Open circuit fault diagnosis for a five-level neutral point clamped inverter in a grid-connected photovoltaic system with hybrid energy storage system

Introduction. Recently, the number of high and medium voltage applications has increased dramatically. The connection between these different applications requires series-parallel combinations of power semiconductors. Multilevel converter topologies provide major advantages to these applications. In this paper, a grid-connected photovoltaic system with a hybrid energy storage system using a five-level neutral point clamped inverter is studied. Although the multilevel inverter has many advantages over the two-level inverter, it has a high probability of experiencing an open circuit fault. In this context, the five-level inverter has 24 controllable switches, one of which may experience an open circuit fault at any time. Therefore, it plays an important part in the reliability and robustness of the whole system. The novelty of this paper presents an approach to accurately detect the open circuit fault in all insulated gate bipolar transistors of a five-level neutral point clamped inverter in a photovoltaic power generation application with a hybrid energy storage system. Purpose. Before using fault-tolerant control to ensure service continuity, fault diagnosis techniques must first be used, which are the crucial phase of reliability. Methods. A detection method based on the maximum and minimum error values is proposed. These errors are calculated using the expected and measured line-to-line pole voltages. Results. The open circuit fault detection method is implemented using MATLAB/Simulink. Simulation results showed the accuracy of detecting the open circuit fault in all insulated gate bipolar transistors in a short time. Moreover, this method is adaptable to several applications and is also robust to transient regimes imposed by solar irradiation and load variations.Вступ. Останнім часом різко зросла кількість застосувань високої та середньої напруги. З’єднання між цими різними використаннями вимагає послідовно-паралельних комбінацій силових напівпровідників. Топології багаторівневих перетворювачів надають цим додаткам великі переваги. У цій статті вивчається фотоелектрична система, підключена до мережі, з гібридною системою зберігання енергії, яка використовує п’ятирівневий інвертор із фіксуванням нейтральної точки. Хоча багаторівневий інвертор має багато переваг, порівняно з дворівневим інвертором, він має високу ймовірність виникнення обриву кола. У зв’язку з цим п’ятирівневий інвертор має 24 керовані перемикачі, один з яких будь-якої миті може зіткнутися з обривом кола. Таким чином, він відіграє важливу роль у надійності та стійкості всієї системи. Новизна цієї статті являє собою підхід до точного виявлення несправності розімкнутого кола у всіх біполярних транзисторах із ізольованим затвором п’ятирівневого інвертора з фіксуванням нейтральної точки у фотоелектричних додатках для вироблення електроенергії з гібридною системою зберігання енергії. Мета. Перш ніж використовувати відмовостійкий контроль для забезпечення безперервності обслуговування, необхідно спочатку використовувати методи діагностики несправностей, які є вирішальним етапом надійності. Методи. Запропоновано метод виявлення, заснований на максимальному та мінімальному значеннях помилок. Ці помилки розраховуються з використанням очікуваних та виміряних міжфазних напруг на полюсах. Результати. Метод виявлення обриву кола реалізовано з використанням MATLAB/Simulink. Результати моделювання показали точність виявлення обриву кола у всіх біполярних транзисторах із ізольованим затвором за короткий час. Більш того, цей метод адаптується до кількох застосувань, а також стійкий до перехідних режимів, викликаних сонячним випромінюванням та змінами навантаження

Atomic Force Microscope: Modeling, Simulations, and Experiments

The quality of atomic force microscope (AFM) data strongly depends on scan and controller parameters. Data artifacts can result from poor dynamic response of the instrument. In order to achieve reliable data, dynamic interactions between AFM components need to be well understood and controlled. In this paper we present a summary of our work in this direction. It includes models for the probe-sample interaction, scanner lateral and longitudinal dynamics, scanner creep, and cantilever dynamics. The models were used to study the effect of scan parameters on the system dynamics. Simulation results for both frequency response and imaging were presented. Experimental results were given supporting the simulations and demonstrating the competence of the models. The results within will be used to develop algorithms that allow automated choice of key system parameters, guaranteeing reliable and artifact-free data for any given operating condition (sample, cantilever, environment). Consequently, expanding the AFM capabilities and permitting its use in a wider range of applications.Singapore-MIT Alliance (SMA

Impacts of Industrial Baseline Errors on Costs and Social Welfare in the Demand Side Management of Day-Ahead Wholesale Markets

Demand Side Management (DSM) has been recognized for its potential to counteract the intermittent nature of renewable energy, increase system efficiency, and reduce system costs. While the popular approach among academia adopts a social welfare maximization formulation, the industrial practice in the United States electricity market compensates customers according to their load reduction from a predefined electricity consumption baseline that would have occurred without DSM. This paper is an extension of a previous paper studying the differences between the industrial & academic approach to dispatching demands. In the previous paper, the comparison of the two models showed that while the social welfare model uses a stochastic net load composed of two terms, the industrial DSM model uses a stochastic net load composed of three terms including the additional baseline term. That work showed that the academic and industrial optimization method have the same dispatch result in the absence of baseline errors given the proper reconciliation of their respective cost functions. DSM participants, however, and very much unfortunately, are likely to manipulate the baseline in order to receive greater financial compensation. This paper now seeks to study the impacts of erroneous industrial baselines in a day-ahead wholesale market context. Using the same system configuration and mathematical formalism, the industrial model is compared to the social welfare model. The erroneous baseline is shown to result in a different and more importantly costlier dispatch. It is also likely to require more control activity in subsequent layers of enterprise control. Thus an erroneous baseline is likely to increase system costs and overestimate the potential for social welfare improvements

An Enterprise Control Assessment Method for Variable Energy Resource-Induced Power System Imbalances--Part II: Parametric Sensitivity Analysis

In recent years, renewable energy has developed to address energy security and climate change drivers. However, as energy resources, they possess a variable and uncertain nature that significantly complicates grid balancing operations. As a result, an extensive academic and industrial literature has developed to determine how much such variable energy resources (VERs) may be integrated and how to best mitigate their impacts. While certainly insightful with the context of their application, many integration studies have methodological limitations because they are case specific, address a single control function of the power grid balancing operations, and are often not validated by simulation. The prequel to this paper presented a holistic method for the assessment of power grid imbalances induced by VERs based upon the concept of enterprise control. This paper now systematically studies these power grid imbalances in terms of five independent variables: 1) day-ahead market time step; 2) real-time market time step; 3) VER normalized variability; 4) normalized day-ahead VER forecast error; and 5) normalized short-term VER forecast error. The systematic study elucidates the impacts of these variables and provides significant insights as to how planners should address these independent variables in the future

An Enterprise Control Assessment Method for Variable Energy Resource-Induced Power System Imbalances--Part I: Methodology

In recent years, an extensive academic and industrial literature has been developed to determine how much such variable energy resources (VERs) may be integrated and how to best mitigate their impacts. While certainly insightful within the context of their application, many integration studies have methodological limitations in that they are case specific, address a single control function of power grid balancing operations, and are often not validated by simulation. This paper presents a holistic method for the assessment of power grid imbalances induced by VERs based upon the concept of enterprise control. It consists within a single package a three-layer enterprise control simulator which includes most of the balancing operation functionality found in traditional power systems. The control layers include a resource scheduling layer composed of a security-constrained unit commitment, a balancing layer composed of a security-constrained economic dispatch, and a regulation layer. The proposed method is validated by a set of numerical simulations. The sequel to this paper submitted to the same issue provides a set of extensive results that demonstrate how power grid balancing operations systematically address VER integration

IMAGE: a multivariate multi-site stochastic weather generator for European weather and climate

Capturing the spatial and temporal correlation of multiple variables in a weather generator is challenging. A new massively multi-site, multivariate daily stochastic weather generator called IMAGE is presented here. It models temperature and precipitation variables as latent Gaussian variables with temporal behaviour governed by an auto-regressive model whose residuals and parameters are correlated through resampling of principle component time series of empirical orthogonal function modes. A case study using European climate data demonstrates the model’s ability to reproduce extreme events of temperature and precipitation. The ability to capture the spatial and temporal extent of extremes using a modified Climate Extremes Index is demonstrated. Importantly, the model generates events covering not observed temporal and spatial scales giving new insights for risk management purposes