Abstract:There is more wind with less turbulence offshore compared with an onshore case, which drives the development of the offshore wind farm worldwide. Since a huge amount of money is required for constructing an offshore wind farm, many types of research have been done on the optimization of the offshore wind farm with the purpose of either minimizing the cost of energy or maximizing the total energy production. There are several factors that have an impact on the performance of the wind farm, mainly energy production of wind farm which is highly decided by the wind condition of construction area and micro-siting of wind turbines (WTs), as well as initial investment which is influenced by both the placement of WTs and the electrical system design, especially the scheme of cable connection layout. In this paper, a review of the state-of-art researches related to the wind farm layout optimization as well as electrical system design including cable connection scheme optimization is presented. The most significant factors that should be considered in the offshore wind farm optimization work is highlighted after reviewing the latest works, and the future needs have been specified.

Abstract:Power systems are evolving to the networks with proliferated penetration of renewable energy resources to leverage their environmental and economic advantages. However, due to the stochastic nature of renewables, the management of the rapidly increasing uncertainty and variability in power system planning and operation is of crucial significance. This paper represents a comprehensive overview of power system flexibility as an effective way to maintain the power balance at every moment. Definitions of power system flexibility from various aspects are explained to reach the reliable and economic planning and operation of the power system. The effects of the high penetration of variable energy resources on power systems and the evolution of flexibility in response to renewables are studied. A variety of resources during the flexibility evolutionary transition are introduced and discussed. As an influential flexibility solution in current power systems integrated with renewable resources, market design improvement is widely reviewed in this paper, and required modifications in market design mechanisms are investigated pertaining to various time horizons.

Abstract:In this paper, a novel contingency-aware method for N-2 security-constrained transmission expansion planning is proposed. To ensure that the transmission construction plan satisfies the N-2 security criterion, the proposed method takes advantage of the adjustable robust optimization (ARO) framework and upgrades it. We construct a discrete uncertainty set in which component failures are treated as uncertain events that are handled as binary variables. In addition to the failure of existing lines and generators, we explicitly model the failure of candidate lines. The proposed model comprises a master problem that makes the transmission construction decision, and a series of subproblems that can detect not only the worst contingency, but also the potential contingencies. Computational studies on the IEEE RTS 24-bus and IEEE 118-bus test systems are carried out to validate the effectiveness of the proposed method. Compared with the deterministic method and ARO method in the literature, the proposed method has higher computational efficiency.

Abstract:A generation company (GENCO) which has a conventional power plant (CPP) intends to add an energy storage system (ESS) beside the CPP to increase its flexibility and profitability. For this purpose, a new model is proposed for coordinated operation planning of the CPP and ESS in energy and spinning reserve markets in the presence of a bilateral contract. The proposed model is based on the stochastic price based unit commitment (PBUC) and price based storage commitment (PBSC). The uncertainties of the energy and spinning reserve prices and delivery requests in the spinning reserve market are modeled via scenarios based upon historical data. The proposed model maximizes the profitability of the ESS beside the CPP and encourages the GENCO to invest the ESS. ESS technology options to use beside the CPP are determined by economic assessments. Numerical results show that utilization of ESSs improves the technical operation of CPPs, as well as GENCOs’ profitability.

Abstract:In order to solve the problem of “abandoned” wind caused by short circuit faults in a wind farm, a wind farm fault locating method based on redundancy parameter estimation is proposed. Using the characteristics of the traveling wave, transmission equations containing the position of the fault point are constructed. Parameter estimation from statistical theory is used to solve the redundant transmission equations formed by multiple measuring points to locate the faults. In addition, the bad data error detection capability of the parameter estimation is used to determine bad data and remove them. This improves locating accuracy. A length coefficient is introduced to solve the error enlargement problem caused by a transmission line sag. The proposed fault locating method can solve the fault branch misjudgment problem caused by the short circuit faults near the data measuring nodes of the wind farm based on the proposed fault interval criterion. It also avoids the requirements to the traveling wave speed of traditional methods, thus its fault location is more accurate. Its effectiveness is verified through simulations in PSCAD/EMTDC, and the results shows that it can be used in the fault locating of hybrid transmission lines.

Abstract:Intentional controlled islanding (ICI) is a final resort for preventing a cascading failure and catastrophic power system blackouts. This paper proposes a controlled islanding algorithm that uses spectral clustering over multi-layer graphs to find a suitable islanding solution. The multi-criteria objective function used in this controlled islanding algorithm involves the correlation coefficients between bus frequency components and minimum active and reactive power flow disruptions. Similar to the previous studies, the algorithm is applied in two stages. In the first stage, groups of coherent buses are identified with the help of modularity clustering using correlation coefficients between bus frequency components. In the second stage, the ICI solution satisfying bus coherency with minimum active and reactive power flow disruptions is determined by grouping all nodes using spectral clustering on the multi-layer graph. Simulation studies on the IEEE 39-bus test system demonstrate the effectiveness of the method in determining an islanding solution in real time while addressing the generator coherency problem.

Abstract:This paper addresses the coordinated operation of the electricity and natural gas networks considering the line pack flexibility in the natural gas pipelines. The problem is formulated as a mixed integer linear programming problem. The objective is to minimize the operation cost of the electricity and natural gas networks considering the price of the natural gas supply. Benders decomposition is used to solve the formulated problem. The master problem minimizes the startup and shutdown costs as well as the operation cost of the thermal units other than the gas-fired generation units in the electricity network. The first subproblem validates the feasibility of the decisions made in the master problem in the electricity network. And if there is any violation, feasibility Benders cut is generated and added to the master problem. The second subproblem ensures the feasibility of the decisions of the master problem in the natural gas transportation network considering the line pack constraints. The last subproblem ensures the optimality of the natural gas network operation problem considering the demand of the gas-fired generation units and line pack. The nonlinear line pack and flow constraints in the feasibility and optimality subproblems of natural gas transportation network are linearized using Newton-Raphson technique. The presented case study shows the effectiveness of the proposed approach. It is shown that leveraging the stored gas in the natural gas pipelines would further reduce the total operation cost.

Abstract:The problems of recovering the state of power systems and detecting the instances of bad data have been widely studied in literature. Nevertheless, these two operations have been designed and optimized for the most part in isolation. Specifically, state estimators are optimized based on the minimum mean-square error criteria, which is only optimal when the source of distortions in the data is Gaussian random noise. Hence, the state estimators fail to perform optimality when the data is further contaminated by bad data, which cannot necessarily be modeled by additive Gaussian terms. The problem of power state estimation has been studied extensively. But the fundamental performance limits and the attendant decision rules are unknown when the data is potentially compromised by random bad data (due to sensor failures) or structured bad data (due to cyber attacks, which are also referred to false data injection attacks). This paper provides a general framework that formalizes the underlying connection between state estimation and bad data detection routines. We aim to carry out the combined tasks of detecting the presence of random and structured bad data, and form accurate estimations for the state of power grid. This paper characterizes the optimal detectors and estimators. Furthermore, the gains with respect to the existing state estimators and bad data detectors are established through numerical evaluations.

Abstract:The consensus protocol of cyber-physical power systems is proposed based on fractional-order multi-agent systems with communication constraints. It aims to enable each generator to reach a time-varying common rotor angle and rotor speed. Communication constraints including event-triggered sampling and partial information transmission are considered to render the consensus protocol more realistic. The Zeno behavior is excluded during the system sampling process. A sufficient condition is derived to solve the consensus problem. The effectiveness of the proposed consensus protocol is demonstrated by a numerical example.

Abstract:High-voltage direct current (HVDC) transmission is playing an increasingly important role in modern power systems, and the resulted power/voltage stability issue has raised widespread concern. This paper presents an on-line power/voltage stability index (PVSI) for multi-infeed HVDC (MIDC) systems. Different from the existing indices which are developed mainly for off-line and static analysis, the proposed PVSI can be applied in real time. Effects of system changes on stability assessment such as change of system states and control strategies are considered. Thus, helpful guidance can be provided for on-line HVDC stability and controls. The PVSI is originally deduced for single-infeed HVDC systems in an “AC way” by analyzing the power and voltage stability of both pure AC systems and HVDC systems. Moreover, its on-line application in practical MIDC systems is realized by building an equivalent single-infeed model, and utilizing nowadays measurement and communication infrastructures such as wide-area measurement system (WAMS). The effectiveness of the PVSI is verified through simulations in real-time digital simulator (RTDS).

Abstract:The smart grid with flexible topologies receives intensive attention recently. Transmission switching (TS) alters the power system topology during operation, and has been demonstrated for the advantage of economic and secure operation of power systems. TS includes a chain of sequential switching actions which bring disturbances to the system if the switching actions are not properly designed. Unfortunately, it is not considered or well-studied in existing works. In this paper, a new multi-period TS model that considers the transition security and a two-stage iterative method are proposed. In the TS model, we take into account the fact that only one line is permitted to switch up or down at a time and the security of each switching action is considered. The proposed iterative solution makes the TS model more tractable under AC framework. Case studies on a 6-bus system and the IEEE 57-bus test system have varified the effectiveness of the proposed model. Numerical results show that: ① the consideration of transition security of TS is essential; ② the transition path is directly related to secure and fast the transmission switching; ③ the proposed model and solution method give an effective way to determine the switching sequence and switching timing under transition security criteria.

Abstract:The increasing penetration of wind farms in the energy sector directly affects the dynamic behavior of the power system. The increasing use of wind energy in the power system worsens its stability and inherently influences the firmness of a small signal. To investigate these effects, one of the synchronous generators (SGs) of the grid is considered defective and is replaced by a doubly fed induction generator (DFIG)-based wind farm of the same rating. The small-signal stability of a power system is usually evaluated via eigenvalue analysis where local-area and inter-area oscillatory modes for the New England test system are identified. SG controls, such as automatic voltage regulator (AVR) and power system stabilizer (PSS), are added to attenuate the generated disturbances. In this study, the impact of wind energy on the small-signal stability of the power system is investigated. Different combinations of AVR and PSS types are considered to mitigate the undesirable alterations. A comparative study is performed based on numerical simulations to choose the best combination of AVR and PSS types. The obtained results prove that the proposed combination yields good results in terms of stability enhancement both under normal operating conditions and in DFIG-based wind farms.

Abstract:Hybrid high-voltage direct current (HVDC) transmission systems employ a new type of HVDC transmission topology that combines the advantages of the line-commutated converter system and the voltage-source converter system. They can improve the efficiency and reliability of long-distance power transmission. However, realizing alternating-current (AC) grid-fault ride through on the inverter side of a hybrid HVDC transmission system is a challenge considering that a voltage-source converter based HVDC (VSC-HVDC) is used on the inverter side. In this study, a control strategy for an overvoltage fixed trigger angle based on the power-balance method is developed by fully utilizing the operation characteristics of a hybrid HVDC transmission system. The strategy reduces the inverter-side overvoltage of the HVDC system under a fault in the inverter-side AC system. Simulations based on Gezhou Dam are conducted to validate the effectiveness of the proposed strategy.

Abstract:This paper presents a security constrained unit commitment (SCUC) suitable for power systems with a large share of wind energy. The deterministic spinning reserve requirement is supplemented by an adjustable fraction of the expected shortfall from the supply of wind electric generators (WEGs), computed using the stochastic feature of wind and loosely represented in the security constraint with scenarios. The optimization tool commits and dispatches generating units while simultaneously determining the geographical procurement of the required spinning reserve as well as load-following ramping reserve, by mixed integer quadratic programming (MIQP). Case studies are used to investigate various effects of grid integration on reducing the overall operation costs associated with more wind power in the system.

Abstract:Doubly-fed induction generator (DFIG)-based wind farm has the characteristic of transient fault with low voltage ride through (LVRT) capability. A new three-phase fault direction identification method for the outgoing transmission line of the wind farm is presented. The ability of the new directional relay to differentiate between a three-phase fault in one direction or the other is obtained by using the increment of phase angle difference between the memory voltage signal and the fault current signal within a certain time, and using the amplitude variation of the fault current. It can be inferred that the fault current is supplied by the wind farm whether the phase angle differs or the current amplitude varies considerably. Different fault locations at the outgoing transmission line have been simulated by PSCAD/EMTDC to evaluate the reliability and sensitivity of the proposed technique. Results show that the new directional relay is of faster response when a three-phase fault occurs at the outgoing transmission line of a DFIG-based wind farm.

Abstract:Wind power can be an efficient way to alleviate energy shortage and environmental pollution, and to realize sustainable development in terms of energy generation. The sustainability assessment of a wind project among its alternatives is a complex task that cannot be solely simplified to environmental or economic feasibility. It requires the consideration of its technological and social aspects as well as other circumstances. This paper proposes a new method for selecting the most sustainable wind projects. The method is based on multi-criteria decision-making techniques. The analytic hierarchy process and entropy weight method are combined to determine the weights of evaluation indexes, and an innovative index-weight optimization method based on the Lagrange conditional extremum algorithm. The fuzzy technique for order preference by similarity to the ideal solution is applied to rank wind project alternatives considering functionality and proportionality of the system. Moreover, the sensitive analysis is applied to verify the robustness of the proposed method. The applicability of the method is demonstrated on a case study from China, where three main wind projects are analytically compared and ranked. The results indicated that the sustainable level of calculated wind power can provide a reference point for the planning and operation of the wind project. The results show that the proposed method is of both theoretical significance and practical application in engineering.

Abstract:Power system security against attacks is drawing increasing attention in recent years. Battery energy storage systems (BESSs) are effective in providing emergency support. Although the benefits of BESSs have been extensively studied earlier to improve the system economics, their role in enhancing the system robustness in overcoming attacks has not been adequately investigated. This paper addresses the gap by proposing a new battery storage sizing algorithm for microgrids to limit load shedding when the energy sources are attacked. Four participants are considered in a framework involving interactions between a robustness-oriented economic dispatch model and a bilevel attacker-defender model. The proposed method is tested with the data from a microgrid system in Kasabonika Lake of Canada. Comprehensive case studies are carried out to demonstrate the effectiveness and merits of the proposed approach.

Abstract:Nowadays, utilities aim to find methods for improving the reliability of distribution systems and satisfying the customers by providing the continuity of power supply. Different methodologies exist for utilities to improve the reliability of network. In this paper, demand response (DR) programs and smart charging/discharging of plug-in electric vehicles (PEVs) are investigated for improving the reliability of radial distribution systems adopting particle swarm optimization (PSO) algorithm. Such analysis is accomplished due to the positive effects of both DR and PEVs for dealing with emerging challenges of the world such as fossil fuel reserves reduction, urban air pollution and greenhouse gas emissions. Additionally, the prioritization of DR and PEVs is presented for improving the reliability and analyzing the characteristics of distribution networks. The reliability analysis is performed in terms of loss of load expectation (LOLE) and expected energy not served (EENS) indexes, where the characteristics contain load profile, load peak, voltage profile and energy loss. Numerical simulations are accomplished to assess the effectiveness and practicality of the proposed scheme.

Abstract:Probabilistic load forecasting (PLF) is able to present the uncertainty information of the future loads. It is the basis of stochastic power system planning and operation. Recent works on PLF mainly focus on how to develop and combine forecasting models, while the feature selection issue has not been thoroughly investigated for PLF. This paper fills the gap by proposing a feature selection method for PLF via sparse L1 -norm penalized quantile regression. It can be viewed as an extension from point forecasting-based feature selection to probabilistic forecasting-based feature selection. Since both the number of training samples and the number of features to be selected are very large, the feature selection process is casted as a large-scale convex optimization problem. The alternating direction method of multipliers is applied to solve the problem in an efficient manner. We conduct case studies on the open datasets of ten areas. Numerical results show that the proposed feature selection method can improve the performance of the probabilistic forecasting and outperforms traditional least absolute shrinkage and selection operator method.

Abstract:Remote and regional areas are usually supplied by isolated and self-sufficient electricity systems, which are called as microgrids (MGs). To reduce the overall cost of electricity production, MGs rely on non-dispatchable renewable sources. Emergencies such as overloading or excessive generation by renewable sources can result in a substantial voltage or frequency deviation in MGs. This paper presents a supervisory controller for such emergencies. The key idea is to remedy the emergencies by optimal internal or external support. A multi-level controller with soft, intermedial and hard actions is proposed. The soft actions include the adjustment of the droop parameters of the sources and the controlling of the charge/discharge of energy storages. The intermedial action is exchanging power with neighboring MGs, which is highly probable in large remote areas. As the last remedying resort, curtailing loads or renewable sources are assumed as hard actions. The proposed controller employs an optimization technique consisting of certain objectives such as reducing power loss in the tie-lines amongst MGs and the dependency of an MG to other MGs, as well as enhancing the contribution of renewable sources in electricity generation. Minimization of the fuel consumption and emissions of conventional generators, along with frequency and voltage deviation, is the other desired objectives. The performance of the proposal is evaluated by several numerical analyses in MATLAB?.

Abstract:Taking the consumption rate of renewable energy and the operation cost of hybrid AC/DC microgrid as the optimization objectives, the adjustment of load demand curves is carried out considering the demand side response (DSR) on the load side. The complementary utilization of renewable energy between AC area and DC area is achieved to meet the load demand on the source side. In the network side, the hybrid AC/DC microgrids purchase electricity from the power grid at the time-of-use (TOU) price and sell the surplus power of renewable energy to the power grid for profits. The improved memetic algorithm (IMA) is introduced and applied to solve the established mathematical model. The promotion effect of the proposed source-network-load coordination strategies on the optimal operation of hybrid AC/DC microgrid is verified.

Abstract:A new hybrid adaptive autoregressive moving average (ARMA) and functional link neural network (FLNN) trained by adaptive cubature Kalman filter (ACKF) is presented in this paper for forecasting day-ahead mixed short-term demand and electricity prices in smart grids. The hybrid forecasting framework is intended to capture the dynamic interaction between the electricity consumers and the forecasted prices resulting in the shift of demand curve in electricity market. The proposed model comprises a linear ARMA-FLNN obtained by using a nonlinear expansion of the weighted inputs. The nonlinear functional block helps introduce nonlinearity by expanding the input space to higher dimensional space through basis functions. To train the ARMA-FLNN, an ACKF is used to obtain faster convergence and higher forecasting accuracy. The proposed method is tested on several electricity markets, and the performance metrics such as the mean average percentage error (MAPE), and error variance are compared with other forecasting methods, indicating the improved accuracy of the approach and its suitability for a real-time forecasting.

Abstract:Due to the capacity of thermal storage, electric water heater (EWH) is one of the best candidates for demand response programs. However, few attentions are given to the modeling and optimization of EWHs with thermostatically-controlled automatic water mixer (TCAWM). In this paper, differential thermodynamic model is established for EWHs with TCAWM and a piecewise linear approximation method is performed for the nonlinear thermodynamic model. The multi-objective optimization model is established by introducing an index reflecting the comfort degree of users, so that the optimal energy usage of the EWH can be obtained by mixed integer linear programming. Testing examples verify the effectiveness of the proposed method.

Abstract:In this paper, a novel framework for the estimation of optimal investment strategies for combined wind-thermal companies is proposed. The medium-term restructured power market was simulated by considering the stochastic and rational uncertainties, the wind uncertainty was evaluated based on a data mining technique, and the electricity demand and fuel price were simulated using the Monte Carlo method. The Cournot game concept was used to determine the Nash equilibrium for each state and stage of the stochastic dynamic programming (DP). Furthermore, the long-term stochastic uncertainties were modeled based on the Markov chain process. The long-term optimal investment strategies were then solved for combined wind-thermal investors based on the semi-definite programming (SDP) technique. Finally, the proposed framework was implemented in the hypothetical restructured power market using the IEEE reliability test system (RTS). The conducted case study confirmed that this framework provides robust decisions and precise information about the restructured power market for combined wind-thermal investors.

Abstract:Considering a demand response (DR) based social welfare maximization model, a complementarity problem based on the Karush-Kuhn-Tuker condition is described, which is a non-dual method for determining real-time price for smart grids. The Lagrange multiplier in the dual method, which is used to determine the basic electricity price, is applied in the model. The proposed method computes the optimal electricity consumption, price and production. According to the electricity price, users can arrange their electricity equipment reasonably to reduce the consumption pressure at peak time. The model aims to encourage users to actively participate in the DR and realize peak cutting and valley filling. In addition, the model considers different utility functions representing three types of users. Finally, a Jacobian smoothing version of Newton method is used to solve the model. Statistical simulations of the model validate the rationality and feasibility of the proposed method.

Abstract:Liberalized electricity markets, smart grids and high penetration of renewable energy sources (RESs) led to the development of novel markets, whose objective is the harmonization between production and demand, usually noted as real time of flexibility markets. This necessitates the development of novel pricing schemes able to allow energy service providers (ESPs) to maximize their aggregated profits from the traditional markets (trading between wholesale/day-ahead and retail markets) and the innovative flexibility markets. In the same time, ESPs have to offer their end users (consumers) competitive (low cost) energy services. In this context, novel pricing schemes must act, among others, as automated demand side management (DSM) techniques that are able to trigger the desired behavioral changes according to the flexibility market prices in energy consumption curves (ECCs) of the consumers. Energy pricing schemes proposed so far, e.g. real-time pricing, interact in an efficient way with wholesale market. But they do not provide strong enough financial incentives to consumers to modify their energy consumption habits towards energy cost curtailment. Thus, they do not interact efficiently with flexibility markets. Therefore, we develop a flexibility real-time pricing (FRTP) scheme, which offers a dynamically adjustable level of financial incentives to participating users by fairly rewarding the ones that make desirable behavioral changes in their ECCs. Performance evaluation results demonstrate that the proposed FRTP is able to offer a 15%–30% more attractive trade-off between the stacked profits of ESPs, i.e. the sum of the profits from retail and flexibility markets, and the satisfaction of the consumers.

Abstract:Uncertainties in wind power forecast, day-ahead and imbalance prices for the next day possess a great deal of risk for the profit of generation companies participating in a day-ahead electricity market. Generation companies are exposed to imbalance penalties in the balancing market for unordered mismatches between associated day-ahead power schedule and real-time generation. Coordination of wind and thermal power plants alleviates the risks raised from wind uncertainties. This paper proposes a novel optimal coordination strategy by balancing wind power forecast deviations with thermal units in the Turkish day-ahead electricity market. The main focus of this study is to provide an optimal trade-off between the expected profit and the risk under wind uncertainty through conditional value at risk (CVaR) methodology. Coordination problem is formulated as a two-stage mixed-integer stochastic programming problem, where scenario-based wind power approach is used to handle the stochasticity of the wind power. Dynamic programming approach is utilized to attain the commitment status of thermal units. Profitability of the coordination with different day-ahead bidding strategies and trade-off between expected profit and CVaR are examined with comparative scenario studies.

Abstract:Boosted by a strong solar power market, the electricity grid is exposed to risk under an increasing share of fluctuant solar power. To increase the stability of the electricity grid, an accurate solar power forecast is needed to evaluate such fluctuations. In terms of forecast, solar irradiance is the key factor of solar power generation, which is affected by atmospheric conditions, including surface meteorological variables and column integrated variables. These variables involve multiple numerical time-series and images. However, few studies have focused on the processing method of multiple data types in an inter-hour direct normal irradiance (DNI) forecast. In this study, a framework for predicting the DNI for a 10-min time horizon was developed, which included the nondimensionalization of multiple data types and time-series, development of a forecast model, and transformation of the outputs. Several atmospheric variables were considered in the forecast framework, including the historical DNI, wind speed and direction, relative humidity time-series, and ground-based cloud images. Experiments were conducted to evaluate the performance of the forecast framework. The experimental results demonstrate that the proposed method performs well with a normalized mean bias error of 0.41% and a normalized root mean square error (nRMSE) of 20.53%, and outperforms the persistent model with an improvement of 34% in the nRMSE.

Abstract:Power quality assessment is an important performance measurement in smart grids. Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters. Addressing this issue, in this study, we propose segregation of the power disturbance from regular values using one-class support vector machine (OCSVM). To precisely detect the power disturbances of a voltage wave, some practical wavelet filters are applied. Considering the unlimited types of waveform abnormalities, OCSVM is picked as a semi-supervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data. This model is able to automatically detect the existence of any types of disturbances in real time, even unknown types which are not available in the training time. In the case of existence, the disturbances are further classified into different types such as sag, swell, transients and unbalanced. Being light weighted and fast, the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring. The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.

Abstract:Power electronic conversion plays an important role in flexible AC or DC transmission and distribution systems, integration of renewable energy resources, and energy storage systems to enhance efficiency, controllability, stability, and reliability of the grid. The efficiency and reliability of power electronic conversion are critical to power system applications. One way to enhance the efficiency and reliability of power electronic conversion is soft-switching technology. This paper introduces a generic zero-voltage-switching (ZVS) technique based on silicon carbide (SiC) power device. Using the proposed ZVS technique, all semiconductor switching devices in a power converter can realize ZVS operations. Next, the applications of the ZVS technique in different power electronic conversion systems such as photovoltaic inverters, wind power systems, energy storage systems and flexible AC transmission system devices are discussed. Finally, as an example, the operation performance and efficiency improvement of a SiC metal-oxide-semiconductor field-effect transistor (MOSFET) ZVS back-to-back converter are discussed.

Abstract:The modular multilevel converter (MMC) is now the most attractive topology for medium and high voltage power conversion applications with several advantages over the traditional voltage source converter (VSC). However, due to a large number of sub-modules (SMs) in the MMC, system reliability is a big challenge in its practical application, where each SM may be considered as a potential point of failure. In this paper, a reliability evaluation based on the Markov model is proposed for the MMC. The failure rates of the power electronic devices and SMs are firstly analyzed. Then, the Markov model and the state transition equation of the system are built in detail. A general reliability evaluation function is established, in which the mean time to failure and reliability evaluation of the MMC with redundant SMs are also discussed. Finally, a practical direct current (DC) distribution example for reliability evaluation is analyzed, and the results verify that the reliability evaluation based on the Markov model could provide a useful reference for project design.