Distributed model predictive control (DMPC) has become a focus in the energy management of shipboard power systems due to its capabilities for privacy preservation, robustness, and distributing computing burdens to local processors. DMPC determines control actions in a distributed manner based on the predictions of system statuses. However, the performance of DMPC is affected by inaccurate predictions resulting from uncertain parameters in nominal prediction models. Particularly, these inaccuracies in predicting propulsion loads and solar panel generation powers can lead to power imbalances when implementing the control actions determined by DMPC. To address this challenge, this paper proposed a novel reinforcement learning compensated DMPC (RL-C-DMPC) to distributively compensate for the control actions determined by DMPC baseline control, thereby rectifying the power imbalances caused by uncertain parameters in nominal prediction models. A value-decomposition-network-based training and distributed testing mechanism is designed for our proposed RL-C-DMPC. Furthermore, a method for range selection of compensation rate is specifically proposed for the energy management of shipboard power systems. To validate the effectiveness of our proposed RL-C-DMPC, we conduct a comprehensive case study utilizing real-life voyage data and historical solar power generation data in the area of the voyage to build the environment for training and testing. By comparing power imbalances between DMPC and RL-C-DMPC, our results indicate significant reductions in power imbalances so that frequency stability can be better ensured. Furthermore, via the case study, we also evaluate the communication robustness of RL-C-DMPC.@en

Predictive maintenance scheduling of modular-multilevel-converters (MMCs) requires estimations of the failure probabilities of MMCs during a period of time in the future. Particularly, the predicted future failure probabilities are influenced by two main factors, the mission profiles of the MMCs and the maintenance decisions on the MMCs during the prediction period. This paper proposes a failure probability prediction model (FPM) for MMCs by considering these two factors. First, the expectations of the failure probabilities of the components for all the scenarios of mission profiles are obtained. Second, in predictive maintenance scheduling problems, the decisions to perform the maintenance actions are represented by binary variables. When the number of submodules is very large, using the binomial probability form currently used in reliability engineering to express the 'r-out-of-n' failure probability of arms of the MMCs is intractable. Thus, this paper proposes a tractable form (T-form) in FPM by observing that the submodules on one arm are homogeneous. Furthermore, an approximation method, i.e., clustering and assignment (C&A), is proposed to reduce the computation times for calculating the parameters needed by the proposed T-form. Then, we perform a case study that assesses the accuracy and computation time of the C&A approach. The results show that the accuracy of the C&A approach is high and that the computation time is reduced significantly compared with the accurate method. We also show that the computation time for solving the predictive maintenance scheduling problem can be reduced hugely by using the T-form instead of the binomial probability form.

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To ensure the reliability of power systems, the independent system operator (ISO) manages the planning process of the maintenance of generation units for generation companies (GENCOs). This paper focuses on a widely studied two-layer long-term predictive maintenance decision making framework in a deregulated environment. In the first layer the ISO-wide maintenance schedule is optimized for the GENCOs, targeting minimal total maintenance cost and degradation statuses. In the second layer, a bidding mechanism is designed for GENCOs who are not satisfied with the time slots scheduled by the first layer, so that they can bid for their preferred time slots. A novel bidding mechanism for the host ISO (i.e., the ISO that manages the maintenance planning process) is proposed, called interchangeable bidding mechanism for maintenance (IBMM). In this mechanism, the GENCOs of the host ISO can use their bid price to purchase the supportive energy from the GENCOs of the neighbor ISOs. Furthermore, they also can pay a penalty fee for reducing the amount of energy transmitted from the host ISO to the neighbor ISO with respect to what has been stipulated in the long-term inter-ISO power exchange contract. IBMM provides more opportunities for GENCOs of the host ISO to obtain their preferred maintenance time slots. Additionally, the power system reliability can be ensured. IBMM is formulated as a mixed-integer non-linear bidding programming problem. Then, the bidding programming problem is recast into a mixed-integer second-order cone programming (MISOCP) problem that can be solved using Gurobi. In the case study, the IEEE 118-bus network is studied to illustrate the performance of the proposed bidding strategy.

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In typical power system operations, failures, e.g., caused by degradation, may result in service interruption and loss of power supply. Furthermore, low-occurrence-probability but high-impact extreme events, e.g., natural disasters and cyber-attacks, may also damage the power grid. This thesis aims to develop innovative strategies for handling these two kinds of failures in power grids. In particular, we develop preventive maintenance strategies, a pre-disaster electrical vehicle charging control strategy, an accurate fault location algorithm, and an unmanned aerial vehicles routing strategy for post-disaster distribution networks....@en

Natural disasters pose a tremendous risk to the reliability of distribution networks. In this paper, a novel real-time UAV routing strategy for coordination between monitoring and inspection for post-disaster restoration in distribution networks is proposed. With our proposed real-time UAV routing strategy, damages can be inspected by UAVs for post-disaster restoration. Besides, transmission lines can be monitored to find potential dangers, and road infrastructure can also be monitored to provide real-time information about traffic conditions so that repair crews can select the best ways to reach damages. In addition, due to unpredictable events during restoration, the UAV routing strategy and schedule need to be updated in real time. Then, to tackle the multi-time-scale characteristic of the proposed UAV routing strategy, a two-layer computation architecture is proposed. A case study based on the distribution network in Zaltbommel, the Netherlands, illustrates the effectiveness of the proposed method compared to other approaches.

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Maintenance of generation units is a measure to ensure the reliability of power systems. In this paper, a novel blockchain-based truthful condition-based maintenance of generation units (T-CBMGU) platform is proposed to innovate and upgrade state-of-the-art CBMGU. In addition, two valid inequalities are proposed to accelerate the convergence speed of Benders decomposition in maintenance scheduling process. The proposed valid inequalities are formulated based on technical/physical analysis and greedy-based heuristic initialization. More specifically, for data acquisition and failure rate diagnosis/prognosis processes, T-CBMGU can ensure the immutability of the collected operational data. In this way, the influence of tampered data on the diagnosis/prognosis results in state-of-the-art CBMGU can be reduced. For maintenance scheduling and bidding to change scheduled time slot processes, in state-of-the-art CBMGU, the decision makers, i.e., independent system operators (ISOs), may not be trusted. However, in T-CBMGU, the scheduling and bidding processes are implemented automatically via smart contracts rather than by the ISOs; as such, incentives to manipulate data can be avoided. Finally, regarding performance of maintenance actions, in contrast to state-of-the-art CBMGU, the implementation process can be truthfully recorded by the T-CBMGU platform, which facilitates backtracking of responsibility. Then, the T-CBMGU platform and the valid inequalities are tested for the IEEE 300-bus power system. Furthermore, cases with tampered data and distrust caused by fairness manipulation are simulated to show the importance of using T-CBMGU. Finally, the Benders decomposition algorithm with valid inequalities is compared with other solvers to demonstrate its fast convergence speed.

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Fault location algorithms for transmission lines use the parameters of the transmission line to locate faults after the faults have occurred along the line. Although these parameters can be estimated by the phasor measurement units (PMUs) at the terminal(s) of the transmission line continuously, the uncertainty in the measurements will give rise to stochastic errors in the measured values. Thus, the uncertainty in measurements definitely influences the estimations of the parameters of the transmission line, which, in turn, influences the results of fault location algorithms. Inaccurate results of fault location algorithms may lead to costly maintenance fees and prolonged outage time. Therefore, in this article, we estimate the parameters of the transmission line considering the uncertainty in the measurements so that a more accurate fault location can be derived. The uncertainty in the measurements will be modeled as a stochastic distribution, and the maximum likelihood estimation (MLE) method will be adopted to reduce the uncertainty in the measurements. In addition, as an illustration, the telegrapher's equations will be used to calculate the parameters of the transmission line, and the two-terminal positive sequence network fault location algorithm will be used to locate the fault. In a simulation, a case study of a real-life transmission line the influence of the uncertainty in the measurements on the transmission line parameter estimations and the effectiveness of the MLE method for estimations are simulated and analyzed. The results show that the influence of the uncertainty in the measurements on the positive sequence network fault location algorithm should not be neglected and that the proposed method is very effective in significantly reducing the influence of the uncertainty in the measurements.

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Preventive maintenance is applied in distribution networks to prevent failures by performing maintenance actions on components that are at risk. Distributed generators (DGs) and batteries can be used to support power to nearby loads when they are isolated due to maintenance. In this paper, a novel short-term preventive maintenance method is proposed that explicitly considers the support potential of DGs and batteries as well as uncertainties in the power generated by the DGs. Two major issues are addressed. To deal with the large-scale complexity of the network, a depth-first-search clustering method is used to divide the network into zones. Moreover, a method is proposed to capture the influence of maintenance decisions in the model of the served load from DGs and batteries via generation of topological constraints. Then a stochastic scenario-based mixed-integer non-linear programming problem is formulated to determine the short-term maintenance schedule. We show the effectiveness and efficiency of the proposed approach via a case study based on a modified IEEE-34 bus distribution network, where we also compare a branch-and-bound and a particle swarm optimization solver. The results also show that the supporting potential of DGs and batteries in preventive maintenance scheduling allows a significant reduction of load losses.

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When the current differential protection is applied to very long transmission lines such as half-wavelength transmission lines, it suffers from the distributed capacitive current as well as from low sensitivity in case of a high-resistance fault. In case of an external fault, the variation of the line storage energy, the distributed resistance energy loss, and the energy injected from both ends of the transmission line satisfy the energy balance equation, and in this case the energy balance is independent of the line length. However, in case of an internal fault, the energy dissipated by the fault resistance breaks the energy balance. Based on the theory of energy balances, this paper proposes a novel pilot protection principle. Compared with the existing long-distance transmission line protection principles, the proposed pilot protection principle has the advantage of not being influenced by the distributed parameters of the long line, and in addition it is suitable for identification of high-resistance grounded faults. A simulation based on EMTDC/PSCAD illustrates the benefits of the proposed approach.

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