Selective harmonic elimination pulse-width modulation (SHE-PWM) method has been widely used in various industrial multilevel converters befitting from low switching frequencies and precise elimination of low-order harmonics. Most of existing studies focus on accelerating calculation of switching angles or optimizing predictive neural networks. However, these offline methods cannot dynamically deal with the errors introduced by nonuniform dc sources of multilevel converters or biased placements of switching angles. This paper proposes an online adaptive SHE (OA-SHE) algorithm to compensate these errors. Errors caused by nonideal dc (NID) sources and leading-lagging placements (LLPs) are analyzed in detail. The error of output waveform is comprehensive result of NID sources and LLPs. By introducing feedback, the output waveform accuracy of multilevel converters can be increased greatly. Realization of the particle swarm optimization (PSO) with an embedded control system is presented to acquire good real-time performance. With the OA-SHE method, the value of particles has been higher than the initial value. High order harmonics, including 1st, 5th, 7th, 11th, and 13th components, are suppressed by the low-pass filter. Comparing to existing studies, this online algorithm does not need detailed model parameters and can be directly applied to practical applications achieving multi-objective optimization. Finally, both simulations and experiments are carried out to verify the effectiveness of this algorithm.
Most system restoration strategies following blackouts are obtained by offline methods, which are efficient in theory. But this may not be the case. A new method is proposed to decide the generator startup sequence during the system restoration. The Monte Carlo tree search algorithm is used to search for the generator to be recovered according to the real-time situation of the power system. All paths from the restored system to the chosen generator are obtained by Depth-First Search. And the K-shortest paths are obtained by Yen's Algorithm to decide the best restoration path. Then, key constraints are verified. Especially, the constraint on the hot start of generators is considered. Finally, the feasible scheme with the maximum power generation is selected for implementation. In addition, the cranking process of G33 to G32 in the IEEE39-bus test system is taken as an example to illustrate the efficiency of this method. Depth-First Search detects 42 restoration paths, and Yen's Algorithm selects three most reasonable paths from them, which greatly improves the efficiency of online decision making.
In order to prevent pullorum disease and fowl typhoid in breeders, the use of oregano essential oil (OEO) was tested for the prevention and treatment of infections of multidrug-resistant Salmonella pullorum (SP) and Salmonella gallinarum (SG) in commercial Yellow-chicken breeders. In the challenge-protection experiment, commercial Hongguang-Black 1-day-old breeder chicks were randomly divided into four groups, including A (challenged, preventive dose), B (challenged, treatment dose), C (challenged, untreated), and D (unchallenged, untreated). Group A was supplemented with 200 μL/L OEO in the drinking water during the whole trial (1-35 days of age) and group B was supplemented with 400 μL/L OEO during 8-12 days of age, while groups C and D were kept as untreated controls. At 7 days of age, birds of groups A, B, and C were divided into two subgroups with equal number of birds (A1-A2, B1-B2, and C1-C2), and then subgroups A1, B1, and C1 were challenged with SP, while subgroups A2, B2, and C2 were challenged with SG. Clinical symptoms and death were observed and recorded daily. Every week during the experiment, serum antibodies against SP and SG of all the groups were detected by the plate agglutinate test (PAT). At the age of 35 days, all birds were weighed and necropsied, lesions were recorded and the challenging pathogens were isolated. The results showed that the positive rates of SP and SG isolation in groups A1, A2 and B1, B2 were significantly lower (P < 0.05) than those of groups C1 and C2, respectively, while groups A1 and A2 were slightly lower (P > 0.05) than those of groups B1 and B2. The average body weight (BW) of groups A1 and A2 were significantly higher (P < 0.05) than those of groups B1, B2 and C1, C2, respectively, but there was no significant difference (P > 0.05) with that of group D. The r-value between PAT positive and the recovery rates of Salmonella was 0.99, which means they are highly positively correlated. The results of this study demonstrated that the prevention dose (200μL/L) and the treatment dose (400 μL/L) of OEO supplemented in the drinking water could all effectively decrease infections of SP and SG and that the effect of the prevention was greater than that of the treatment and finally that the prevention could also significantly reduce the BW decline of birds challenged with SP and SG.
The current theoretical study of the seismoelectric method is based on two sets of the governing equations, one is the electrokinetic coupling coefficient proposed by pride (1994) which is characterized by the zeta potential. The other is the electrokinetic coupling coefficient proposed by Revil & Linde (2006) which is based on the amount of excess charge in the pore volume. In this study, the Luco-Apsel-Chen generalized reflection and transmission method was used to solve the second set of seismoelectric governing equations and separate their interfacial response signals. The correctness of the algorithm is determined by comparing the consistency of the total interface signal with the superposition of the interface signals of each layer. The properties of the interface signals are investigated and it is found that different interface responses contribute differently to the overall signal and that the amplitude and phase of the interface signals are influenced by frequency and medium parameters.
Perfect dispatch refers to finding the after-the-fact optimal allocation of output power among generating units based on actual loads. This paper proposes a new idea to solve the economic dispatch problem that regards it as a learning problem, not an optimal problem. The adaptive Gate Recurrent Unit (GRU) neural network is applied to construct the perfect dispatch learning model, which makes the perfect dispatch from an offline manual analysis method to an automatic online learning method. The hierarchical clustering analysis is performed to determine historical similar days for forecast day. Then the historical days that have the same load characteristic with the forecast day form the training set for the GRU learning model. The matrix correlation analysis is performed to select critical historical instants for each dispatch instant. According to matrix correlation analysis results, the number of the memory blocks in each GRU learning model is determined. The structure of GRU learning models is adaptive for different dispatch instants. The IEEE 39-bus test system is used to verify the feasibility and accuracy of the proposed perfect dispatch learning model. Study results indicate that the generation dispatch schedule obtained by the proposed GRU learning model is closer to the perfect dispatch schedule than the generation dispatch schedule obtained by solving the non-linear optimization problem.
Hydrogen energy is now a crucial technological option for decarbonizing energy systems. Comprehensive utilization is a typical mode of hydrogen energy deployment, leveraging its excellent conversion capabilities. Hydrogen is often used in combination with electrical and thermal energy. However, current hydrogen utilization modes are relatively singular, resulting in low energy utilization efficiency and high wind curtailment rates. To improve energy utilization efficiency and promote the development of hydrogen energy, we discuss three utilization modes of hydrogen energy, including hydrogen storage, integration into a fuel cell and gas turbine hybrid power generation system, and hydrogen methanation. We propose a hydrogen energy system with multimodal utilization and integrate it into an electrolytic hydrogen–thermal integrated energy system (EHT-IES). A mixed-integer linear programming (MILP) optimization scheduling model for the EHT-IES is developed and solved using the Cplex solver to improve the operational feasibility of the EHT-IES, focusing on minimizing economic costs and reducing wind curtailment rates. Case studies in northwest China verify the effectiveness of the proposed model. By comparing various utilization modes, energy storage methods, and scenarios, this study demonstrated that integrating a hydrogen energy system with multimodal utilization into the EHT-IES offers significant technical benefits. It enhances energy utilization efficiency and promotes the absorption of wind energy, thereby increasing the flexibility of the EHT-IES.
In order to ensure traffic safety operation under limited visibility conditions, this paper provide an innovative solution — Intelligent Guidance System for Foggy Area Traffic Safety Operation. This Intelligent guidance system mainly consists of visibility tester, illuminant guidance facilities, traffic flow detectors, variable message signs, local and superior controller. According to different visibilities and flow conditions, system controller can adjust brightness, color, flashing frequency, etc. of the illuminant guidance facilities to implement guidance strategy. The system can provide roadway outline reinforcement, vehicle active guidance, rear-end collision warning and safety information advice functions. The biggest advantage of the system is the rear-end collision warning function, which dynamically generate red warning belt to indicate the following space, thus, to ensure vehicles to pass foggy road section safely to great extent. Constitution of system, principle of operation, and control strategy of system are elaborated in this paper.
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