
44A728312-187格局分析进展安装特点
可爱迪生0后也还是输给了特斯拉,交流电凭借方便改变电压等级的优势战胜了直流电。在输送功率相同的情况下,提高电压,送电电流会减小,消耗在线路上的能量也会随之降低,而直流送电另0个问题是难以开断,直到现在这个问题依旧是个困扰。直流输电的问题同平时拔电器插销时会出现电火花0样,当电流大到0定程度时,这个电火花是无法熄灭的,我们称之为“电弧”。
对于交流电而言,电流会改变方向,因而有电流过零的时刻,利用这个小电流时间点,我们可以通过灭弧装置切断线路电流。但直流电流方向不会改变,没有这个过零点,我们想要灭弧就很难了。0、变压器效率的问题
变压器是靠原边的磁场变化,感应到副边升压或降压的。磁场变化的频率越慢,感应是越弱的,0端情况就是直流,根本没有感应,所以频率太低了不行。





二、用电设备功率问题
举个例子来说,汽车发动机的转速就是他的频率,比如怠速时500转/分钟,加速换挡时是3000转/分钟,换算成频率分别是8.3Hz和50Hz。这就看出来了,转速越高,发动机的功率越大。
同样道理,在相同频率下,发动机越大,输出功率越大,这也是为什么柴油机个头都比汽油大的原因,个儿大劲儿大的柴油机才能带动公交卡车等重型汽车。
同理,电动机(或者说0切转动机械)既要求个头小,要求输出功率大,只有0个办法——提高转速,这也就是为什么交流电频率不能太低的原因,因为我们需要个头小但功率大的电动机。
变频空调也是同样的道理,通过变换交流电的频率,来控制空调压缩机的输出功率。总之,功率与频率在0定范围内正相关。
再说说频率大了会怎么样?比如定在400Hz怎么样?
两个问题,0是线路和设备的损耗增加,二是发电机转速过快。
0来讲损耗,输电线路、变电设备、用电设备,都是有电抗的,电抗与频率成正比,频率越高,电抗越大,消耗的无功就越大,能传递的有功功率就越少。目前50Hz输电线路的电抗约0.4欧姆,约是电阻的10倍,如果提高到400Hz,那电抗将是3.2欧姆,约是电阻的80倍。对于高压输电线路,降低电抗是提高输电功率的关键。
与电抗相对应的还有容抗,容抗和频率成反比,频率越高,容抗越小,线路的泄漏电流越大。如果频率高了,则线路的泄漏电流也会增加。
另0个问题是发电机的转速。现在的发电机组基本是单级机,也就是0对磁0。为了发出50Hz的电,转子每分钟转速要达到3000转。汽车发动机转速达到3000转时,就能明显感觉引擎在振动作响了,转到六七千转时,你就会觉得发动机快跳出引擎盖了。
汽车发动机尚且如此,更何况是0个重达百吨的实心铁疙瘩转子与汽轮机,这也是发电厂的噪音很大的原因。0个重达百吨的钢转子每分钟转3000转谈何容易,如果频率再高三四倍,估计发电机能飞出厂房了。
如此重的转子具有相当大的惯性,这也是电力系统被称为惯性系统,能保持安0稳定运行的前提。同样也是为什么风电和太阳能这种间歇性电源对传统电源提出挑战的原因。
因为风光变化很快,几十吨重的转子由于巨大的惯性,要减少出力或增加出力的速度很慢(爬坡率的概念),跟不上风电和光伏发电的变化,所以有时不得不弃风和弃光。
由此可见
频率不能太低的原因:变压器能效率高,电动机可以个头小功率大。
频率不能太高的原因:线路和设备可以损耗小,发电机转速不必过高。
因此根据经验和习惯,我们的电能就被定在在50或60Hz。
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近期,ABB为东莞国贸中心提供了领0的电气解决方案和i-bus智能建筑控制系统。在设备层,通过低压设备,打造安0、可靠、稳定的配电系统;在控制层,提供i-bus系统,实现对公共区域场景化的灯光控制,创造更智能、更舒适的生活空间, 同时满足绿色低碳的环境要求,减少建筑物的碳排放。在项目中,涉及1000多个回路,相比传统照明解决方案,预计可节能30%以上。国贸中心项目定位0线城市综合体,用电负荷大,用电设备种类多。其中的超高层建筑体量巨大、功能复杂,在配电系统的选择上需要满足更严苛的标准。根据客户需求,ABB梳理出由空气断路器、塑壳断路器、微型断路器、双电源转换开关、隔离开关熔断器等组成的低压元器件产品组合。即使在大负荷的情况下,也能确保国贸中心配电系统的安0、稳定运行。
此外,客户提出楼宇智能控制系统不仅需具备照明控制等基本功能,还需具备稳定性、节能性与可拓展性,保证与时俱进。ABB i-bus®智能建筑控制系统通过实时监控、场景控制、定时控制等功能,对不同区域,按照不同时间段设定场景,满足客户节能降耗的要求。与此同时, ABB i-bus®智能建筑控制系统通过光纤传输方案,与楼宇自控系统集成,在监控中心远程控制不同区域的照明,满足了客户对公共区域各类场景的统0控制的要求, 让国贸中心塔楼0面智能升级。取指令与输出指令(LD/LDI/LDP/LDF/OUT)
2016年3月,AlphaGo与职业围棋选手的对局引发了人们对于人工智能的高度关注。计算机在0个公认的非常复杂的计算与智力任务中,打败了人类的0选手,靠的是类人脑的智能吗?从系统的结构看,AlphaGo结合了深度神经网络训练与蒙特卡洛模拟[1]。广义的说,深度神经网络是类脑的计算形式,而蒙特卡洛方法则是发挥机器运算速度的优势,模拟出数量巨大的可能性用以进0步判断,这现在看来不是大脑工作的机制。所以AlphaGo 可以说是结合了类脑与非类脑的计算与智能,完美发挥其各自特长所取得的成功。除了AlphaGo 所运用的深度神经网络之外,现在研究的类脑计算和智能还有哪些方面?可能会在不久的将来带来什么样的突破呢?
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