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Abstract Newer generation sources and loads are posing new challenges to the conventional power system protection schemes. Adaptive and intelligent protection methodology, based on advanced measurement techniques and intelligent fault diagnosis such as machine learning (ML), is found to be useful to meet these challenges. A large number of research works are reported on ML-based power system fault diagnosis. However, ML techniques are evolving at a very fast pace, and an inclusive, as well as state-of-the-art review on ML-based power system fault diagnosis, is not available in the literature. Given this need and growing trend towards ML, the study presented in this paper aims to provide a comprehensive review of ML-based power system fault diagnosis. At first, efforts have been made to enlist the issues present in conventional fault diagnosis which led to the popularity of ML techniques. Also, a baseline framework and workflow for ML-based fault diagnosis are presented. Next, various unsupervised and supervised learning techniques have been discussed separately which have been used by several researchers for fault diagnosis. The discussion throughout is supported with tabulated facts for fault detection, classification and localization works with techniques used, different simulation tools used, and their application system. The advantages and disadvantages of all the techniques of fault diagnosis have also been discussed which will help the readers in the selection of techniques for their research. A brief review of reinforcement learning and transfer learning is also given as they are gaining popularity in power system-related studies and have the potential to be used for fault diagnosis. Finally, the research trends, some key issues, and directions for future research have been highlighted.

Abstract Newer generation sources and loads are posing new challenges to the conventional power system protection schemes. Adaptive and intelligent protection methodology, based on advanced measurement techniques and intelligent fault diagnosis such as machine learning (ML), is found to be useful to meet these challenges. A large number of research works are reported on ML-based power system fault diagnosis. However, ML techniques are evolving at a very fast pace, and an inclusive, as well as state-of-the-art review on ML-based power system fault diagnosis, is not available in the literature. Given this need and growing trend towards ML, the study presented in this paper aims to provide a comprehensive review of ML-based power system fault diagnosis. At first, efforts have been made to enlist the issues present in conventional fault diagnosis which led to the popularity of ML techniques. Also, a baseline framework and workflow for ML-based fault diagnosis are presented. Next, various unsupervised and supervised learning techniques have been discussed separately which have been used by several researchers for fault diagnosis. The discussion throughout is supported with tabulated facts for fault detection, classification and localization works with techniques used, different simulation tools used, and their application system. The advantages and disadvantages of all the techniques of fault diagnosis have also been discussed which will help the readers in the selection of techniques for their research. A brief review of reinforcement learning and transfer learning is also given as they are gaining popularity in power system-related studies and have the potential to be used for fault diagnosis. Finally, the research trends, some key issues, and directions for future research have been highlighted.

Abstract The main hindrances to the large-scale development of renewable-energy projects are the lack of bankability and the inability to align investments and investors with suitable financial instruments or robust policy measures. To illustrate a bankable project, this paper presents a research-based case study on the installation of solar photovoltaic panels on the rooftops of 195 trains of the Indian Railways. Detailed information on the annual running hours, exposure to sunlight, efficiency of solar photovoltaic generation and electrical power demands of each rail coach is considered to conduct a quantitative measure of the tentative amount of fossil fuel savings. The purpose is to provide insight into the types of renewable-energy projects that can be highly attractive to financial institutions and promoters due to their lucrative internal return on investment. As seen in this case study, there are annual savings in diesel of 12 323 088 litres and a CO2 reduction of 32 755 tonnes, with return on investment of 1.3 years. Furthermore, this study conducts a comprehensive analysis of the limitations of existing renewable-energy project financing mechanisms in India. Subsequently, three policy measures are recommended to develop a robust financial mechanism that can effectively meet the needs of investors and investors. These measures include increasing equity injection through a buy-and-hold strategy, providing direct tax benefits to promoters and financing through real-estate investment trusts. The findings are highly relevant to address the challenges associated with bridging the financial gap between access to finance and capital investment in the renewable-energy sector, especially for Asian countries.

Abstract The main hindrances to the large-scale development of renewable-energy projects are the lack of bankability and the inability to align investments and investors with suitable financial instruments or robust policy measures. To illustrate a bankable project, this paper presents a research-based case study on the installation of solar photovoltaic panels on the rooftops of 195 trains of the Indian Railways. Detailed information on the annual running hours, exposure to sunlight, efficiency of solar photovoltaic generation and electrical power demands of each rail coach is considered to conduct a quantitative measure of the tentative amount of fossil fuel savings. The purpose is to provide insight into the types of renewable-energy projects that can be highly attractive to financial institutions and promoters due to their lucrative internal return on investment. As seen in this case study, there are annual savings in diesel of 12 323 088 litres and a CO2 reduction of 32 755 tonnes, with return on investment of 1.3 years. Furthermore, this study conducts a comprehensive analysis of the limitations of existing renewable-energy project financing mechanisms in India. Subsequently, three policy measures are recommended to develop a robust financial mechanism that can effectively meet the needs of investors and investors. These measures include increasing equity injection through a buy-and-hold strategy, providing direct tax benefits to promoters and financing through real-estate investment trusts. The findings are highly relevant to address the challenges associated with bridging the financial gap between access to finance and capital investment in the renewable-energy sector, especially for Asian countries.

The present authors reply to the comment by Popov et al. (IEEE Trans. Power Del., vol.22, no.2, p.1261, April 2007) on the original paper by Ragavan and Satish (IEEE Trans. Power Del., vol.20, no.2, pt.1, p.780-8, April 2005)

The present authors reply to the comment by Popov et al. (IEEE Trans. Power Del., vol.22, no.2, p.1261, April 2007) on the original paper by Ragavan and Satish (IEEE Trans. Power Del., vol.20, no.2, pt.1, p.780-8, April 2005)

Microalgae are promising sources of valuable compounds: carotenoids, polyunsaturated fatty acids, lipids, etc. To overcome the feasibility challenge due to low yield and attain commercial potential, researchers merge technologies to enhance algal bioprocess. In this context, nanomaterials are attractive for enhancing microalgal bioprocessing, from cultivation to downstream extraction. Nanomaterials enhance biomass and product yields (mainly lipid and carotenoids) through improved nutrient uptake and stress tolerance during cultivation. They also provide mechanistic insights from recent studies. They also revolutionize harvesting via nano-induced sedimentation, flocculation, and flotation. Downstream processing benefits from nanomaterials, improving extraction and purification. Special attention is given to cost-effective extraction, showcasing nanomaterial integration, and providing a comparative account. The review also profiles nanomaterial types, including metallic nanoparticles, magnetic nanomaterials, carbon-based nanomaterials, silica nanoparticles, polymers, and functionalized nanomaterials. Challenges and future trends are discussed, emphasizing nanomaterials' role in advancing sustainable and efficient microalgal bioprocessing, unlocking their potential for bio-based industries.

Microalgae are promising sources of valuable compounds: carotenoids, polyunsaturated fatty acids, lipids, etc. To overcome the feasibility challenge due to low yield and attain commercial potential, researchers merge technologies to enhance algal bioprocess. In this context, nanomaterials are attractive for enhancing microalgal bioprocessing, from cultivation to downstream extraction. Nanomaterials enhance biomass and product yields (mainly lipid and carotenoids) through improved nutrient uptake and stress tolerance during cultivation. They also provide mechanistic insights from recent studies. They also revolutionize harvesting via nano-induced sedimentation, flocculation, and flotation. Downstream processing benefits from nanomaterials, improving extraction and purification. Special attention is given to cost-effective extraction, showcasing nanomaterial integration, and providing a comparative account. The review also profiles nanomaterial types, including metallic nanoparticles, magnetic nanomaterials, carbon-based nanomaterials, silica nanoparticles, polymers, and functionalized nanomaterials. Challenges and future trends are discussed, emphasizing nanomaterials' role in advancing sustainable and efficient microalgal bioprocessing, unlocking their potential for bio-based industries.

FAERE Working Paper, 2019.23 ; While environmental values are spreading among societies, theyhardly lead to effective political actions. This may be due to an overestimation of the sharing of those values among people, or to a lack ofpolitical power of environmentalists vis-à-vis materialist citizens. Wepropose a theoretical model to investigate these two channels, based ona setup a la Grossman and Helpman (1994), in which lobby is a strategy available to social groups, in order to influence the government onenvironmental taxes. Because societies have being historically markedby materialist habits, citizens sharing those habits face lower costswhen getting organized. By considering endogenous lobby formation ala Mitra (1999), we show that, in order for environmental and materialist lobbies to coexist, the society must be mixed enough. Based on adynamic framework a la Besley and Persson (2019), we investigate howsocial values change over time. Whenever lobbying by materialists prevails, a unique social equilibrium exists, featuring a stable hegemonyby materialist values. If environmentalists get organized too, a secondsocial equilibrium emerges, that is locally stable and more favorableto them. However, the threshold might be very high, above which thecultural transition effectively takes off. By calibrating the model, westudy counter-acting forces allowing to improve the odds of the environmental transition, such as cultural mutations, social-signaling, andlowering organizational costs. Finally, we provide policy implications. ; Alors que les valeurs environnementales se répandent dans les sociétés, elles ne débouchent guère sur des actions politiques efficaces. Cela peut être dû à une surestimation du partage de ces valeurs entre les gens, ou à un manque de pouvoir politique des écologistes vis-à-vis des citoyens matérialistes. Nous proposons un modèle théorique pour investiguer ces deux canaux, basé sur un cadre à la Grossman et Helpman (1994), dans lequel le lobbying est une stratégie à la disposition des ...