preventative steps must be taken to remove or insulate it. As a result, detecting, locating, and classifying faults early and accurately can improve safety and dependability while reducing downtime and hardware damage. Machine learning-based solutions and tools to carry out effective data processing and analysis to aid power system operations and decision-making are becoming preeminent with better system condition awareness and data availability. Power transformers, Phase Shift Transformers or Phase Angle Regulators, and transmission lines are critical components in power systems, and ensuring their safety is a primary issue. Differential relays are commonly employed to protect transformers, whereas distance relays are utilized to protect transmission lines. Magnetizing inrush, overexcitation, and current transformer saturation make transformer protection a challenge. Furthermore, non-standard phase shift, series core saturation, low turn-to-turn, and turn-to-ground fault currents are non-traditional problems associated with Phase Angle Regulators. Faults during symmetrical power swings and unstable power swings may cause mal-operation of distance relays, and unintentional and uncontrolled islanding. The distance relays also mal-operate for transmission lines connected to type-3 wind farms. The conventional protection techniques would no longer be adequate to address the above-mentioned challenges due to their limitations in handling and analyzing the massive amount of data, limited generalizability of conventional models, incapability to model non-linear systems, etc. These limitations of conventional differential and distance protection methods bring forward the motivation of using machine learning techniques in addressing various protection challenges. The power transformers and Phase Angle Regulators are modeled to simulate and analyze the transients accurately. Appropriate time and frequency domain features are selected using different selection algorithms to train the machine learning algorithms. The boosting algorithms outperformed the other classifiers for detection of faults with balanced accuracies of above 99% and computational time of about one and a half cycles. The case studies on transmission lines show that the developed methods distinguish power swings and faults, and determine the correct fault zone. The proposed data-driven protection algorithms can work together with conventional differential and distance relays and offer supervisory control over their operation and thus improve the dependability and security of protection systems

Enhanced flat-topped modulation for MMC control in HVDC transmission systems

Flat-topped modulation is a member of the family of triplen-series injection techniques that have been extensively utilized in PWM inverter systems to increase the DC-link, and hence semiconductor, utilization. We propose the use of an optimized flat-topped modulation scheme for the modular multilevel converter (MMC) control. The optimized flat-topped waveform minimizes the magnitude of the triplen harmonics, particularly compared to the popular space-vector modulation (SVM) technique, while fully utilizing the DC voltage. This has particular advantages if the converter-side of the interfacing transformer is earthed. Under such conditions, the zero-sequence earthing current is affected by the triplen series injected into the sinusoidal modulating functions. Therefore, it is critical to minimize the injected triplen harmonics. The operating principle of the flat-topped scheme is presented and the Fourier coefficients are compared with the SVM technique. Additionally, the influence of the proposed control scheme on MMC performance is evaluated mathematically. Simulation of a point-to-point HVDC link using average model demonstrates the effectiveness of the proposed MMC operational schemes. The third harmonic of the flat-topped modulation is reduced by 33%, which lowers the potential zero-sequence current flowing to earth. Compared to conventional sinusoidal modulation, the submodule capacitance is reduced by 25%. This significantly lowers submodule cost, volume, and weight. Station conduction losses are expected to reduce by 11%, yielding higher efficiency and lowering cooling system capacity. In addition to the improvement under normal operation, the proposed control scheme also reduces the fault current by 13.4%

Impacts of midpoint FACTS controllers on the coordiantion between generator phase backup protection and generator capability limits

The thesis reports the results of comprehensive studies carried out to explore the impact of midpoint FACTS Controllers (STATCOM and SVC) on the generator distance phase backup protection in order to identify important issues that protection engineers need to consider when designing and setting a generator protection system. In addition, practical, feasible and simple solutions to mitigate the adverse impact of midpoint FACTS Controllers on the generator distance phase backup protection are explored. The results of these studies show that midpoint FACTS Controllers have an adverse effect on the generator distance phase backup protection. This adverse effect, which can be in the form of underreach, overreach or a time delay, varies according to the fault type, fault location and generator loading. Moreover, it has been found that the adverse effect of the midpoint FACTS Controllers extends to affect the coordination between the generator distance phase backup protection and the generator steady-state overexcited capability limit. The Support Vector Machines classification technique is proposed as a replacement for the existing generator distance phase backup protection relay in order to alleviate potential problems. It has been demonstrated that this technique is a very promising solution, as it is fast, reliable and has a high performance efficiency. This will result in enhancing the coordination between the generator phase backup protection and the generator steady-state overexcited capability limit in the presence of midpoint FACTS Controllers. The thesis also presents the results of investigations carried out to explore the impact of the generator distance phase backup protection relay on the generator overexcitation thermal capability. The results of these investigations reveal that with the relay settings according to the current standards, the generator is over-protected and the generator distance phase backup protection relay restricts the generator overexcitation thermal capability during system disturbances. This restriction does not allow the supply of the maximum reactive power of the generating unit during such events. The restriction on the generator overexcitation thermal capability caused by the generator distance phase backup protection relay highlights the necessity to revise the relay settings. The proposed solution in this thesis is to reduce the generator distance phase backup protection relay reach in order to provide secure performance during system disturbances

Grid integration of renewable power generation

This thesis considers the use of three-phase voltage and current source inverters as interfacing units for renewable power, specifically photovoltaic (PV) into the ac grid. This thesis presented two modulation strategies that offer the possibility of operating PV inverters in grid and islanding modes, with reduced switching losses. The first modulation strategy is for the voltage source inverter (VSI), and exploits 3rd harmonic injection with selective harmonic elimination (SHE) to improve performance at low and high modulation indices, where the traditional SHE implementation experiences difficulties due to pulse dropping. The simulations and experimentation presented show that the proposed SHE allows grid PV inverters to be operated with less than a 1kHz effective switching frequency per device. This is vital in power generation, especially in medium and high power applications. Pulse dropping is avoided as the proposed modified SHE spreads the switching angles over 90°, in addition increasing the modulation index. The second proposed modulation strategy, called direct regular sampled pulse width modulation (DRSPWM), is for the current source inverter (CSI). It exploits a combination of forced and natural commutation imposed by the co-existence of an insulated gate bipolar transistor in series with a diode in a three phase current source inverter, to determine device dwell times and switching sequence selection. The DRSPWM strategy reduces switching frequency per device in a CSI by suspending each phase for 60°, similar to VSI dead-band, thus low switching losses are expected. Other benefits include simple digital platform implementation and more flexible switching sequence selection and pulse placement than with space vector modulation. The validity of the DRSPWM is confirmed using simulations and experimentation. This thesis also presents a new dc current offset compensation technique used to facilitate islanding or grid operation of inverter based distributed generation, with a reduced number of interfacing transformers. The proposed technique will enable transformerless operation of all inverters within the solar farm, and uses only one power transformer at the point of common coupling. The validity of the presented modulation strategies and dc current offset compensation technique are substantiated using simulations and experimentation.This thesis considers the use of three-phase voltage and current source inverters as interfacing units for renewable power, specifically photovoltaic (PV) into the ac grid. This thesis presented two modulation strategies that offer the possibility of operating PV inverters in grid and islanding modes, with reduced switching losses. The first modulation strategy is for the voltage source inverter (VSI), and exploits 3rd harmonic injection with selective harmonic elimination (SHE) to improve performance at low and high modulation indices, where the traditional SHE implementation experiences difficulties due to pulse dropping. The simulations and experimentation presented show that the proposed SHE allows grid PV inverters to be operated with less than a 1kHz effective switching frequency per device. This is vital in power generation, especially in medium and high power applications. Pulse dropping is avoided as the proposed modified SHE spreads the switching angles over 90°, in addition increasing the modulation index. The second proposed modulation strategy, called direct regular sampled pulse width modulation (DRSPWM), is for the current source inverter (CSI). It exploits a combination of forced and natural commutation imposed by the co-existence of an insulated gate bipolar transistor in series with a diode in a three phase current source inverter, to determine device dwell times and switching sequence selection. The DRSPWM strategy reduces switching frequency per device in a CSI by suspending each phase for 60°, similar to VSI dead-band, thus low switching losses are expected. Other benefits include simple digital platform implementation and more flexible switching sequence selection and pulse placement than with space vector modulation. The validity of the DRSPWM is confirmed using simulations and experimentation. This thesis also presents a new dc current offset compensation technique used to facilitate islanding or grid operation of inverter based distributed generation, with a reduced number of interfacing transformers. The proposed technique will enable transformerless operation of all inverters within the solar farm, and uses only one power transformer at the point of common coupling. The validity of the presented modulation strategies and dc current offset compensation technique are substantiated using simulations and experimentation

Review on Design and Simulation of Electricity Theft Detection and Protection System with their techno-economic Study

Theft of electricity is a major problem faced by both developed as well as developing countries. It affects both the power as well as economic situation. It also at times is the root cause of blackouts. The objective of this paper is to design and simulate a system that can detect, which in turn, will help in protection from electricity theft

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Failure analysis informing intelligent asset management

With increasing demands on the UK’s power grid it has become increasingly important to reform the methods of asset management used to maintain it. The science of Prognostics and Health Management (PHM) presents interesting possibilities by allowing the online diagnosis of faults in a component and the dynamic trending of its remaining useful life (RUL). Before a PHM system can be developed an extensive failure analysis must be conducted on the asset in question to determine the mechanisms of failure and their associated data precursors that precede them. In order to gain experience in the development of prognostic systems we have conducted a study of commercial power relays, using a data capture regime that revealed precursors to relay failure. We were able to determine important failure precursors for both stuck open failures caused by contact erosion and stuck closed failures caused by material transfer and are in a position to develop a more detailed prognostic system from this base. This research when expanded and applied to a system such as the power grid, presents an opportunity for more efficient asset management when compared to maintenance based upon time to replacement or purely on condition